This master thesis describes an algorithm for automated categorization of scientific documents using deep learning techniques and compares the results to the results of existing classification algorithms.

As an additional goal a reusable API is to be developed allowing the automation of classification tasks in existing software.

A design will be proposed using a convolutional neural network as a classifier and integrating this into a REST based API. This is then used as the basis for an actual proof of concept implementation presented as well in this thesis.

It will be shown that the deep learning classifier provides very good result in the context of multi-class document categorization and that it is feasible to integrate such classifiers into a larger ecosystem using REST based services.

CHAPTER: NOTATION

This list gives an overview about frequently used symbols and their significance in this thesis:

$D$	   	Corpus consisting of $\norm{D}$ documents

$d \in D$	   	An individual document, consisting of $\norm{d}$ terms

$n$     Sample/document index

$N$     Number of samples/documents

$t \in d$     A term within a document (also used as an index)

$tf(t,d)$     Frequency of the term $t$ within a document $d$

$df(t,D)$     Frequency of documents in the corpus $D$ that contain the term $t$

$tfidf(t,d)$     The tf-idf weight of a term $t$ in a document $d$

$v$     The vector representation of a term, with $\norm{v}$ components

$i,j$     Neuron indices

$w_{i,j}$     The weight a neuron $i$ applies to the output of a neuron $j$

$netinput_i$     Weighted sum of a neuron's input

$a_i$     Output of neuron $i$

$\sigma(netinput)$     The activation function of a neuron

$\sigma_{tanh}$     The tanh activation function

$\sigma_{sigmoid}$     The sigmoid/logistic activation function

$\sigma_{softmax}$     The softmax activation function

$\sigma_{relu}$     The ReLU activation function

$\sigma_{leaky}$     The Leaky ReLU activation function

$X$     Input of a classifier (a Tensor)

$y$     Vector representing the expected output of a classifier

$c_k \in C$     The categories/classes to be differentiated

$\norm{y} = \norm{C}$     Number of categories/classes

$E$     Error function used to train a neural network

$E'$     First derivative of the error function

$E_{sq}$     Quadratic error function

$E_{bc}$     Binary cross-entropy error function

$E_{cc}$     Categorical cross-entropy error function

symbol     Style used for code symbols, such as variables

Missing 'biblatex' package
The bibliography requires the 'biblatex' package.

The MIT Press

titleA Semantic Web Primer, 2Nd Edition (Cooperative Information Systems)

The MIT Press

titleDigital Libraries (Digital Libraries and Electronic Publishing)

Hanover, MA, USA

Now Publishers Inc.

journaltitleFound. Trends Mach. Learn.
titleLearning Deep Architectures for AI

journaltitleJ. Mach. Learn. Res.
titleA Neural Probabilistic Language Model

titleLearning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

titleThe Loss Surface of Multilayer Networks

titleEmpirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

titleDeep Big Simple Neural Nets Excel on Handwritten Digit Recognition

abstractThe support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
journaltitleMachine Learning
titleSupport-Vector Networks

Cass, Lockwood & Brainard Company

titleA Classification and Subject Index, for Cataloguing and Arranging the Books and Pamphlets of a Library

journaltitleJ. Mach. Learn. Res.
titleWhy Does Unsupervised Pre-training Help Deep Learning?

University of California, Irvine

titleArchitectural Styles and the Design of Network-based Software Architectures

RFC Editor

howpublishedRFC 2616
titleRFC 2616, Hypertext Transfer Protocol – HTTP/1.1
2616 http standard seminar2006 rfc

Upper Saddle River, NJ, USA

Prentice Hall Press

titleDatabase Systems: The Complete Book

booktitleProceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011
titleDeep Sparse Rectifier Neural Networks

titleA Primer on Neural Network Models for Natural Language Processing

titleDeep Residual Learning for Image Recognition

abstractMost current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
journaltitleIEEE Signal Processing Magazine
titleDeep Neural Networks for Acoustic Modeling in Speech Recognition

Cambridge, MA, USA

MIT Press

journaltitleNeural Comput.
titleLong Short-Term Memory

IEEE Press

booktitleA Field Guide to Dynamical Recurrent Neural Networks
titleGradient flow in recurrent nets: the difficulty of learning long-term dependencies

University of Hagen

titleAsset Annotation auf Basis von Namend Entity Recognition im RAGE Eco-system

London, UK, UK

booktitleProceedings of the 10th European Conference on Machine Learning
seriesECML '98
titleText Categorization with Suport Vector Machines: Learning with Many Relevant Features

booktitleJournal of Advances In Information Technology, VOL
titleA Review of Machine Learning Algorithms for Text-Documents Classification

titleConvolutional Neural Networks for Sentence Classification

titleAdam: A Method for Stochastic Optimization

Amsterdam, The Netherlands

The University of Amsterdam

titleAn Introduction to Neural Networks

title20 newsgroups data set

University of Hagen

titleAbout the modernization of a web based machine learning environment

MIT Press

booktitleThe Handbook of Brain Theory and Neural Networks
titleConvolutional Networks for Images, Speech, and Time-Series

Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

abstractDeep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
titleDeep learning

journaltitleJ. Mach. Learn. Res.
titleRCV1: A New Benchmark Collection for Text Categorization Research

New York, NY, USA

journaltitleJ. ACM
titleAutomatic Indexing: An Experimental Inquiry

titleEfficient Estimation of Word Representations in Vector Space

Association for Computational Linguistics

booktitleProc. of the 2013 Conf. of the NAACL: HLT
titleLinguistic Regularities in Continuous Space Word Representations

Cambridge, MA

MIT Press

The MIT Press

titleFoundations of Machine Learning

University of Hagen

titleÜber die Auswahl und Evaluation von Cloud Services zur Dokumentenkategorisierung

New York, NY, USA

titleScalable Parallel Programming with CUDA

Determination Press

titleNeural Networks and Deep Learning

Stanford University

titleOntology Development 101: A Guide to Creating Your First Ontology

booktitleEmpirical Methods in Natural Language Processing (EMNLP)
titleGloVe: Global Vectors for Word Representation

howpublishedVrije Universiteit Brussel
titleOntologies - Introduction and Overview

journaltitleBulletin of Information Technology
titleFrom Card Catalogue to Web OPACs

booktitle2003 CIB w78 conference
titleClarity in the usage of the terms ontology, taxonomy and classification
titleReuters Corpora (RCV1, RCV2, TRC2)

titleNeuronale Netze: eine Einführung in die Grundlagen, Anwendungen und Datenauswertung

seriesSpringer Lehrbuch
titleTheorie der neuronalen Netze: eine systematische Einführung

titleword2vec Parameter Learning Explained

journaltitlePsychological Review
titleThe Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain

New York, NY, USA

journaltitleCommun. ACM
titleA vector space model for automatic indexing
automatic indexing,automatic information retrieval,content analysis,document space

Thessaloniki, Greece

booktitleProceedings of the 20th International Conference on Artificial Neural Networks: Part III
titleEvaluation of Pooling Operations in Convolutional Architectures for Object Recognition

New York, NY, USA

journaltitleACM Comput. Surv.
titleMachine Learning in Automated Text Categorization
ine learning,text categorization,text classification

American Statistical Association

abstractWe consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptotically equivalent to many other model selection methods such as the Akaike information criterion (AIC), the Cp, and the bootstrap, is asymptotically inconsistent in the sense that the probability of selecting the model with the best predictive ability does not converge to 1 as the total number of observations n → ∞. We show that the inconsistency of the leave-one-out cross-validation can be rectified by using a leave-nν-out cross-validation with nν, the number of observations reserved for validation, satisfying nν/n → 1 as n → ∞. This is a somewhat shocking discovery, because nν/n → 1 is totally opposite to the popular leave-one-out recipe in cross-validation. Motivations, justifications, and discussions of some practical aspects of the use of the leave-nν-out cross-validation method are provided, and results from a simulation study are presented.
journaltitleJournal of the American Statistical Association
titleLinear Model Selection by Cross-Validation

Tarrytown, NY, USA

Pergamon Press, Inc.

journaltitleInf. Process. Manage.
titleA Systematic Analysis of Performance Measures for Classification Tasks
ine Learning,Performance evaluation,Text classification

journaltitleJournal of Machine Learning Research
titleDropout: A Simple Way to Prevent Neural Networks from Overfitting
deep learning,model combination,neural networks,regularization

University of Hagen

titleTowards effectivity augmentation of automated scientific document categorization by continuous feedback

RFC Editor

abstractJavaScript Object Notation (JSON) is a lightweight, text-based, language-independent data interchange format. It was derived from the ECMAScript Programming Language Standard. JSON defines a small set of formatting rules for the portable representation of structured data.
howpublishedRFC 7159
seriesRequest for Comments
titleThe JavaScript Object Notation (JSON) Data Interchange Format
family=Theano Development Team,

journaltitlearXiv e-prints
titleTheano: A Python framework for fast computation of mathematical expressions
Computer Science - Symbolic Computation,Computer Science - Learning,Computer Science - Mathematical Software

titleEmpirical Evaluation of Rectified Activations in Convolutional Network

titleText Understanding from Scratch

titleA Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

I hereby declare that the thesis submitted is my own unaided work. All direct or indirect sources used are acknowledged as references. This paper was not previously presented to another examination board.

[5cm](Thomas Krause)

Cologne, 26th of June 2016


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