# Hierarchical Text Classification of Urdu News using Deep Neural Network

Taimoor Ahmed Javed<EMAIL_ADDRESS>0000-0003-0624-2871 National
University of Computer and Emerging Sciences,
IslamabadH-11/4IslamabadIslamabad Capital TerritoryPakistan44000 ,  Waseem
Shahzad  National University of Computer and Emerging Sciences,
IslamabadH-11/4IslamabadPakistan<EMAIL_ADDRESS>and  Umair Arshad
National University of Computer and Emerging Sciences, IslamabadPakistan

(2021)

###### Abstract.

Digital text is increasing day by day on the internet. It is very challenging
to classify a large and heterogeneous collection of data, which require
improved information processing methods to organize text. To classify large
size of corpus, one common approach is to use hierarchical text
classification, which aims to classify textual data in a hierarchical
structure. Several approaches have been proposed to tackle classification of
text but most of the research has been done on English language. This paper
proposes a deep learning model for hierarchical text classification of news in
Urdu language - consisting of 51,325 sentences from 8 online news websites
belonging to the following genres: Sports; Technology; and Entertainment. The
objectives of this paper are twofold: (1) to develop a large human-annotated
dataset of news in Urdu language for hierarchical text classification; and (2)
to classify Urdu news hierarchically using our proposed model based on LSTM
mechanism named as Hierarchical Multi-layer LSTMs (HMLSTM). Our model consists
of two modules: Text Representing Layer, for obtaining text representation in
which we use Word2vec embedding to transform the words to vector and Urdu
Hierarchical LSTM Layer (UHLSTML) an end-to-end fully connected deep LSTMs
network to perform automatic feature learning, we train one LSTM layer for
each level of the class hierarchy. We have performed extensive experiments on
our self created dataset named as Urdu News Dataset for Hierarchical Text
Classification (UNDHTC). The result shows that our proposed method is very
effective for hierarchical text classification and it outperforms baseline
methods significantly and also achieved good results as compare to deep neural
model.

Hierarchical Urdu Text Classification,Natural language processing, Deep Neural
Network, Urdu News Dataset

††copyright: acmcopyright††journalyear: 2021††doi:
10.1145/1122445.1122456††journal: TALLIP††journalvolume: 37††journalnumber:
4††article: 111††publicationmonth: 8††ccs: Computing methodologies Neural
networks††ccs: Information systems Clustering and classification††ccs:
Computing methodologies Natural language processing††ccs: Computing
methodologies Supervised learning

##  1\. Introduction

Nowadays, electronic information extraction systems are omnipresent, from
instant mobile messaging apps to automated archives having millions of data.
Due to large amount of data, it has produce significant amount of challenges.
However one initiative is to categorize some of this text content
automatically, so that users can more easily access, interpret and modify data
for patterns and knowledge generation. Organizing large amount of electronic
data into categories is becoming a topic of interest for many individuals and
companies. The only solution to this problem is text classification. Text
classification is the process to categorize documents according to its
categories. It uses a wide variety of areas of expertise which includes AI,
NLP and machine learning. It uses a supervised learning based approach, in
which we train a model by giving a large amount of data. Recently, several
algorithms have been proposed like SVM, k-Nearest Neighbor, Naive Bayes.
Results shows that these approaches are very effective in conventional text
classification applications. There are many application of text classification
like topic modeling, sentiment analysis, intent detection and spam detection.
Generally text classification tasks have only few classes. When classification
tasks have large number of classes then we will extend text classification
problem because they have some specific challenges which require particular
solution. There are numerous significant issues in classification which
consists of similar categories which are organized into hierarchical
structure. So here hierarchical classification comes into being. In
hierarchical classification, classes are organized into categories and sub
categories or we can say that classes are organized into class hierarchy and
when we apply this to textual data it will become hierarchical text
classification.
In recent years, hierarchical classification seems to be an active topic of
research in the field of databases. Web directories like Yahoo, Wikipedia are
example of hierarchical text repositories. There are different applications
nowadays in which documents are organized as hierarchical structure. If we
take a real world example, Hierarchical text classification is like a
librarian job, who want to place a book in a shelf. Many organizations like IT
companies, law firms and medical companies are taking advantage of automatic
classification of documents. Therefore, it shows that hierarchical
classification have a significant impact on many applications and
organizations.
The Urdu language has a global reach and it is Pakistan’s national language.
Urdu language is also being spoke in six states of India as their official
language. There are more than 12 million speakers in Pakistan and over 300
million speakers all across the globe (Riaz, 2008). Previously, Urdu was
ignored by researchers due to its complex composition, peculiar
characteristics and absence of linguistic capital (Daud et al., 2017). Due to
these features, Urdu text classification is much more complex than other
languages. Moreover, automatic text classification systems which is developed
for other languages cannot be used for Urdu language. Since last 10 years, we
saw a rapid growth in Urdu text documents like journals, web databases and
online news articles. Because of these considerations, it raise the interest
of researchers in the Urdu language.
Mostly research work is done in English language for hierarchical text
classification. Recently, many researches shows that this topic is gaining a
lot of interest nowadays. So research work on hierarchical classification has
been started in many languages like Chinese, Arabic and Indonesian. There is a
huge gap for Urdu language. Research is done on simple text classification but
there is no research work on hierarchical Urdu text classification. However,
many studies has been found on simple Urdu text classification. For instance,
(Ali and Ijaz, 2009) have done research on Urdu text classification by
applying SVM on large urdu corpus. (Khan et al., 2015) have applied different
feature extraction techniques on Urdu language text and Decision Tree
algorithm J-48 is used for classification. Most of the previous studies on
automated text classification is based on supervised learning techniques such
as decision trees, naive Bayes and support vector machines (SVM). Nevertheless
automated classification has become more and more difficult due to a rising
number of corpus sizes and categorizing a document into fields and subfields.
Due to this, these machine learning algorithms tend to have less accuracy. In
fact mostly studies perform performance on machine learning methods. Since,
the proposed approach is based on deep neural network.
Neural networks have recently been shown to be efficient in conducting endways
learning of hierarchical text classification. To fill a gap of hierarchical
text classification for Urdu language and overcome the limitations of the
above machine learning models and to obtain improved results for the Urdu
document classification, we introduce a hierarchical model based on LSTM
mechanism named as Hierarchical Multi-layer LSTMs (HMLSTM). HMLSTM
incorporates deep learning algorithms to facilitate both general and advanced
learning at the document hierarchy level. In this study, we implement a
supervised HMLSTM model on Urdu news to extract categories and sub categories
and their hierarchical relationships. We first apply Text Representing Layer
to achieve text representation in which we use Word2vec embedding to transform
the words to vector and prepare words vocabulary. Then we apply Urdu
Hierarchical LSTM Layer (UHLSTML) on our self created dataset named as Urdu
News Dataset for Hierarchical Text Classification
(UNDHTC)111https://github.com/TaimoorAhmedJaved/Urdu-News-Dataset-for-
Hierarchical-Text-Classification. In our experiments, we select six state of
the art machine learning models and one deep neural model to evaluate the
effectiveness of HMLSTM. The experiments on real world dataset shows the
efficiency and predictive capacity of our model. In particular, our model
exhibits improved accuracy over conventional text classification approaches.
To summarize, this paper includes the following contributions:

  * •

Urdu language has insufficient resources, but it has a complex and complicated
morphological script, which makes automated text processing more difficult. A
big issue for the Urdu language is the lack of availability of large,
standardized, accessible and cost-free text dataset. So in this study, we
develop a large dataset of Urdu news documents and will make this dataset
freely accessible for future study.

  * •

This research is conducted because hierarchical text classification method has
never been applied to classify Urdu news using deep neural network. So, we
have developed the HMLSTM method which can predict the categories of each
level while classifying all categories in the whole hierarchical structure
accurately.

  * •

We conduct a thorough evaluation of our proposed method on Urdu News dataset
(UNDHTC) from different domains. The experiment results show that our method
can outperforms various baselines methods and deep neural model.

The rest of this paper is structured as follows. In the next segment, we will
be discussing related work. In Section 3, we will discuss the background of
hierarchical text classification process. We will address what form of class
structures most widely used, that is either DAG structure or tree structure.
We will also discuss the types of hierarchical text classification and analyse
which type is better than other. In Section 4, we will discuss base line
techniques. In Section 5, we will address our proposed methodology and
dataset. Section 6 presents the experiments and results. Section 7 concludes
the paper and also future work is discussed.

##  2\. Related Work

This section of the paper discusses recent efforts and contribution on
hierarchical text classification and their methods used in similar areas which
have an influence in this field. A variety of methods have been proposed to
enhance the quality of classification results which are discussed below.
In conventional text classification, feature engineering has been used to get
useful quality features which help in improving the model performance. Text
classification is a well-proven way to organize a large scale document set.
Many algorithms have been proposed (Joulin et al., 2017) to do text
classification, such as SVM, kNN, naïve bayes, decision trees. Analyses showed
that in conventional text classification applications, these algorithms are
very efficient. Nowadays, deep learning models are widely use in natural
language processing tasks. Recently, Nabeel et al (Asim et al., 2020) applied
deep learning classification methodologies on Urdu text. For Arabic text
classification (Elnagar et al., 2020) also applied deep neural models.
Similarly, authors applied attention mechanism for Chinese text classification
(Xie et al., 2020). Nonetheless, text classification has become very
challenging these days. It is due to the growth in corpus sizes, categories
and subcategories. With the increasing number of categories and subcategories
of a document, it is important to not just label the document by a specialized
field but also organized in its overall category and subcategory. So we use
hierarchical classification.

Deep neural models have recently gained popularity in the classification of
text by means of their communication capacity and low requirements for
features. But deep neural models based on extensive training data and needs
people to provide lots of labelled data and in many cases when we have to
label data, we have to find domain expert person to do this task which can be
too costly. Another issue in deep neural models is that in the hierarchical
text classifications it cannot determine appropriate levels in the class
hierarchy. There is no automatic way to determine best level in class
hierarchy. So recently Meng et al (Meng et al., 2019) proposed a weakly
supervised neural method for hierarchical text classification which address
above issues. This approach is based on deep neural network which requires
only small amount of labelled data provided by the users. It doesn’t require
large amount of training data. So they pre train local classifier at each node
in hierarchical structure and then ensemble all local classifier into a global
classifier using self-training. The entire process automatically determine the
most appropriate level of each document. They have performed experiments on
New York Times news articles. Corpus consists of 5 categories and 25 sub
categories. The model that have been used is CNN. They have compared their
results with other models and gives satisfactory results.

There are many existing approaches for document classification but few
organize the document into the correct areas. This paper (Kowsari et al.,
2017) proposed a new approach for hierarchical classification of text which is
named as Hierarchical Deep learning for text classification. This paper
employed new methods of deep learning. In neural networks, deep learning is
very effective. Deep learning can manage supervised, unsupervised and semi
supervised learning. In this paper, approaches to the hierarchy of the
classification of documents advance the concept of deep neural learning
network. The authors brought together various approaches to create
hierarchical document classifiers. They have used three neural networks DNN,
CNN and RNN. DNN is used for first level of classification of documents. Lets
suppose if the first model output is medical, in next level it will train only
all medical science documents. So, DNN is trained on first hierarchical level
and in next level, each hierarchy of document is trained with specified
domain. They have collected data of articles from Web of science. Results were
compared with conventional text classification methods like K-NN, decision
tree, Naive Bayes and came to know that RNN outperforms all of them. CNN
performs second best. The results shows that by using deep learning
approaches, we can get good improvement in performance as compared to
traditional methods. The HDLTex approach clearly shows variations of RNN at
the top level and CNN or DNN at the lower level produced consistently good
results.

A topical text classification is basic issue for many applications like
emotion analysis, question answering, auto-tagging customer queries and
categorizing articles. To handle large amount of labels, hierarchical text
classification has been used as an effective way to organize text. Authors
have proposed a new technique named as Hierarchically Regularized Deep graph
Cnn (Peng et al., 2018), which will solve the problem of document level
topical classification and also address issues in topical classification which
are long distance word dependency and nonconsecutive phrases. First documents
are converted into graphs that is centered on the word co-occurrence. Every
document represents as a graph of vectors. For nodes of graph, pretrained
vectors are used as an input. Word2vec is used in this scenario. After that
they have generated sub graph of words and apply graph preprocessing on them.
They have used both RNN and CNN models for text classification. They have
proposed deep graph CNN approach for classification of text. Dependency
between the labels can improve classification results, so that why they have
used recursive regularization. They have used two data sets RCV1 and NY Times
for experiments. Authors compared their experimental results with traditional
hierarchical text classification models. The results shows that proposed
approach have achieved good results than other models. Many applications
arrange documents in the form hierarchical structure where classes are divided
into categories and subcategories. In previous papers flat based methods were
proposed. They only predict the categories of last level and ignore the
hierarchical structure. To solve this problem, some work has been done in
hierarchical category structure. Nevertheless, these literature focuses mainly
on certain local areas or global structure as a whole ignoring dependencies
between different levels of hierarchy.

In short, it’s a challenging task in which records are allocated to different
categories and every category have its dependencies. The challenges are,
connection among texts and hierarchy must be identified when interpreting the
semantics of documents. Next challenge is that the hierarchical category
structure has dependencies among different levels, which is mostly ignored by
past research papers. Another challenge is that when we are assigning document
to hierarchical categories, we should consider overall structure of
hierarchical tree rather than just considering local region. This paper (Huang
et al., 2019) have proposed a new approach named as recurrent neural network
based on hierarchical attention to solve all above challenges. This framework
automatically compiles a document with the most appropriate level of
categories. In this paper, authors have first work on the illustration of
documents and hierarchical framework. They have applied embedding layer. This
layer is used to computes text and the structure of their hierarchy. Word2vec
is used for this task. After that, they have applied Bi LSTM to improve
semantic text representation. After enhancing text representation and their
structure, attention based recurrent layer is applied to represent the
dependency for each category level from top to down. After that hybrid
approach is designed for making predictions of the categories for each level.
The experiments were conducted on academic exercises and repository of patent
papers. Educational dataset is categorized into three levels hierarchy while
repository of patent papers into four levels hierarchy. Experimental results
shows that HARNN approach is effective.

Ivana et al (Irsan and Khodra, 2016) have done hierarchical classification on
Indonesian news articles. This research is conducted to examine whether
hierarchical multi label classification could provide desirable results in
classifying Indonesian news articles. So authors have proposed a hierarchical
multi label classification model for Indonesian news articles. Three kinds of
methods are available, which are global approach, flat approach and top down
approach. To handle multi label classification, authors have used calibrated
label ranking and binary relevance. Dataset is collected from news website and
it is organized into two level hierarchy. The news articles have been split
into 10 categories and 4 subcategories. For classification algorithms that
were used are j48, Naïve bayes and SVM.

In this paper (Al-Anzi and AbuZeina, 2018), the author has present a study on
Hierarchical text classification for Arabic language. Previously no research
work is done on Hierarchical Arabic text classification, however many studies
shows that work is done on flat Arabic text classification. The method used
for Hierarchical Arabic text classification is first order Markov chain.
Transition probability matrix is created for each category when performing
text classification task in Markov chain approach then for every document, a
scoring procedure is carried out for all training set categories. In this
study, data is taken from Alqabas newspaper. The documents are divided into 3
level hierarchy. First they preprocess the data. These kind of activities
helps to get rid of useless data .TF IDF is used for preprocessing. Then they
train the data by creating markov chain matrices. For each Arabic character a
number is assigned. This process is called mapping. Probability matrix is
created for each category. The writers have compared their proposed solution
with LSI. Results demonstrate that top level performance is relatively good
while it keeps on decreasing in below levels.

Deep neural networks are becoming more common task for classifying text
because of their strong expressive capacity and less practical technical
requirement. Although it is so attractive, models of neural text recognition
suffer from the absence of trained data in many applications. In this paper
(Yang et al., 2016), author have proposed a hierarchical attention network for
text classification of document. They have evaluated their model on large
scale data. Datasets that they have used are reviews of imdb, yelp and amazon.
They have compared their model with baselines methods like SVM, LSTM and word
based CNN. Their model develops the document vector by combining relevant
terms to the sentence vector and then the sentence vector to the document
vector. The experimental results shows that their model perform better than
the previous models.

Mao et al. suggested a method for hierarchical text classification. Authors
have formulate hierarchical text classification as Markov decision process
(Mao et al., 2019). In this process, at each phase, the agent examines the
current situation and makes decisions. In this paper, authors suggested a
framework in which system learns label assignment policy to decide when to
place object and where to interrupt the assignment process. Their label
assignment policy place each object to its label in a hierarchical structure.
At start policy place an object on the root label. At every point, the policy
determines which label object will be put next until stop action has been
taken. Hierarchical label assignment policy examines the hierarchical
structure of labels, both through training and analysis, in a precise way,
which eliminates identity prejudice frequently seen in previous local and
global strategies. Hierarchical label assignment policy is more versatile than
other approaches in learning when to stop that support leaf node prediction.
When exploring the labels hierarchy during training, Hierarchical label
assignment policy performs better as compared to flat and local methods.
Authors have conducted experiments on five publically available datasets. Two
datasets are related to news category which are RCV1 and NYT and other two are
related to protein functional catalogue and gene prediction and last dataset
is from Yelp Dataset Challenge. The models that have been used for feature
encoding are TextCNNN proposed by Kim et al. (Kim, 2014), Hierarchical
Attention Network proposed by Yang et al. (Yang et al., 2016) and bow-CNN
proposed by Johnson et al. (Johnson and Zhang, 2015). They have compared their
results with state of art hierarchical text classification methods and neural
methods. The experiments results shows that their method surpassing
traditional hierarchical text classification methods notably.

Recently many approaches have been proposed for chinese text classification.
Chinese text classification is difficult as compared to other language like
English, owing to the features of the Chinese text itself. To improve quality
of chinese text classification, Liu et al. (Liu et al., 2019) proposed a
hierarchical model architecture which can extract rigorous context and
information in a sequence manner from Chinese texts named as hierarchical
comprehensive context modeling network. This approach is a combination of LSTM
and temporal convolutional networks (Bai et al., 2018). LSTM is used for
extracting context and sequence features of document. In this paper authors
have used four datasets for experimentation. The results conducted on CRTEXT
dataset shows that their model achieved the best results as compared to other
datasets. This paper (Gong et al., 2020) proposed a new approach for a large-
scale multi-label text classification. First text is converted into a graph
structure then attention mechanism is used to represent the full functionality
of the text. They have also used convolutional neural networks for feature
extraction. The proposed method shows good results as compared to state-of-
the-art methods. In this paper (Li et al., 2019) authors have proposed a
neural network approach which is based on BiLSTM and Hierarchical Attention
layer. They have performed experiments on different news datasets. They have
compared their model with CNN and BiLSTM attention network and achieved better
results.

##  3\. Hierarchical Text Classification Background

Nowadays, text classification techniques are normally focused on a flat model.
Currently, some methods for hierarchical text classification have been
implemented to resolve the constraints of flat models and their methodology is
focused on the classification method and feature selection. Mostly supervised
learning technique is used which requires a category-based linear
classification. In hierarchical classification, we are provided with different
categories which are organized hierarchically. The corpus consist of training
data in which documents are placed in categories and sub categories. Words or
vocabulary are extracted from this corpus which have high frequency in a
certain category. The vocabulary of every node is filtered and words are
weighed according to their defined category. After this, test documents are
evaluated and categories are ranked according to their term weighting. Term
weights are assigned to each level node of hierarchy. These classifications
allocate documents to categories on the basis of ranking. For categorizing,
feature selection is used to determine categories of document. The words which
are useless or have low frequency are removed to increase accuracy. There are
different approaches that used to remove low frequency words.

Figure 1. Example of Hierarchical Classification.

For example in “Fig. 1”, news is divided into three category i.e sports,
entertainment and technology. On level 1, we can easily differentiate between
all categories, if or not the document refers to sports, entertainment or
technology. If text corresponds to sports, then we will further categories
into sub categories, either the sports is cricket, football or hockey. In a
hierarchy, each level represents the category of a document. A classifier is
trained for each node in a hierarchical tree. When the new document arrives in
a classification process, it is assigned to a suitable root node category then
it use good category classifier to determine which direction to proceed in a
hierarchy. And it will proceed until a document is assigned to a leaf node.
There are two approaches for describing a hierarchical structure, which are
tree structure and Direct Acyclic Graph also known as DAG. The difference
between these two structures is that, in tree structure each node should have
one parent while in Direct Acyclic Graph structure single node may have one or
more parent. Fig. 2 shows the illustration of both structure.

Figure 2. Illustration of hierarchical structure’s types

According to Freitas et al. (2007) and Sun et al. (2001), there are different
types of hierarchical classification (Sun and Lim, 2001), which are discussed
below.

###  3.1. Flat Classification

Koller and Sahami (1997) proposed this approach(Koller and Sahami, 1997). It
is the most simple and straightforward method to handle hierarchical
classification and it totally neglects class hierarchy. In flat classification
you don’t worry about parent categories, only just classify document to its
final leaf node. This method works as a conventional algorithm for
classification. Nonetheless, the problem of hierarchical classification is
solved implicitly. If a leaf class is allocated to instance, almost all of its
descendant classes are also allocated to it indirectly. The advantage of this
method is its simplicity. It is an easy way to execute with an out of-the-box
classifier. This approach also have some disadvantages. The main disadvantage
is that this approach doesn’t explore complete information of parent child
class relationship which can lose important information and could decrease the
efficiency. “Fig. 3” shows the illustration of flat classification approach.
The dashed rectangle shows multi class classifier.

Figure 3. Flat classification approach.

###  3.2. Global (big bang) approach

This approach is simple, relatively complicated model, in which the whole
class hierarchy takes into account in a single run. That means it classify
data in one run. Fig. 4 shows the illustration of one global classifier for
the entire class hierarchy. When used in the test process, the induced model
is identified by each test example, which can allocate classes to check
example at any hierarchical level. This approach can go in different
direction. Many use clustering approach, some recreate the problem as a multi-
label, and others have altered the latest algorithms. The advantage of this
approach is that this approach have smaller model as compare to other models
and the class relationship is determined during model development and it
provide fast predictions. The drawback of this approach is that due to the
high computations, there is higher chance of misclassify unseen data. However,
due to the high complexity of this approach, it is rarely used.
Labrou et al. illustrate this approach. In their work (Labrou and Finin,
1999), the program classifies website as subcategory of Yahoo! hierarchical
groups. This approach is unique to applications for text mining. A threshold
is used for the final classification. There are some other method of global
classifier which is based on multi label classification. Kiritchenko et al.
have done work on multi label classification(Kiritchenko et al., 2006). To
determine a hierarchical class, the learning process is amended to include all
hierarchical classes by increasing the number of nodes having no child with
information of predecessor classes.

Figure 4. Global approach: one global classifier for the entire class
hierarchy.

###  3.3. Hierarchically-Structured Local Classifiers

The third approach is called as local approach which is also known as top down
approach. This method uses predefined data classification scheme to construct
a classification hierarchy. It uses hierarchy to develop classifiers utilizing
local information. Benefits of this strategy are straightforward, effective
and can handle multi-label hierarchical classification and it maintains
natural information of data hierarchy. One major drawback of this approach is
if there is classification mistake in parent node, it will be propagated
downwards the hierarchy. It can greatly affect the result of classification.
There are three methods to implement local classification approach named as
local classifier per parent node, local classifier per node and local
classifier per level.

####  3.3.1. Local classifier per parent node

This strategy is also called top down approach. In this approach, we train one
multi class classifier for each parent node to differentiate between its child
nodes. Now lets take an example of class tree as shown in Figure. 5 and
approach that is used is class prediction of hierarchy. Lets assume document
is assigned to class 2 node by initial classifier. Then in level 2 we have
second classifier which is class 5 node, is learned with the children of class
2 node. Then class 5 node will make its class assignment. This process will
continue until classifiers are available. In our example, we have one
classifier on the first level and one classifier on second level. To stop
incoherence with the prediction of different level, then you can develop a
framework in which a document classified by first classifier as node 2, can
only be seen by the node 2 classifier at the second level.

Figure 5. Illustration of local classifier per parent node (each dashed
rectangle represents a multi-class classifier).

####  3.3.2. Local classifier per node

This popular is most popular which have been used in many researches. This
method trains one binary classifier for each node of hierarchical class except
root node. Figure. 6 shows that illustration of local classifier per node.
There are a few issues with this strategy, first one is the hierarchical
structure of local training sets is not taken into account. Secondly, it is
restricted to issues where partial depth labeling examples involves. The
benefit of this method is that it is by default multi label that means it can
predict several labels per class hierarchy.

Figure 6.  Illustration of Local classifier per node approach (each dashed
rectangle represents a binary classifier).

####  3.3.3. Local classifier per level

In this method we train one multi class classifier for each level of the class
hierarchy. This classification method has not been widely used in researches.
Figure. 7 shows that illustration of local classifier per level approach. Lets
take this example, we have three classifier that will be trained to predict
classes. The training data are built in the same manner as the parent node
method is applied in the local classifier. The disadvantage of this approach
is the inconsistency problem it presents.

Figure 7. Local classifier per level (each dashed rectangle represents a
multiclass classifier).

Local classifier approaches are very efficient. It uses hierarchical data
information while preserving generality and smoothness. Nevertheless, the
categorization method that have chosen, you could have a heavy final model in
the end. In local classifier approach, there is also an issue of error
propagation, which means if an error occur on first level, it will affect all
the following ones down the hierarchy.

##  4\. Proposed Methodology and Dataset

In this section, we will discuss our proposed methodology and self created
dataset for hierarchical text classification of urdu news. As shown in figure
8, our approach Hierarchical Multi-Layer LSTMs (HMLSTM) contains two parts
that is Text Representing Layer (TRL) and Urdu Hierarchical LSTM Layer
(UHLSTML). Mainly, we use the Text Representing Layer to achieve a unified
representation of each urdu news text and the hierarchical category structure.
After this, we apply our deep neural model Urdu Hierarchical LSTM Layer
(UHLSTML) to predict hierarchical categories and sub categories of urdu news
text.

Figure 8. Hierarchical Multi-Layer LSTMs (HMLSTM)

###  4.1. Text Representing Layer

In first phase of our approach HMLSTM, text representing layer TRL purpose is
to make a unified representation of each urdu news text and their hierarchy
structure. As we know, it is complicated for machine learning algorithms to
use natural language directly. So we use embedding for this, which helps to
convert words into a numeric representation. In TRL, an embedding layer is
used to encode text. We first convert urdu news text U into tokens and take
hierarchical category labels L as an input. After this, we apply word2vec
(Mikolov et al., 2013) embedding, word2vec requires list of tokens of a
document then start vector encoding of tokens. The fundamental principle of
Word2vec is that rather than representing words as one-hot encoding i.e tf-idf
vectorizer in high dimensional space, we define words in dense low dimensional
space in such a way that identical words get identical word vectors, such that
they are projected to adjacent points. It learns to represent words with
similar meanings using similar vectors. Word2vec accepts text corpus as an
input and converts text into numerical data which will later pass it to our
model as an input. We can formulize urdu news text U as a sequence of N words
$U=\left(v_{1},v_{2},\ldots,v_{N}\right)$ where $u_{i}\in\operatorname{R}_{h}$
is initialized by h-dimensional of the feature vectors with Word2vec .

As our approach is supervised learning we have labeled data in the form of
categories and sub categories. Our proposed model UHLSTML take hierarchical
category structure labels L as an input. We can formulize labels L as a
sequence of 12 classes. $L=\left(l_{1},l_{2},\ldots,l_{12}\right)$. So similar
to word2vec, we have to convert categorical data into numerical data. There
are several methods to transform categorical values to numerical values. Every
method has certain effects on the feature set. Here we are using label
encoding for categories and sub categories labels. As we have 12 classes, 3
categories and 9 sub categories. Label encoding convert these labels in
machine readable form. So, Label L is an input vector which has integers
representing various classes in the data. As already discussed, UHLSTML is
deep neural model, it works well if we have classes distributed in a binary
matrix. So, we convert label class vectors (ordinal numbers) to binary matrix
so that our labels are easily understandable by UHLSTML. For this, we have
used tensorflow keras (Chollet et al., 2015) to-categorical which converts a
class vector (integers) to a matrix of binary values (either ‘1’ or ‘0’).
After this our text representation layer obtained whole representation of data
and achieved a unified representation of each urdu news text and the
hierarchical category structured labels, we pass it towards our propose model
UHLSTML.

###  4.2. Urdu Hierarchical LSTM Layer

Urdu Hierarchical LSTM layer is based on Hierarchically-Structured Local
Classifiers approach in which we have Local classifier per level method. In
this method we train one LSTM layer for each level of the class hierarchy as
shown in figure 9.

Figure 9. LSTM layer per level (each dashed rectangle represents a LSTM
layer).

After receiving combined and structured representation of urdu news text and
labels, we propose UHLSTML, an end-to-end fully connected deep LSTMs network
to perform automatic feature learning. It is based on recurrent neural
networks architecture. Figure 10 shows the architecture of the proposed
network, which has two LSTM layers, one LSTM layer for top categories and
second LSTM layer for sub categories and a softmax layer that gives the
predictions. First layer will also be used as feedforward layer, it learns the
features of top level category that is sports, technology and entertainment
and forward all these features to second LSTM layer.

The input layer contains text features. We pass input which is urdu news text
U to first LSTM layer which learns features of top category and gives output
of y. First LSTM layer is used for first level of classification. After this,
we pass these features as an input to second LSTM layer. The second level
classification in the hierarchy consists of a trained top category. In second
LSTM layer, each second level hierarchy is connected to the output of the
first level. Let’s take an example in this 10 case, if the output of our first
LSTM is labeled as Sports then the second LSTM is trained only with all sports
related news like football, hockey and cricket. Which means first level LSTM
is trained with all news whereas second level LSTM will only trained with the
news of specific category. The LSTMs in this research are trained with a
standardized back propagation algorithm that uses ReLU as an activation
function and a softmax layer that gives the output. Optimizer that we used is
Adam having learning rate 0.001. To calculate loss, categorical crossentropy
is used. LSTM dropout is applied to the last LSTM layer to reduce over fitting
and allow extra efficient learning.

Figure 10. The proposed Urdu Hierarchical LSTM Layer having two LSTM layers
and one dropout layer.

The LSTM network consists of various memory blocks which are called cells.
LSTM module composed of 3 gates named as Forget gate, Input gate and Output
gate. In Forget Gate, it determine which details should be removed from the
cell in that specific time duration. Information which is no longer needed for
the LSTM to comprehend items or information that is of less value is
eliminated. This is handled by sigmoid function. Forget gate takes in two
inputs, previous state ht-1 and input at that particular time x-t and outputs
a vector, with values ranging from 0 to 1. This vector value is multiplied to
the cell state.

(1) $f_{t}=\sigma\left(W_{f}\cdot\left[h_{t-1},x_{t}\right]+b_{f}\right)$

In input gate, we add some new information to the cell state. Sigmoid function
decides, which value is added to the cell state. The values which are passed
tanh function gives weightage to them which results values from -1 to +1.

(2) $\displaystyle i_{t}$
$\displaystyle=\sigma\left(W_{i}\cdot\left[h_{t-1},x_{t}\right]+b_{i}\right)$
$\displaystyle\tilde{C}{t}$
$\displaystyle=\tanh\left(W{C}\cdot\left[h_{t-1},x_{t}\right]+b_{C}\right)$

In Output Gate, the role is to pick valuable information from the current cell
and makes it to the output through the output gate. It is handled by Sigmoid
function, which value is added to the cell state and tanh function assigns
weights to the values and multiplying the value of this to the output of
Sigmoid. Here is an equation.

(3)
$\begin{array}[]{l}o_{t}=\sigma\left(W_{o}\left[h_{t-1},x_{t}\right]+b_{o}\right)\\\
h_{t}=o_{t}*\tanh\left(C_{t}\right)\end{array}$

###  4.3. Proposed Dataset

In supervised learning, the performance of the model mainly relies on dataset.
The learning of a neural network relies on the dataset and if there is less
amount of dataset, the learning will be insufficient and it will decrease the
accuracy of model. There are alot of publicly available dataset like
Reuters-21578, 20 Newsgroups and RCV1 for english news but there is limited
amount of large dataset for urdu news. So large datasets are required to
perform various natural language tasks and applications. To provide a suitable
dataset for model training and evaluation of model results, we created a urdu
news corpus which is named as Urdu News Dataset for Hierarchical Text
Classification (UNDHTC) which will be publicly available at github
222https://github.com/TaimoorAhmedJaved/Urdu-News-Dataset-for-Hierarchical-
Text-Classification. The main goal of corpus development is to make use of it
in comparison and evaluation in hierarchical classification and to help
researchers in future to perform NLP tasks. UNDHTC is classified into 12
categories that is 3 categories Sports, Technology and Entertainment which is
further classified into 9 sub categories cricket, hockey, football,
applications, mobile, internet, music, fashion and movies as shown in figure
11.

Figure 11. Urdu-News-Dataset-for-Hierarchical-Text-Classification

####  4.3.1. Dataset Creation

Most of data was collected from popular Pakistani urdu news websites which are
Daily Pakistan333(www.dailypakistan.com.pk, Urdupoint444www.urdupoint.com,
Express news555(www.express.pk, Dawn news666www.dawnnews.pk, BBC
Urdu777(www.bbc.com/urdu, ARYnews888www.urdu.arynews.tv, BOL
news999(www.bolnews.com/urdu and Hum news101010www.humnews.pk. To automate
collection of data, we have used Selenium with Chrome driver for Chrome web
browser.

####  4.3.2. Annotation

The dataset labels are in twelve categories. We labeled our dataset with the
support of our coworkers who have a solid knowledge of Urdu language. The
proper instructions and exemplars provided them for annotation of data. The
labels were assigned according to the context and subject of the sentence.

####  4.3.3. Dataset Statics

Approximately, 299815 news were extracted from different websites by using
scraping tools. 57566 news were cleaned and labeled as shown in table 1 and
total number of documents in categories are shown in table 2.

Table 1.  Dataset Statics Dataset Statistics
---
Total News | 299815
Cleaned | 57566
Labeled | 51325
Table 2. Dataset Stats in Detail Category | Sub Category | Number of documents | Total
---|---|---|---
| Cricket | 12431 |
Sports | Hockey | 2011 | 20002
| Football | 5560 |
| Internet | 1735 |
Technology | Applications | 3742 | 16208
| Mobile | 10731 |
| Movies | 3902 |
Entertainment | Music | 2552 | 15115
| Fashion | 8661 |

In next section, we will discuss experiments that were performed on this
dataset and also compare results with baseline methods.

##  5\. Experiments

In this section, we first introduce the dataset and apply preprocessing steps.
Then we conduct extensive experiments and compare our proposed approach with
state-of-the-art traditional machine learning algorithms and deep neural
model. In the end we will discuss experimental results. Figure 12 shows
complete flow diagram of our experiment.

Figure 12. Experimental Flow Diagram.

The total number of 57566 news were used in this experiment. UNDHTC dataset is
manually labeled which are divided into total 12 categories which contain 3
top categories and 9 sub categories. The training/test split for UNDHTC is
done by ourselves, which is 80% for training and 20% for test as shown in
table 3. We randomly sample 20% from the training data .

Table 3.  The training/test split for UNDHTC Dataset | Training | Testing | Class/Labels
---|---|---|---
51325 | 41060 | 10265 | 12

###  5.1. Preprocessing

We have applied basic pre-processing methods on our data. Data pre-processing
in every language is an essential step taken before applying any machine
learning algorithm or deep neural network. It helps to remove unnecessary data
in the corpus. English characters, special symbols, numeric values, and URLs
are omitted such that the text includes only the target Urdu language. The
structured representation of text data is efficiently controlled to improve
the experiment’s accuracy and efficiency. Preprocessing steps that we applied
on our dataset are as follows.

####  5.1.1. Tokenization

This is the initial step in the processing of any language of the NLP task. It
transforms the phrases into valuable tokens or a single expression. It
transforms raw text into a tokens chart, in which every token is a Word.
Inaccurate tokenization can affect the results of the experiment. Urdu is a
less linguistic tool and its signs face different problems as spatial
incoherence between terms and sentence limit detection. Here is a figure 13
which show urdu text converted into tokens.

Figure 13. Urdu tokenize document

####  5.1.2. Stop Words Removal

To remove unnecessary words we use stop words. It is used for removing words
that carry little to no meaning. Natural languages are consist of meaningless
words and functional words. In Urdu, we call stopwords as Haroof-e-Jar and in
English they are called conjunctions. Stop words are often omitted from the
corpus before NLP is done. By removing these words, we can get better results
in our research work.

####  5.1.3. Punctuation Removing

In Urdu, mostly used punctuation are ‘-‘, ’_’, and ‘.’. Such symbols do not
have much value to be used as a feature for classification tasks. So we remove
these symbols by using regular expressions.

###  5.2. Experimental Setup

All of our experiments were run on 6 core Intel Core i7 9750 @2.60GHz, with 16
GB RAM and GTX 1660ti GPU. The software that we used is jupyter notebook
having Python 3.7.6. In our experiments, we used public version of word2vec in
which training algorithm CBOW is used to train 100 dimensional word embeddings
with minimum count of words 5, filter window size is 5 and other default
parameters. For deep learning model, the parameters of training the models
were set as batch size 32, drop out 0.5 and other default parameters. Data was
trained on 80% for training and 20% randomly selected for test as shown in
table 3. We have done our hardest to use the best parameters in our
experiments.

###  5.3. Baselines and Deep Neural Model Comparison

We compare our proposed approach HMLSTM with several baseline methods, such as
Naïve Bayes, Support Vector Machines (SVMs), Decision Tree, Random Forest,
K-Nearest Neighbors, Logistic Regression and deep neural model such as
convolutional neural network (CNN).

  * •

Gaussian Naïve Bayes : We have used global approach in this method that builds
a single decision tree to classify all categories simultaneously. In this
method, we first convert urdu news text in the form of words. After this we
use one hot encoding to encode those words. We encode both columns category
and sub category having labels by using label encoding. Then we combine both
the labels (category and subcategory) in a single variable. After that, we
split the dataset to the train and the test parts. Then we apply GaussianNB
algorithm using binary relevance with default parameters in which variance is
1.0 raise to power -9 and perform predictions. skmultilearn library is used in
it (Szymański and Kajdanowicz, 2017).

  * •

Support Vector Machines (SVMs): In this method, we perform standard multi-
label classification using one-vs-the-rest (OvR) strategy because we have
multiple labels, so we use it. Scikit-learn is a pipeline framework for
automating workflows for training. In machine learning applications, pipelines
are very common because there are many data to manipulate and many
transformations to be carried out. So we used pipeline to train OnevsRest
classifier. We use LinearSVC with OneVsRestClassifier and other default
parameters.

  * •

Decision Tree: In this method, we also used global approach strategy. Data
Reading is done using Pandas. We first convert urdu news text in the form of
tokens. After this we use word2vec embedding to encode those words. For
labels, we use label encoding. Label encoding convert these labels into
vectors. As, we have multiple labels so we used multilabel classification.
Multilabel classification tasks can be handled using decision trees. In
Decision Tree Classifier, we set criterion=’entropy’ and remaining parameters
are set as default and perform predictions. sklearn library (Pedregosa et al.,
2011) is used for classification.

  * •

Random Forest: Random forest is also used for multilabel classification task.
So we used this method in hierarchical text classification. Random Forest can
inherently deal with multiclass datasets. As a result, we adopted a one-
versus-rest strategy using random forest classifier. Regular Random Forest
cannot perform well on multilabel classification (Adnan and Islam, 2015).
Experimental results shows better classification performances with one versus
all classifier. In random forest, we set parameters as default and perform
predictions.

  * •

K-Nearest Neighbors: In this method, we first convert data and labels into
vectors by using embedding and label encoding. After that, we split the
dataset to the train and the test parts then apply k-nearest neighbors (KNN)
algorithm. We set number of neighbors 3 with other default parameters and
perform predictions.

  * •

Logistic Regression: In this method, we also perform standard multi-label
classification using one-vs-the-rest (OvR) strategy. Scikit-learn (Pedregosa
et al., 2011) is a pipeline framework for automating workflows for training.
Here we also used pipeline to train OnevsRest classifier and apply logistic
regression on data. L2 regularization is applied in logistic regression with
other default parameters and perform predictions.

  * •

Convolutional Neural Network: In CNN, we first convert urdu news text in the
form of tokens. After this we use word2vec embedding to encode those words.
Then we apply CNN on our dataset, we pass the kernel over these embeddings to
find convolutions. CNN model is trained with two layers, one is convolution
and other is softmax layer. We also applied “relu” activation layer on the
output of every neuron. Drop out layer is also applied to reduce overfitting.

###  5.4. Evaluation Metrics

To evaluate the performance of algorithms we have used three common evaluation
metrics i.e. Precision, Recall, and F1 score which includes Micro-F1 and
Macro-F1. We also used Classification Accuracy, which is also known as term
accuracy. Let $TP_{l},FP_{l},FN_{l}$ be the number of true positives, false
positives and false negatives for the l-th label in the category C
respectively. Then the equation of Micro-F1 is

(4)
$P=\frac{\sum_{l\in\mathcal{C}}TP_{l}}{\sum_{l\in\mathcal{C}}TP_{l}+FP_{l}},R=\frac{\sum_{l\in\mathcal{C}}TP_{l}}{\sum_{l\in\mathcal{C}}TP_{l}+FN_{l}},F_{1Micro}=\frac{2PR}{P+R}\\\
$

Equation for Macro-F1 is defined as

(5)
$P_{l}=\frac{TP_{l}}{TP_{l}+FP_{l}},R_{l}=\frac{TP_{l}}{TP_{l}+FN_{l}},F_{1Macro}=\frac{1}{|\mathcal{C}|}\sum_{l\in\mathcal{C}}\frac{2P_{l}R_{l}}{P_{l}+R_{l}}\\\
$

###  5.5. Qualitative Analysis

To measure the performance of algorithms across different examples, we
summarizes some of the examples from evaluation set as shown in figure 14.We
can see that HMLSTM works well in most of the cases because LSTM can look back
to many time steps which means it retain long term memory when dealing with
long sentences. In first example, we can see that all baseline approaches
fails, but HMLSTM and CNN predict correctly. However our model still needs
improvement, as we can see in second example our model fails because it didn’t
consider context of news due to presence of keywords like actor and drama.

Figure 14. Qualitative Analysis of HMLSTM results and other baseline methods

###  5.6. Experimental Results

Table 4. HMLSTM and other model Results on Urdu News Dataset Methods | #Classes | Accuracy | Precision | Recall | Macro-F1 | Micro-F1
---|---|---|---|---|---|---
Gaussian Naïve Bayes | 12 | 0.4744 | 0.4223 | 0.8693 | 0.5257 | 0.6987
Support Vector Machines | 12 | 0.7711 | 0.7434 | 0.7769 | 0.7590 | 0.8878
Decision Tree | 12 | 0.7285 | 0.6113 | 0.6092 | 0.6102 | 0.7949
Random Forest | 12 | 0.7959 | 0.9256 | 0.6331 | 0.7391 | 0.8806
K-Nearest Neighbors | 12 | 0.5989 | 0.6300 | 0.5668 | 0.5467 | 0.6940
Logistic Regression | 12 | 0.7962 | 0.7875 | 0.7822 | 0.7848 | 0.8980
CNN | 12 | 0.8740 | 0.8866 | 0.8496 | 0.8632 | 0.9323
HMLSTM | 12 | 0.9402 | 0.9259 | 0.8812 | 0.8927 | 0.9683

To demonstrate practical significance of our proposed model, we compare HMLSTM
with all the baselines. The experiments perform on urdu news dataset (UNDHTC)
shows that our proposed approach Hierarchical Multi-Layer LSTMs (HMLSTM)
outperforms the machine learning models. The table 4 shows comprehensive
results of all models. CNN performed well and gave promising result as compare
to state-of-the-art methods. For traditional text classification algorithms,
we can see that Logistic Regression using one-vs-the-rest (OvR) strategy and
applying regularization, it outperform other models. Then we have Support
Vector Machines after LR, which is performing better than other four
algorithms. SVM is good in performing multi label classification, that is why
its better than rest. While K nearest neighbor perform least on urdu news
dataset.

Figure 15. Results showing Accuracy, Macro-F1 and Micro-F1

By splitting labels according to their levels, we evaluate causes of
performance gains. Table 4 indicates Macro-F1 variations between the base
models. HMLSTM achieves the best results in all evaluation metrics, which
shows HMLSTM is more competent for doing hierarchical multi label text
classification tasks with hierarchical group structure and it deal with
hierarchical category adequately and correctly. The main reason is that HMLSTM
could identify the correlations between the Urdu news texts and labels, while
other algorithms can’t. HMLSTM integrate the predictions of every level in the
form of hierarchical structure.

Figure 16. Hierarchical Multi-Layer LSTMs (HMLSTM) Results

The bar chart as shown in figure 16 clearly demonstrates the effectiveness of
our proposed approach for hierarchical text classification.

##  6\. CONCLUSION AND FUTURE WORK

In this paper, we have proposed Hierarchical Multi-Layer LSTMs method built
upon deep neural network which is used for hierarchical text classification of
urdu news text. We first applied Text Representing Layer for obtaining numeric
representation of text and categories by applying embedding. After receiving
combined and structured representation of urdu news text and labels, we
applied Urdu Hierarchical LSTM Layer, an end-to-end fully connected deep LSTMs
network to perform automatic feature learning. Finally, extensive experiments
is performed on our self created dataset named as Urdu news dataset for
hierarchical text classification. Our experimental results clearly demonstrate
that our method outperforms baseline methods significantly and also performed
well as compare to neural model CNN. In the future, we are interested to
concentrate further on improving the network structure in order to further
improve the efficiency of our method. We will also try to develop Bi-LSTM
based approach to to obtain improved hierarchical structure. In future, we
will also try to optimize our model because it takes long training time.

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# Two-particle Direct Photon-Jet Correlation Measurements in PHENIX

Justin Frantz for the PHENIX Collaboration

(Received: date / Revised version: date)

###### Abstract

Various 2-particle direct photon-hadron correlation yields in $p+p$ and
$Au+Au$ collisions at $\sqrt{s_{NN}}$ = 200 GeV are presented. The per-trigger
yield of direct photon hadron pairs from direct-photon-jet correlations is
obtained by a statistical subtraction of the decay photon pairs from inclusive
photon-hadron sample. The decay photon per-trigger yields are estimated from
the measured $\pi^{0}$-hadron by means of a Monte Carlo based calculation
which takes into account decay kinematics and detector response. Under the
assumption that the suppression is nearly $p_{T}$ independent using a specific
averaging scheme, we find a ratio of $Au+Au$ to $p+p$ per-trigger photon
yields $I_{AA}$ averaged over all available $p_{T}$ bins consistent with the
single particle suppression level $R_{AA}$, which can be interpreted as a
qualitative confirmation of the basic geometrical picture of jet suppression
at RHIC. The application of the event by event photon isolation cuts in $p+p$
results our highest precision measurement yet, and allows for precision
studies of the baseline fragmentation function $D(z)$ and also a determination
of the apparent intrinsic $k_{T}$, or non-zero transverse momentum of the
original collision partons. With a model dependent extraction method, the
average $\sqrt{<k_{T}^{2}>}$ at this $\sqrt{s}$ in $p+p$ is found to be
approximately 2.5-3 GeV, consistent with analysis of di-hadron (di-jet)
correlations [1]. Finally, we present a unique direct measurement of prompt
photons from jet fragmentation.

Hard Probes 2008 Conference Proceedings. June 9th, 2008. Illa da Toxa, Spain

Two-particle Direct Photon-Jet Correlation

Measurements in PHENIX

J. Frantza for the PHENIX Collaboration

a State University of New York (SUNY), Stony Brook, Stony Brook, NY, U.S.A

Contact e-mail<EMAIL_ADDRESS>
##  1 Introduction

At the Relativistic Heavy Ion Collider (RHIC), experimental results from the
have established the formation of hot and dense matter of a fundamentally new
nature in Au+Au collisions at $\sqrt{s_{NN}}$=200 GeV [2]. One of the most
important probes of this dense colored matter is energy loss by hard partons
leading to a suppression of normal jet production of hard (E $>\sim 1-2$ GeV)
particles. As a complement to di-hadron correlations, which can directly
access di-jet production and their structure [3], [4], direct photon-hadron
correlations can be used to study photon-jet production in the medium without
various biases and complications. This is due to the fact that since it lacks
color charge, the photon escapes the dense QCD matter without interacting. For
this reason, using high $p_{T}$ prompt photons, which have been demonstrated
to be unmodified when traversing the medium due to their lack of color charge
[5], as triggers in $\gamma$ \- hadron ($i.e.$ $\gamma$-jet) correlation
studies has long been considered to be a ”golden channel” to study energy
loss. When the photon trigger is a product of the dominant Leading Order QCD
Compton scattering process, the photon’s 4-momentum should be the same as the
opposite quark parton’s. Therefore the photon in its primordial state can be
used to directly measure the fragmentation functions of the opposite jet which
itself is modified by final state effects in the case of Au+Au collisions.

The above picture is based on a Leading Order (LO) approximation and
interpretation of events where there are always only two ”clean” outgoing
products, di-parton (_g, q_) or photon-parton for $\gamma$-jet, which are
perfectly balanced in momentum in the direction transverse to the incoming
colliding particles. In reality there are a number of complications that could
make this interpretation incorrect that need to be understood even in the
baseline elementary $p+p$ collisions. For example, Next to Leading Order
(NLO)/fragmentation contributions of hard prompt photons in di-jet events
obviously have different properties, in particular in $Au+Au$ are expected to
follow similar suppression effects as the di-jet events which makes making
quantifying the baseline probability for their appearance in the overall
$\gamma$-jet event samples crucial. Another example is the role of an apparent
intrinsic net transverse momentum $k_{T}$ of the incoming colliding parton-
parton system potentially due to initial state effects or non-perturbative or
higher order gluon radiation, which at the value discovered in dihadron
correlations [1] can considerably change the actual momentum transfer
($Q^{2}$) being sampled in the kinematic ranges currently being studied. Thus,
before studying $\gamma$-jet production A+A, one needs to understand these
intrinsic complications to the LO picture in $p+p$ to gauge their effect on
calculations and interpretations of energy loss observables.

This analysis was performed using approximately 950 million Au+Au minimum bias
events from the Run 4 data set and 471 million level-1 photon-triggered events
from the p+p Run 5 and Run 6 data sets. The Beam-Beam Counters (BBC) and Zero-
Degree Calorimeters (ZDC) are used to trigger the minimum bias data. These
detectors are also used to estimate the collision centrality. The p+p photon
trigger requires that a module in the Electromagnetic Calorimeter (EMC) fire
in coincidence with the BBC.

The PHENIX central arms, each covering the 0.7 units of pseudo-rapidity around
mid-rapidity and $90^{\circ}$ in azimuth, contain charge particle tracking
chambers and electromagnetic calorimetry. The EMC consists of two types of
detectors, six sectors of lead-scintillator sampling calorimeters and two of
lead-glass Cherenkov calorimeters. The fine segmentation of the EMC ($\sim
0.01\times 0.01$ for PbSc and $\sim 0.008\times 0.008$ for PbGl) allows for
the reconstruction of the $2\gamma\ $ $\pi^{0}$ and $\eta$ decays to $p_{T}$
$>20$ GeV.

Charged hadrons are detected using a drift chamber at a radial distance of 2.0
m and and a multi-wire proportional chambers (PC1) at a distance of 2.5 m. The
momentum resolution was determined to be $0.7\%\bigotimes 1.0\%p$ (GeV/c).
Secondary tracks from decays and conversions are suppressed by matching tracks
to hits in a second multi-wire chamber (PC3) and the EMC, both at distance of
$\sim 5.0$ m. Track projections to the EMC plane are used to veto charged
hadrons which shower in the EMC.

Two-particle correlations are performed by measuring the azimuthal angle
between photon triggers and charged hadrons. The correlation function

$C(\Delta\phi)\equiv\frac{N_{pairs}(\Delta\phi)}{N_{mixed}(\Delta\phi)}$

corrects for the limited acceptance of photon-hadrons pairs by dividing out
the mixed events distribution. The correlation function is decomposed via the
two-source model where the jet correlation is superimposed on an underlying
event which is modulated by elliptic flow. Hence, the jet function is
expressed as $J(\Delta\phi)\equiv C(\Delta\phi)-b_{0}(1+2\langle
v_{2}^{\gamma}v_{2}^{h}\rangle\cos{2\Delta\phi})$. The underlying event level,
$b_{0}$, is determined by the ZYAM procedure, described elsewhere [6].

We define a direct photon as any photon not from a decay process. It follows
that the per-trigger yield ($Y\equiv 1/N_{trigger}\ J(\Delta\phi)$) for direct
photons may be obtained by a statistical subtraction of the decay per-trigger
yield from the inclusive per-trigger yield according to:

$Y_{direct}=\frac{R_{\gamma}Y_{inclusive}-Y_{decay}}{R_{\gamma}-1}$ (1)

where $R_{\gamma}$$\equiv N_{inclusive}/N_{decay}$. $R_{\gamma}$ is determined
for Au+Au collisions in [5] and is derived from the $\pi^{0}$ [7] and direct
photons spectra in p+p [8]. The direct photon yields from the statistical
subtraction method do not, by definition, exclude photons from jet
fragmentation or medium induced photon production.

The decay photon per-trigger yields are determined from the parent ($\pi^{0}$
or $\eta$) per-trigger yields via a Monte Carlo procedure. A flat distribution
of parent mesons are decayed into the PHENIX aperture uniformly in the z and
phi directions. This determines the mapping of the parent to daughter $p_{T}$,
$\wp(p_{T}^{\pi^{0}}\rightarrow p_{T}^{\gamma})$, where $p_{T}^{\gamma}$ is
smeared by the detector resolution. In order to reproduce the correct
$p_{T}$dependence of the decay photon distribution W is applied as a weight
factor to the parent meson-hadron $p_{T}$distribution on a pair-by-pair basis.
The finite reconstruction efficiency of the parent mesons is corrected for
using the published PHENIX $p_{T}$ spectra [7], [9]. The decay per-trigger
yield from $\pi^{0}$’s can then be expressed in terms of the parent per-
trigger yield.

##  2 Constraining Direct Photon-Jet Production in Elementary $p+p$ Collisions

###  2.1 Isolation Cut Analysis

In addition to the above statistical subtraction method, standard photon
isolation cuts were applied event by event in a new analysis in order to
dramatically reduce the contamination of di-jet events with $\pi^{0}$ decay or
fragmentation photons in the $\gamma$-hadron event sample. To be considered
isolated, the sum of $p_{T}$from all tracks and and EMCal energies must be
$<0.1E_{\gamma}$ in a cone around the photon of size $\Delta
R=\sqrt{\Delta\phi^{2}+\Delta\eta^{2}}=0.5$. Statistical subtraction of the
remaining contribution for isolated $\pi^{0}$ production is achieved through a
modified version of the statistical method above where isolated $\pi^{0}$ are
used as input to the decay-photon calculation.

As in the statistical method the analysis is performed as a function of
$\Delta\phi$. We find good agreement with the statistical method results as
shown in figure 1. Since the statistical subtraction method includes, in
principle, the contribution from fragmentation photon triggers, the agreement
between the two methods places a constraint on the magnitude of such a
contribution.

Figure 1: For two trigger photon bins as indicated on the figure, the awayside
per-trigger yield vs. $\Delta\phi$ for the statistical and isolation cut
analysis. The two methods give consistent results with the isolation method
having much improved precision due to having only a small decay photon
background subtraction.

###  2.2 Per trigger yields and Awayside Jet Fragmentation Function Analysis

Results from the isolation cut analysis are shown in figure 2, plotted in
terms of the fragmentation variables $z_{T}=p_{T}^{assoc}/p_{T}^{\gamma}$ and
$x_{E}=z_{T}\cos{\Delta\phi}$ (taking only the component of the associated
hadron momentum that is in the same direction as the trigger photon). We
measure in 6 photon trigger $p_{T}$ bins covering the range 5-15 GeV/c. The
inverse slope parameter of exponential fits to the $x_{E}$ distribution with
$\pi^{0}$ and direct photon triggers are shown in figure 3. Comparisons with
theoretical calculations are currently underway.

Figure 2: Per trigger yields of direct photon-hadron pairs as a function of
$x_{E}/z_{T}$ for various selections of trigger $p_{T}$. The data have been
scaled by factors of 10 for visibility. Figure 3: The inverse slope parameter
of exponential fits to the $x_{E}$ distributions for $\pi^{0}$ direct photon
triggers.

###  2.3 Model Dependent Determination of $k_{T}$

Also in [1], a method for extracting the apparent intrinsic $k_{T}$ itself was
described and used to extract a value of $k_{T}$= $2.68\pm 0.35$ for
$\pi^{0}$-h correlations. Using isolated direct photon triggers, the method is
simplified and can be also be used to extract a $k_{T}$ value in this channel.
Please refer [1] for details of the method. The simplification occurs due to
the lack of an additional fragmentation variable on the nearside, i.e
$p_{Tt}\equiv\hat{p}_{Tt}\equiv p_{T\gamma}$. The measured experimental
variable $p_{out}$ whose distribution for various trigger $p_{T}$ bins is
shown in figure 4, is defined as $p_{Ta}\sin{\Delta\phi}$, or the associated
hadron’s transverse momentum component perpendicular to the trigger photon
direction, and is proportional to the $k_{T}$ on an event by event basis.

Figure 4: $p_{out}$ distributions for various values of trigger $p_{T}$. The
data have been scaled by factors of 10 for visibility.

By finding the average $p_{out}$ for each trigger photon $p_{T}$ bin as shown
in 5 $a)$ one can extract the quantity average $k_{T}$/$\hat{x}$ where
$\hat{x}$ is just the ratio of the true awayside jet momentum to that of the
direct photon trigger. This can be extracted from other measurements as in [1]
and eliminated, leaving a pure measurement of the $k_{T}$. For now, we simply
use the event generator PYTHIA $6.3$ to generate direct photon processes with
initial and final state radiation turned off, and phenomenological $k_{T}$
parameters as shown on 5 $b)$ to extract $\hat{x}$ and make a comparison to
PYTHIA, which shows that for PYTHIA-like distributions of $\hat{x}$ a $k_{T}$
parameter in the vicinity of 2.5-3 GeV gives values similar to the data, well-
consistent with the value of 2.68 found for di-hadron di-jet correlations.

This implies that the same $k_{T}$ bias effects in the di-hadron correlations
exist also in the direct photon-jet correlations. This makes the comparison
with the perturbative QCD calculations, which at only NLO should _not_ contain
such $k_{T}$ bias modifications even more important, as well as understanding
in energy loss models of $Au+Au$ how such a large value of imbalance between
the trigger photon and awayside jet might alter energy loss interpretations.

Figure 5: a) $\sqrt{<p_{out}^{2}>}$ vs trigger $p_{T}$ for direct photons b)
$k_{T}$/$\hat{x}$ vs trigger $p_{T}$ for direct photons along with
calculations from PYTHIA $6.3$.

###  2.4 Direct Measurement of Prompt Photons from Jet Fragmentation

PHENIX has made further strides towards understanding complications to the LO
intepretations of direct photon-jet correlations, in studying the contribution
of single prompt photons that occur in di-jet events, the so-called
fragmentation photons produced by NLO hard photon radiation or non-
perturbative fragmentation processes. A direct measurement technique discussed
in [10] has been used to measure the angular distribution of such photons with
respect to trigger _hadrons_ in events where high $p_{T}$ photons are tagged
to only be nearby such hard hadrons–an _anti_ -isolation selection.
Integrating this distribution one can find the fraction of such photons to
total hadron-correlated photons from all sources including decay, shown in
figure 6 which can possibly be related to the fraction of the total direct
photon production rate due to these fragmentation photons. However due to the
restricted kinematic region of the measurement, interpretations of this
fraction and the angular distribution itself need input from sophisticated
higher order pQCD calculations (which likely do not yet exist) and thus close
attention from the theory community. Nonetheless, the measurement is exciting
because it provides the first step towards making the measurement in the
$Au+Au$ environment where several interesting predictions of jet-medium
enhancement of the rate of production of such photons exist.

Figure 6: Fraction of hadron-photon that contain a prompt (fragmentation)
photon

##  3 Constraining Energy Loss theory in Au+Au with $\gamma$-jet

In previous conferences [11, 12] PHENIX has presented $Au+Au$ results of the
statistical subtraction method for direct photon-hadron correlation yields.
With the now statistically improved $p+p$ results (for this section, using the
pure statistical subtraction method in $p+p$ for comparison, combining Run6
and Run6 statistics) and an expanded $p_{T}$ range for the trigger photons in
$Au+Au$, we can now divide the per-trigger yields for $Au+Au$ and $p+p$in many
$p_{T}$ bins making the ratio $I_{AA}$ = $Y_{direct}^{A+A}/Y_{direct}^{p+p}$
for an awayside integration range of $2\pi/5$ radians around $\pi$. An example
is shown for the direct photon (trigger) $p_{T}$ bin 5-7 GeV/c in figure 7. It
is apparent that there are very large uncertainties, but most points have
positive yield in $Au+Au$ and a value of $I_{AA}$ between 0 and 1 which
indicates the basic expectation of suppression of the awayside jet.

Figure 7: Ratio $I_{AA}$ (see text) for the lowest trigger direct photon
$p_{T}$ bin.

We find that different sources of uncertainties dominate in different ranges
of trigger photon and associated hadron hadron $p_{T}$. As either $p_{T}$ is
increased the statistics in the raw correlation functions obviously decrease
as the production probabilities follow steeply falling spectra. However the
combinatoric background from pairs of hard particles and soft underlying or
otherwise uncorrelated particles is reduced dramatically as the $p_{T}$ of the
associated hadron is increased, having the effect of reducing the systematic
uncertainty from the underlying event subtraction. Further, as the $p_{T}$ of
the trigger photon is increased there is a larger fraction of direct photon-h
pairs in the inclusive photon-h pair sample due to the increasing direct
photon signal relative to the suppressed decay photon background, and thus the
higher trigger photon $p_{T}$ bins also have a reduction in the uncertainty
from the subtraction in equation 1. Because of these effects we find that the
total uncertainty remains more constant with increasing $p_{T}$ than the loss
in statistical precision that the falling production probability would
normally manifest. For this reason, integrating over large $p_{T}$ hadron or
trigger $p_{T}$ bins causes a loss of information since the steeply falling
production probability causes uncertainties to be dominated by the source
corresponding to the low end of the $p_{T}$ bin. Therefore in order to
effectively give the higher $p_{T}$ bins more weight, we take the plain
average of all $p_{T,\gamma},p_{T,h}$ bins. We call this average the Mean
Value $I_{AA}$ akin to performing the functional mean value:

$MeanValue\;\;I_{AA}=\frac{1}{{\Delta
p_{T}}}\int{I_{AA}(p_{T}^{assoc})dp_{T}}$ (2)

Under the further assumption that $I_{AA}$ remains constant with $p_{T}$ this
average indeed corresponds exactly to the functional mean value, and it is
found that this assumption is satisfied to what is likely a sufficient degree
(considering our large uncertainties which would dominate any such
uncertainties in the assumption) both in measurements of di-hadron
correlations [4] and [3] and in most theoretical predictions [13, 14] at
sufficiently high $p_{T}$ (associated $p_{T}$ $>\simeq$ GeV/c).

Figure 8: a) Mean value $I_{AA}$ for each trigger $p_{T}$ bin b) over all
$p_{T}$ bins for 0-20%, 20-40%, and 40-92% centrality bins.

Results of the Mean Value $I_{AA}$ are shown in figure 8. The transition of
different uncertainty sources from systematic to statistical is even more
apparent in figure 8 $a)$ for central events, and indeed the Mean Value
$I_{AA}$ seems to favor a constant value _vs._ trigger direct photon $p_{T}$
although the uncertainties are still much too large to rule out with any
significance rather large possible trigger photon $p_{T}$ dependence
scenarios. Still, again taking the average of _all_ $I_{AA}$ points together,
shown on figure 8 $b)$, now also for more peripheral bins, we find that the
Mean Value $I_{AA}$ for the central events is significantly positive at a two-
sigma level and consistent with the single particle suppression level
$R_{AA}$.

This confirms the basic geometrical picture of energy loss at RHIC [15],[16]
because vertices in $\gamma$-h correlations, and the trigger direct photon
themselves, come from the entire collision volume. Therefore the fraction of
$\gamma$-$h$ pairs that are observed without significant suppression, come
from the same geometrical region that surviving single high $p_{T}$ particles
do. Dividing this quantity by the unsuppressed direct photon yield is
analogous to dividing the surviving single particle production rate by the
expectation from $p+p$ multiplied over the entire production weighted
geometric volume of the initial distribution of all hard scattering production
points, as is done in the construction of $R_{AA}$Ṫhis means that the away-
side suppression in the $\gamma-jet$ channel should simply reflect the same
geometry as the single particle picture and, if geometry plays a dominant
role, give the same suppression level. Since this geometrical picture is
believed to be a ”surface bias” picture where the only di-jets initially
produced near the surface it could be a confirmation of the surface bias
picture, although further comparison to the di-hadron $I_{AA}$ comparing
$\gamma$-hadron and di-hadron correlations together in the same energy loss
framework, are necessary to make this statement, in order to rule out possible
effects in the di-hadron $I_{AA}$ that are believed _not_ to be from geometry.

Looking forward to the full release of our Run7 analysis, we also present a
first look in figure 9 at the lowest trigger $p_{T}$ bin using $\simeq$ 3.0
billion events, which entices us to look across several $p_{T}$ bins to look
for consistent behavior that may be consistent to jet-medium displaced peak as
seen already in di-hadron correlations, although at this point uncertainties
are too large to make a definitive statement with just a single $p_{T}$ bin
combination.

Figure 9: $\Delta\phi$ distribution of direct photon-hadron correlations from
Run7 data for 5-7 trigger direct photon bin.

##  4 Conclusions

Various 2-particle direct photon-hadron correlation yields in $p+p$ and
$Au+Au$ collisions at $\sqrt{s_{NN}}$ = 200 GeV have been presented. Under the
assumption that the suppression is nearly $p_{T}$ independent using a specific
averaging scheme, we find a ratio of $Au+Au$ to $p+p$ per-trigger photon
yields averaged over all available $p_{T}$ bins, $I_{AA}$, consistent with the
single particle suppression level $R_{AA}$, which can be interpreted as a
qualitative confirmation of the basic geometrical picture of jet suppression
at RHIC. To the extent that the prevailing geometric picture is believed to be
that of surface bias where only jets ejected near the surface dominately
contribute to yields, [15], [16], it may be said to confirm this picture,
however it should be noted that any geometric scenario would yield $I_{AA}$
$\simeq$ $R_{AA}$. Nonetheless future comparisons of $I_{AA}$ from
$\gamma$-jet to that of di-jets, may indeed yield some insight into the
details of possible surface bias models.

We have also presented the first event by event isolation cut 2-p correlation
results at RHIC. The application of the event by event photon isolation cuts
in $p+p$ results our highest precision measurement yet, and allows for
precision studies of the baseline fragmentation function $D(z)$, and well as a
variable $p_{out}$ which is proportional to the apparent intrinsic $k_{T}$, or
non-zero transverse momentum of the original collision partons. With a model
dependent extraction method, the average $\sqrt{<k_{T}^{2}>}$ at this
$\sqrt{s}$ in $p+p$ is found to be approximately 2.5-3 GeV, consistent with
analysis of di-hadron (di-jet) correlations [1]. The possible implications of
this and also the improved precision in the isolated yields warrant detailed
comparison with the baseline perturbative QCD (pQCD) calculations used in the
various models of jet energy loss. Finally, we have presented a unique direct
measurement of single prompt photons from jet fragmentation, of both angular
information and the fraction of these photons in the entire photon sample
(including decay photons) in the vicinity of the jet cone and for specific
$p_{T}$ cuts.

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