# Learning Relation Prototype from Unlabeled Texts for Long-tail Relation
Extraction

Yixin Cao, Jun Kuang, Ming Gao∗, Aoying Zhou, Yonggang Wen, Tat-Seng Chua
∗Corresponding author. This paper is an extended version of the ICDE’2020
conference paper [1].Yixin Cao and Yonggang Wen are with School of Computer
Science and Engineering, Nanyang Technological University, Singapore. Email:
<EMAIL_ADDRESS>and<EMAIL_ADDRESS>Kuang and Aoying Zhou are with
School of Data Science and Engineering, East China Normal University,
Shanghai, China. Email<EMAIL_ADDRESS>Ming Gao is with the School
of Data Science and Engineering, and KLATASDS-MOE in the School of Statistics,
East China Normal University, Shanghai, China, 200062<EMAIL_ADDRESS>Tat-Seng Chua is with School of Computing, National University of Singapore,
Singapore. Email<EMAIL_ADDRESS>
###### Abstract

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by
extracting entity relations from texts. However, it usually suffers from the
long-tail issue. The training data mainly concentrates on a few types of
relations, leading to the lack of sufficient annotations for the remaining
types of relations. In this paper, we propose a general approach to learn
relation prototypes from unlabeled texts, to facilitate the long-tail relation
extraction by transferring knowledge from the relation types with sufficient
training data. We learn relation prototypes as an implicit factor between
entities, which reflects the meanings of relations as well as their
proximities for transfer learning. Specifically, we construct a co-occurrence
graph from texts, and capture both first-order and second-order entity
proximities for embedding learning. Based on this, we further optimize the
distance from entity pairs to corresponding prototypes, which can be easily
adapted to almost arbitrary RE frameworks. Thus, the learning of infrequent or
even unseen relation types will benefit from semantically proximate relations
through pairs of entities and large-scale textual information.

We have conducted extensive experiments on two publicly available datasets:
New York Times and Google Distant Supervision. Compared with eight state-of-
the-art baselines, our proposed model achieves significant improvements (4.1%
F1 on average). Further results on long-tail relations demonstrate the
effectiveness of the learned relation prototypes. We further conduct an
ablation study to investigate the impacts of varying components, and apply it
to four basic relation extraction models to verify the generalization ability.
Finally, we analyze several example cases to give intuitive impressions as
qualitative analysis. Our codes will be released later.

###### Index Terms:

Relation Extraction; long-tail; Knowledge Graph; Prototype Learning.

##  1 INTRODUCTION

In the past decade, we have seen the emergence of various Knowledge Graphs
(KGs), such as YAGO [2] and DBPedia [3]. They have achieved great success in
both academic and industrial applications, ranging from recommendation [4] to
Question Answering [5]. However, these KGs are far from complete, which limits
the benefits of transferred knowledge. Relation Extraction (RE) is a vital
step to complete KGs by extracting the relations between entities from texts.
It is nontrivial since the same relation type may have various textual
expressions, and meanwhile, different types of relations can also be described
with the same words. Such ambiguity between relations and texts challenges the
supervision of RE models.

Due to the expensive human annotation cost, distant supervision is proposed to
automatically annotate the mappings between sentences and relations [6]. It
assumes that if two entities participate in a relation, a.k.a., a triple
$(e_{h},r_{i},e_{t})$ (i.e., head entity, relation, tail entity) exists in KG,
all of the sentences that contain $e_{h}$ and $e_{t}$ might express the
relation $r_{i}$. However, it is argued that the quality and quantity of
automatic annotations are usually not satisfactory [7, 8]. In terms of
quality, much noise comes with the failure of the assumption — some sentences
include the same entity pair $(e_{h},e_{t})$ but express another relation
$r_{j}$. As shown in Figure 1a, given the triple (Phoenix,
/location/us_state/capital, Arizona), we collect two sentences that include
the entity pair (Phoenix, Arizona). Clearly, the first sentence expresses a
similar meaning with the given relation type, but the second one implies
another type of relation city of, which brings in noise to the training
corpora111As the term relation can refer to either relation type or relation
instance (between specific entity pairs), in the paper, we simplify the use of
term relation for relation type unless otherwise stated.. To highlight
informative sentences, many existing works introduce the attention mechanism
to assign sentences with different learning weights [7].

In terms of quantity, on the other hand, most of the training data collected
by distant supervision concentrate mainly on a few relations, leading to the
issue of the lack of sufficient annotations for the remaining relations. Take
the widely used dataset, New York Times (NYT) [9], as an example, we present
the number of training instances of each relation in Figure 1b.
Unsurprisingly, the annotations are long-tail concerning different relations,
and the tail relations suffer from insufficient training corpora. More
specifically, each relation $r_{i}$ refers to multiple entity pairs
$(e_{h},e_{t})$, and the sentences for each entity pair also have long-tail
distributions (Figure 1c). That is, only a small portion of entities
frequently co-occur in sentences, then we can collect sufficient training
data. Most entities rarely co-occur or are even unseen for the corresponding
relations, which exacerbates the long-tail issue in relation extraction.

(a)  (b)  (c)

Figure 1: Illustration of relations, training sentences, and their long-tail
distributions in NYT. We present relation hierarchy as the widely used prior
knowledge for long-tail RE, where dashed lines denote a distance measurement
(Lowest Common Ancestor) between two relation types. We highlight similar
textual patterns with underlines between proximate relations. We denote
entities in sentences with bold fonts.

In this paper, we propose to improve relation extraction by learning
prototypes for each relation type, which can be utilized to transfer knowledge
from the relations with sufficient training data to the long-tail relations.
As shown in Figure 1a, the long-tail relation /location/fr_region/capital is
semantically similar to the relation /location/us_state/capital, which has 798
training instances. They share a common textual pattern in sentences (e.g., is
the capital of), and thus we can utilize such relation proximity for transfer
learning.

A major challenge lies in correctly identifying the proximity among relations;
otherwise, the knowledge transfer between irrelevant relations will bring in
noise and mislead the model training. Existing works [10, 8] introduce the
hierarchies of relations as prior knowledge. They assume that the smaller the
structural distance between two nodes (e.g., Lowest Common Ancestor in Figure
1a) is, the similar the relations are. However, such a prior hierarchy is not
always available and sometimes incorrect. For instance, although the distance
between $(r_{3},r_{4})$ is smaller than that between $(r_{1},r_{2})$,
$(r_{1},r_{2})$ should be more similar with respect to RE prediction
distributions because of the more common textual contexts. Therefore, how to
capture relation proximity in a more precise and general way remains
challenging.

Another major challenge is to distinguish between different relations, in case
the knowledge transfer introduces a bias towards the same prediction for
proximate relations. For example, as mentioned above, both
/location/us_state/capital and /location/fr_region/capital indicate the
capital relation, and the only difference is that between two United States
entities or French entities. DPEN [11] incorporates entity type information to
learn relation-specific classifier dynamically. However, entity type
information is sparse in KGs (nearly 40% entities in DBPedia [3] do not have
any type), challenging the scalability.

To address the first issue, we propose to learn relation prototypes that
capture the proximity relationship among relations from involved entity pairs.
Inspired by Prototypical Networks [12], we represent each relation prototype
with the centroid of its training data, and each data point is defined as the
difference between the pair of entity embeddings, namely implicit mutual
relation (instance). Given any entity pair, we compute the implicit mutual
relation and its distance to each relation prototype. These proximities
suggest possible relations to the classifier, which further makes correct
predictions by extracting discriminative signals from supportive sentences.
Relation prototypes can also be enhanced by prior information (i.e., relation
hierarchy and entity types), and be applied to arbitrary sentence encoder.

To address the second issue, we enhance entity embeddings with textual
information for implicit mutual relation learning. In specific, we construct
an entity co-occurrence graph from unlabeled texts and modeling both the
first-order and second-order structural proximity. The massive textual
contexts are helpful to infer entity types for distinguishment. Besides, long-
tail entity pairs can also benefit from additional textual information. We
summarize our main contributions as follows:

  * •

We highlight the importance of considering long-tail distributions of both
relation types and their referred entity pairs (i.e., relation instances).

  * •

We propose a novel model that learns relation prototypes from unlabeled texts,
to improve long-tail relation extraction by transferring knowledge from
relations with sufficient training instances. The learned prototypes can be
applied to almost arbitrary RE models.

  * •

We have conducted extensive experiments on two publicly available datasets,
and compared them with eight state-of-the-art baseline methods. We have
analyzed the impacts of relation prototypes, prior hierarchy, entity types,
and implicit mutual relations, especially on long-tail settings. The results
demonstrate the effectiveness of our proposed method.

A preliminary version of this work has been published in the conference of
ICDE 2020 [1]. We summarize the main changes as follows:

  * •

Introduction (Section 1). We have reconstructed the abstract and introduction
to highlight the motivations of the extended version.

  * •

Methods (Section 3). We propose a novel relation prototype learning framework
that extends the preliminary model to alleviate the long-tail issue in RE.

  * •

Experiment (Section 4). We add experiments to verify the new model on long-
tail settings compared with another two baselines. We further explore the
learned prototypes via ablation study and case study to justify the
effectiveness and generalization ability.

  * •

Preliminaries (Section 2) and Related Work (Section 5). The two sections are
reconstructed to make the paper more complete and self-contained.

Figure 2: Framework. There are five main components: Relation Prototype
Learning, Implicit Mutual Relations Modeling, Incorporation of Entity Type,
Sentence Neural Encoder, and Classification Layer. We denote the same type of
embeddings with the same color. Each relation prototype is the centroid
(yellow star) of the implicit mutual relations (green circles) between
referred pairs of entities. The components with dashed rectangles are
optional. We omit the co-occurrence times in the graph for clarity.

##  2 PRELIMINARY

In this section, we first formulate the task and necessary notations, and
overview our framework.

###  2.1 Notations

Knowledge Graph is a directed graph
$\mathcal{G}=(\mathcal{E},\mathcal{R},\mathcal{T})$ consisting of a set of
entities $\mathcal{E}=\\{e\\}$, a set of relations $\mathcal{R}=\\{r\\}$ and a
set of triples $\mathcal{T}=\\{e_{h},r_{i},e_{t}\\}$ that denote factual
relationship between a pair of entities. Entities may have type information
$c$ in the KG, such as person and location. Following conventions, we use
bold-face letters to denote the embeddings of corresponding terms. For
example, $\mathbf{e}$ is the embedding vector of entity $e$.

Implicit Mutual Relation $MR$ aims to reflect implicit relations between two
entities. We thus define it as the distance between an entity pair in the
vector space. This is inspired by analogy semantics of Word2vec [13], the
entity pairs with the same relation type should have similar implicit mutual
relations (e.g., $MR_{1}=vec(Phoenix)-vec(Arizona)\approx
MR_{2}=vec(Columbus)-vec(Ohio)$).

Relation Prototype $RP$ is learned for each relation type. They define a
metric space, in which the distance between two prototypes refects their
relation proximity. Different from implicit mutual relation, which is defined
on each entity pair, each relation prototype refers to one relation type that
may consist of multiple entity pairs.

Relation Extraction aims to classify the relation $r^{*}$ between two entities
$(e_{h},e_{t})$ within a predefined set $R$, given a set of training sentences
$S=\\{s_{1},s_{2},\cdots,s_{n}\\}$, where each sentence $s_{i}$ offers
boundaries of entities. As shown in Figure 1a, given the top two sentences and
entity mentions (bold fonts), RE aims to predict the relation type $r_{1}$.

###  2.2 Framework

As illustrated in Figure 2, our proposed method consists of five main
components. Note that the prior relation hierarchy and entity types are
optional (dashed rectangles).

  * •

Relation Prototype Learning: Instead of heavily relying on prior hierarchy, we
learn relation prototypes to reflect its meaning as well as the relationships
with other relations. We achieve this based on implicit mutual relations. If
the prior hierarchy is available, relations in different layers will have
their own prototypes.

  * •

Implicit Mutual Relation Modeling: Implicit mutual relations capture analogy
semantics between entity pairs. We achieve this by (1) constructing an entity
co-occurrence graph from unlabeled texts, and (2) modeling the structural
proximity for entity embedding learning. MRs are also utilized to compute
similarity with prototypes to estimate a preliminary relation for RE
classifier.

  * •

Incorporation of Entity Type: Entity types are commonly used to filter
impossible relations between two entities. For example, Obama is person
entity, Hawaii is a location, and their relation cannot be child of. We learn
type representations and feed them into the classifier, and other side
information can also be incorporated in this way.

  * •

Sentence Neural Encoder: RE models usually utilize a neural encoder to capture
textual context information. This component is not our primary focus, so we
adopt a widely used PCNN with attention to mitigate the negative impacts of
noise. We also try different encoders to verify the generalization ability.

  * •

Classification Layer: We introduce a classification layer to integrate the
above four types of information and output a confidence score for each
relation, indicating how possible the given entity pair and coupled sentences
have the relation.

##  3 METHODOLOGY

For knowledge transfer, we learn prototypes for each relation type based on
implicit mutual relation between each involved entity pair. We represent
entity by pre-training their embeddings to capture both the first-order and
second-order proximity over an entity co-occurrence graph, which is
constructed using unlabeled texts only. Thus, the long-tail relations will
benefit from their proximate relations with sufficient training corpora, and
the infrequent or even unseen entity pairs will benefit from the large-scale
textual information, leading to superior relation extraction performance. In
this section, we describe each component in detail.

###  3.1 Relation Prototype Learning

Relation Prototypes can capture relation type proximity and avoid the heavy
reliance on prior hierarchy. Inspired by Prototypical Networks [12], we learn
prototypes as centroids of training data, and data points are defined by
implicit mutual relations between referred entity pairs:

$RP_{i}=\frac{1}{|\mathcal{T}_{i}|}\sum_{(e_{h},r_{i},e_{t})\in\mathcal{T}_{i}}MR_{h,t}$
(1)

where $RP_{i}$ is the prototype for relation $r_{i}$. We define
$\mathcal{T}_{i}=\\{(e_{h},r_{i},e_{t})|e_{h},e_{t}\in
E,(e_{h},r_{i},e_{t})\in\mathcal{T}\\}$ as the triples with relation $r_{i}$,
and $|\cdot|$ is the total number of the set. $MR_{h,t}$ is the implicit
mutual relation between entities $e_{h}$ and $e_{t}$.

Typically, similar relations have similar implicit mutual relations, and the
prototypes will be close in the vector space. Also, we can enhance the learned
relation proximity with prior hierarchy if available. Assuming that the
hierarchy has $K$ layers, we learn each node with a prototype:

$RP_{i}^{k}=\frac{1}{|\downarrow(r_{i}^{k})|}\sum_{r_{j}^{k+1}\in\downarrow(r_{i}^{k})}RP_{j}^{k+1}$
(2)

where $r_{i}^{k}$ denotes the $i$th node in the $k$th layer, and
$\downarrow(r_{i}^{k})$ denotes the set of children nodes. Note that all the
prototypes for the leaf nodes $RP_{i}^{K}=RP_{i}$ can be computed according to
Equation 1. Thus, we can utilize the learned prototypes for transfer learning
in Section 3.5.

###  3.2 Implicit Mutual Relations Modeling

Implicit Mutual Relation aims to reflect the analogy semantics between entity
pairs, such that $MR_{1}=vec(Phoenix)-vec(Arizona)\approx
MR_{2}=vec(Columbus)-vec(Ohio)$, where these two pairs of entities have the
same relation /location/us_state/capital. Meanwhile, the entity pair (Fort-de-
France, Martinique) with a similar relation /location/fr_region/capital has a
similar implicit mutual relation $MR^{\prime}$. All of them reflect the
capital relationship and contribute common semantics to prototype learning.

Inspired by Word2vec [13], we achieve this by capturing the co-occurrence
among entities in texts. There are three stages for implicit mutual relations
modeling. (1) We construct an entity co-occurrence graph based on the co-
occurrence frequency of each entity pair. (2) Then, the entity representations
are learned based on the entity co-occurrence graph. (3) We obtain the
implicit mutual relations of any entity pair based on their representations.

####  3.2.1 Entity co-occurrence graph construction

The goal of entity co-occurrence graph is that entities in the graph play a
similar role as the words in texts, which claim that words with similar
contexts shall have similar embeddings. That is, entities with similar
topological structures will have similar semantics. For example, as
illustrated in Figure 3 (for clarity, we have omitted some unimportant points
and edges), there are directed edges between entities Houston and Dallas. They
are similar in semantics, and such similarity can be simply evaluated by the
number of common neighbors between these two entities in the graph.

Figure 3: The similar topological structure of entities Houston and Dallas,
both which serve as locations to some relation (e.g., BornIn).

To construct the graph, we utilize large scale unlabeled corpora, such as
Wikipedia corpora, TIME magazine, and Google News. We employ exact string
matching to identify entities from the unlabeled corpora, and then count the
co-occurrences of entity pairs. For example, entities Obama and Hawaii exist
in the same sentence “Obama was born in Honolulu, Hawaii.”, then the co-
occurrence time of Obama and Hawaii will increase 1.

Each entity is a vertex in the entity co-occurrence graph, and each edge is
assigned with a weight as follows:

$w_{i,j}=\frac{\log{(co_{i,j})}}{\log{(\max_{(e_{k},r,e_{l})\in\mathcal{T}}\\{co_{k,l}\\})}},$
(3)

where the value of $co_{i,j}$ denotes the co-occurrence number of entity pair
$(e_{i},e_{j})$, and $\max_{(e_{k},r,e_{l})\in\mathcal{T}}\\{co_{k,l}\\}$
denotes max co-occurrence number among all entity pairs. To reduce the
computation complexity, we remove the entity pairs that co-occur below a
manually defined threshold. In experiments, we have tried $\\{10,20,30\\}$ and
found similar results. Thus, we set the thresholds to $10$ and $100$ for GDS
and NYT datasets, leading to 15270(121218) and 69040(2085206)
entities(relations), respectively. In the weighted graph, two vertices with
similar topological structures indicate that the corresponding entities have
similar semantics in unlabeled corpora. Next, we learn entity embeddings by
preserving the structure information.

####  3.2.2 Learning entity embedding

The entity embeddings can be pretrained offline, which will not decrease RE
efficiency. Here, we follow a widely used network embedding approach [14] due
to its effectiveness yet simplicity. More fancy models may improve embedding
quality and RE performance. We do not explore them, because the goal of this
paper is to provide a general framework for long-tail RE and the superior
network embedding methods are out of the scope. We consider the graph
structure with first-order and second-order proximity. To be specific, first-
order proximity is defined to capture observed links in the graph, and the
second-order proximity aims to capture the contextual proximity between
entities.

First-order proximity: A superior way to preserve the first-order proximity is
to minimize the distance between the joint probability of entity pair and its
empirical probability. Following Line [14], KL-divergence is chosen as
distance measurement to minimize these two distributions. The final objective
function is as follows:

$O_{1}=-\sum_{e_{i},e_{j}\in\mathcal{E}}w_{i,j}\cdot\log{P_{1}(e_{i},e_{j})},$
(4)

where the $w_{i,j}$ denotes the weight of edge between entities $e_{i}$ and
$e_{j}$ defined in equation (3), and $P_{1}(e_{i},e_{j})$ denotes the joint
probability between $e_{i}$ and $e_{j}$, which is defined as:

$P_{1}(e_{i},e_{j})=\frac{1}{1+exp(-\textbf{e}_{i}^{T}\cdot\textbf{e}_{j})},$
(5)

where $\textbf{e}_{i}\in\mathbb{R}^{d1}$ is the vector representation of
entity $e_{i}$ in the $d1$-dimensional embedding space.

Second-order proximity: We assume that vertices with many shared neighbors are
similar to each other, called the second-order proximity. To preserve it, for
each directed edge between $e_{i}$ and $e_{j}$ in the graph, we minimize the
distance between the probability of context $e_{j}$ generated by vertex
$e_{i}$ and its empirical probability. Similarly, when the KL-divergence is
chosen, the objective function is as follows:

$O_{2}=-\sum_{e_{i},e_{j}\in\mathcal{E}}w_{i,j}\cdot\log{P_{2}(e_{j}|e_{i})},$
(6)

where $P_{2}(e_{j}|e_{i})$ is the probability of “context” $e_{j}$ generated
by vertex $e_{i}$, which is defined as:

$P_{2}(e_{j}|e_{i})=\frac{\exp{(\textbf{e}_{j}^{T}\cdot\textbf{e}_{i})}}{\sum_{k=1}^{|\mathcal{E}|}{\exp{(\textbf{e}_{k}^{T}\cdot\textbf{e}_{i})}}},$
(7)

where $|\mathcal{E}|$ denotes the total amount of vertices.

In practice, computation of the conditional probability $P_{2}(e_{j}|e_{i})$
is extremely expensive. A simple and effective way is to adopt the negative
sampling approach mentioned in [14]. Thus, the above objective function can be
simplified:

$O_{2}=\log{\sigma(\textbf{e}_{j}^{T}\cdot\textbf{e}_{i})}+\sum_{i=1}^{K}E_{e_{n}\sim
N(e_{i})}[\log{\sigma(-\textbf{e}_{n}^{T}\cdot\textbf{e}_{i})}],$ (8)

where $N(e)$ denotes the neighbors of entity $e$ in the graph,
$\sigma(x)=1/(1+exp(-x))$ is the sigmoid function, and $K$ is the number of
negative edges. The first term models the observed links, and the second term
models the negative links drawn from the noise distribution.

To embed the vertices in the proximity graph, we preserve both the first-order
proximity and second-order proximity separately, then obtain the embedding
vector for a vertex by concatenating corresponding embedding vectors learned
from the two models.

####  3.2.3 Implicit mutual relation

Considering that $vec(Phoenix)-vec(Arizona)\approx vec(Columbus)-vec(Ohio)$,
we represent the implicit mutual relation between entities $e_{i}$ and $e_{j}$
as follows:

$MR_{i,j}=\textbf{e}_{j}-\textbf{e}_{i},$ (9)

###  3.3 Incorporation of Entity Type

We aim to propose a general way to deal with long-tail relation extraction,
which can easily incorporate other side information. Here, we take entity type
as an example, which is optional in our proposed model. In intuition, entity
types are beneficial to predict the relation. For example,
/people/person/place_of_birth is the relation between Location and Person,
rather than Person and Person. Existing works [15, 16, 17] have also shown
that entity type information plays a positive role in relation extraction.

Following existing works [18], entity types are extracted from KG and further
mapped to FIGER [15], while our proposed method is flexible in arbitrary
entity type information. FIGER defines 112 fine-grained entity types. To avoid
over-parameterization, we employ 38 coarse types in the first hierarchy in
FIGER. Each entity type is embedded into $d2$ dimensional space to get the
embedding vector of an entity type. When an entity has multiple types, we take
the average over the embedding vectors.

We concatenate the type embeddings for a target entity pair $(e_{i},e_{j})$ as
follows:

$\mathbf{c}_{i,j}=[\mathbf{c}_{i}||\mathbf{c}_{j}],$ (10)

where $\mathbf{c}_{i}$ is the type embedding for entity $e_{i}$, and
$[\cdot||\cdot]$ denotes the concatenation operation.

###  3.4 Sentence Neural Encoder

To encode the supportive sentences, we introduce the widely used Piecewise CNN
(PCNN) with sentence-level attention [7] as our neural encoder, although our
proposed model can be applied with almost arbitrary encoders. The encoder can
represent multiple sentences with a single fixed-length embedding, and
highlight the informative sentence with higher attention weights. This
component consists of three indispensable steps:

  * (1)

Sentence Embedding: Each sentence $s_{i}$ in a training sentence bag
$S=\\{s_{1},s_{2},\cdots,s_{n}\\}$ should be represented by word embedding and
relative position embedding, which means the relative position of all words in
the sentence to the target entities.

  * (2)

Sentence Encoding: As the previous works [19, 7] shown, the convolutional
neural networks with piecewise max-pooling (PCNN) is a fast and effective way
to encode the sentence. Consequently, we get the encoding of each sentence
using PCNN.

  * (3)

Sentence-Level Attention: The distant supervision suffers from noisy labels,
i.e., not all sentences in a bag can express the target relation for the given
entity pair. To address this issue, we utilize sentence-level attention to
mitigate effects from noise sentences. To encode the bag of sentences, the
model gives each sentence a score indicating the probability of expressing the
relation.

The encoding of the $i$th sentence bag can be represented:

$\mathbf{X}_{bag_{i}}=\sum_{j\in bag_{i}}\alpha_{j}\mathbf{x}_{j},$ (11)

where $X_{bag_{i}}$ denotes the bag formed by all training sentences of the
$i$th entity pair, and $\mathbf{x}_{j}$ is the embedding of sentence $s_{j}$.
The score $\alpha_{j}$ for sentence $s_{j}$ is calculated by the selective
sentence attention:

$\alpha_{j}=\frac{\exp\left(q_{j}\right)}{\sum_{k}\exp\left(q_{k}\right)},$
(12)

where $q_{j}$ is a query-based function which scores how well the sentence
$s_{j}$ and the predict relation $r$ matches. We use the bi-linear function to
calculate the scores:

$q_{j}=\mathbf{x}_{j}\mathbf{A}\mathbf{r},$ (13)

where $\mathbf{A}$ is a weighted diagonal matrix, and $\mathbf{r}$ is the
query vector associated with relation $r$.

###  3.5 Classification Layer

Given a bag of sentences, relation prototypes, entity pairs $(e_{h},e_{t})$
and their types, the classification layer first takes each output of the above
components as inputs, then outputs confident scores over predefined relation
set $\\{r_{1},r_{2},\cdots,r_{m}\\}$. For inference, we choose the relation
with the highest probability as the final prediction. For training, we choose
cross-entropy loss:

$\mathcal{L}=-\sum\hat{y}logP(r|RP,e_{h},e_{t},S,c_{h},c_{t})$ (14)

where $\hat{y}$ is the groundtruth, and the probability distribution over $m$
relations between entities $e_{h}$ and $e_{t}$ can be computed:

$P(r|RP,e_{h},e_{t},S,c_{h},c_{t})=\sigma(W(\alpha C_{PR}+\beta
C_{Type}+\gamma C_{RE})+b)$ (15)

where $\alpha$, $\beta$ and $\gamma$ are learnable weights of three following
components $C_{RP}$, $C_{Type}$, and $C_{RE}$. In experiments, the ratio of
them is around $1.5:1:0.6$ on average, which indicates the importance of our
proposed relation prototypes as well as the effectiveness of the type
information. In specific, first, we compute the distance from the implicit
mutual relation of $(e_{h},e_{t})$ to each relation prototype, to suggest
possible relations that reflect the proximity for transfer learning.

$C_{RP^{k}}=\sigma([d(MR_{h,t},RP_{1}^{k})||\cdots||d(MR_{h,t},RP_{mk}^{k})))$
(16)

where $\sigma$ is the softmax function, $mk$ is the number of relations in the
$k$th layer if the prior relation hierarchy is avaiable, otherwise $k=1$
indicates there is only the predefined relation set, and $r_{mk}=r_{m}$.
$d(\cdot,\cdot)$ is the distance measurement, and we use L2 distance in
experiments. We concatenate $C_{RP^{k}}$ for different layers as the final
prototype-based features $C_{RP}$. Thus, proximate relations will have similar
features. During training, this will regularize the prediction distribution of
long-tail relations similar to their proximate relations with sufficient
training data, thereby transferring knowledge for better performance.

For entity types, we concatenate the type embeddings of a target entity pair
and then use a fully connected layer with a Softmax to calculate the
confidence score:

$C_{Type}=\sigma(W^{c}\mathbf{c}_{h,t}+b^{c}),$ (17)

where the $W^{c}$ and $b^{c}$ are trainable parameters.

For the sentence bag, we calculate the confidence score:

$C_{RE}=\sigma(W^{RE}\mathbf{X}_{bag}+b^{RE}),$ (18)

where $W^{RE}$ and $b^{RE}$ are trainable parameters. This component can be
replaced by any RE model.

###  3.6 Discussion

The relation prototypes can flexibly combine with various RE models as well as
side information. We integrate them with typical PCNN with attention and the
type information as described above. In experiments, we will verify the
effectiveness of the prototypes using other CNN-based and RNN-based RE models,
which present positive effects.

Instead of the given entity graph in existing KGs, such as Wikipedia
hyperlinks and YAGO [2], we capture the entity co-occurrence from texts,
because (1) under the opened-world assumption, existing KGs are far from
complete and may not cover all relevant information about entities; and the
missing links and multi-relational data may bring noise; (2) we are inspired
by Word2vec [13], which presents the expect analogy nature by modeling co-
occurrence; and (3) although TransE [20] can also model the relation between
entities as a translation operation in the vector space, similar to Word2vec,
the explicitly modeled relation $r$ may have a different distribution with
that in texts, which is the target source of RE. Nevertheless, we are
interested in incorporating KG as side information in the future.

##  4 EXPERIMENTS

In this section, we conduct comprehensive experiments to evaluate the
performance of our proposed approach by comparing it with eight SOTA systems
and six variants of our approach on two public datasets. Through empirical
study, we aim at addressing the following research questions:

  * •

RQ1: How does our proposed approach perform compared with state-of-the-art RE
approaches?

  * •

RQ2: How do the relation prototypes and implicit mutual relations affect the
RE model?

  * •

RQ3: Could the relation prototypes and implicit mutual relations improve the
performance of existing RE methods, such as GRU, PCNN, and PCNN + ATT?

Besides, we conduct a case study, which qualitatively demonstrates the
effectiveness of our proposed method.

TABLE I: The statistic on NYT and GDS datasets.

Datasets |  | NYT
---
(#Relations: 53)
| GDS
---
(#Relations: 5)
| #Sentences | #EntityPair | #Sentences | #EntityPair
Training | 522,611 | 281,270 | 13,161 | 7,580
Testing | 172,448 | 96,678 | 5,663 | 3,247

###  4.1 Experimental Settings

####  4.1.1 Datasets

(a) PR curve on NYT dataset

(b) PR curve on GDS dataset

Figure 4: The Precicion-Recall curves of six baseline methods as well as two
variants of our methods on NYT and GDS datasets.

We adopt two widely used public datasets to demonstrate the effectiveness of
our method and baselines. They are New York Times (NYT) [9] and Google Distant
Supervision (GDS) [21] datasets. The statistical descriptions of them are
illustrated in Table I.

  * •

NYT dataset is generated by annotating entities with Stanford NER tool in the
New York Times corpus, which is then aligned with Freebase to get the relation
between entities. The training samples are from the corpus of the years
2005-2006, and the testing samples are from the corpus of the year 2007. There
are 53 different relations, including a relation NA that indicates there is no
relation between two entities.

  * •

GDS dataset is an extension of the manually annotated data set Google relation
extraction corpus. The entities in Google relation extraction corpus are
aligned with web documents to obtain new sentences, which also contain
targeted entities. There are five different relations, including a relation
NA.

To learn implicit mutual relations, we utilize Wikipedia articles as textual
corpora to construct the entity proximity graph, since it thoroughly covers
the entities existed in both NYT and GDS datasets.

####  4.1.2 Evaluation metrics

Similar to most existing works, we evaluate our model with the held-out
metrics, which compare the predicting relation facts from the test sentences
with those in Freebase. We report the precision, recall, F1-score, precision
at top $N$ prediction (P@N), and AUC (area under the Precision-Recall curve).
For different thresholds, the precision and recall are different, so we report
the precision and recall at the point of max F1-score. In addition, we compute
the average score for each metric after running the same experiment five
times.

####  4.1.3 Parameter settings

We use grid search to tune the optimal model parameters. The grid search
approach is used to select the learning rate $\lambda$ for stochastic gradient
descent optimizer among $\\{0.1,0.2,0.3,0.4,0.5\\}$, the sliding window size
$l$ of CNN among $\\{1,2,3,4,5\\}$, the number of filters $k$ of CNN among
$\\{180,200,230,250,300\\}$, and the size of entity type embedding $d2$ among
$\\{10,15,20,25,30,40\\}$. For the entity embedding size, we follow the
setting of [14]. Table II shows the optimal parameters used in the
experiments.

TABLE II: Parameter settings Parameter Description | Value
---|---
Relation prototype embedding size | 128
Entity type embedding size | 20
Window size | 3
CNN filters number | 230
POS embedding dimension | 5
Word embedding dimension | 50
Learning rate | 0.3
Sentence max length | 120
Dropout probability | 0.5
Batch size | 160
TABLE III: Overall performance on NYT and GDS datasets. Dataset | Method | AUC | Precision | Recall | F1-Score | P@100 | P@200
---|---|---|---|---|---|---|---
NYT | PCNN | 0.3296 | 0.3830 | 0.4020 | 0.3923 | 0.77 | 0.72
PCNN+ATT | 0.3424 | 0.3588 | 0.4564 | 0.4018 | 0.75 | 0.75
BGWA | 0.3670 | 0.3994 | 0.4451 | 0.4210 | 0.76 | 0.74
CNN+RL | 0.3735 | 0.4201 | 0.4389 | 0.4293 | 0.79 | 0.73
DSGAN | 0.3801 | 0.4251 | 0.4591 | 0.4414 | 0.80 | 0.78
PCNN+HATT | 0.3857 | 0.4313 | 0.4476 | 0.4393 | 0.80 | 0.79
PA-TMR | 0.3939 | 0.4320 | 0.4615 | 0.4463 | 0.83 | 0.79
PA-TRP | 0.4371 | 0.4291 | 0.4995 | 0.4616 | 0.88 | 0.82
GDS | PCNN | 0.7798 | 0.6804 | 0.8673 | 0.7626 | 0.88 | 0.90
PCNN+ATT | 0.8034 | 0.7250 | 0.8474 | 0.7814 | 0.94 | 0.93
BGWA | 0.8148 | 0.7725 | 0.7162 | 0.8385 | 0.99 | 0.98
CNN+RL | 0.8554 | 0.7680 | 0.9132 | 0.8343 | 1.00 | 0.96
DSGAN | 0.8445 | 0.7526 | 0.9115 | 0.8245 | 0.99 | 0.97
PCNN-HATT | 0.8540 | 0.7728 | 0.8979 | 0.8307 | 0.99 | 0.97
PA-TMR | 0.8646 | 0.8058 | 0.8641 | 0.8339 | 1.00 | 0.98
PA-TRP | 0.8725 | 0.7964 | 0.9553 | 0.8686 | 1.00 | 0.98

####  4.1.4 Baselines

We compare with the following baselines:

BGWA [21] is a bidirectional GRU based relation extraction model. It focuses
on reducing the noise from distant supervision by using hierarchical
attentions.

PCNN [19] is a CNN based relation extraction model which utilizes the
piecewise max pooling to replace the single max pooling to capture the
structural information between two entities.

PCNN+ATT [7] combines the selective attention over instances with PCNN, which
is expected to dynamically reduce the weights of those noisy instances,
thereby reducing the influence of wrong labeled instances.

CNN+RL [22] contains two modules: an instance selector and a relation
classifier. The instance selector chooses high-quality sentences with
reinforcement learning. The relation classifier makes a prediction by the
chosen sentences and provides rewards to the instance selector.

DSGAN [23] utilizes an adversarial learning framework to learn a sentence
level true-positive generator. The generator is used to filter the noise in
the distant supervision dataset, in which way to obtain a cleaned dataset.
Then the cleaned dataset is used to train a RE model. In their paper, the best
results are produced by PCNN+ATT.

PCNN+HATT [10] improves PCNN+ATT by utilizing prior relation hierarchy, which
provides the proximity information for transfer learning. It computes
selective attention within each layer in the hierarchy, and concatenates all
of the layers for final classification.

PCNN+KATT [8] improves PCNN+HATT by utilizing GCN to model relation hierarchy
and introducing pretrained KG embeddings as external knowledge.

DPEN [11] dynamically learns relation-specific classifier, which utilizes the
entity types and relation classes to generate the classification parameters.

We carefully implement all the above baselines for our empirical study except
for PCNN+KATT and DPEN, because we cannot find their released codes. We thus
compare with them on the old-version NYT dataset in the long-tail settings, as
they reported such performance in the original papers. For other methods, we
have fairly tuned the hyper-parameters, and use the optimal hyper-parameter
setting to report the average performance of five runs. Compared to the
original papers, their performance of our implementation are not worse.

We also investigate several variants of our proposed method PA-TRP (PCNN-ATT +
Type + Relation Prototypes), to provide insights on the impacts of each main
component. PA-TMR (PCNN-ATT + Type + implicit Mutual Relation) directly
utilizes implicit Mutual Relation as entity pair representations, without the
learning relation prototypes, which is proposed in the previous paper [1].
PA-T only adopts the type information, PA-MR only adopts implicit mutual
relation, and PA-RP learns relation prototypes without the incorporation of
entity types. In addition, to verify the effect of relation hierarchy, we
remove the prior information in PA-TRP/h.

###  4.2 Performance Comparison (RQ1)

Figure 4 and Table III show the overall performance of our models as well as
the baseline methods on both NYT and GDS datasets. First, from the precision-
recall curves in Figure 4, we can see that NYT is a more challenging dataset
than GDS, and the testing performance is much more stable on NYT than that on
GDS. This accords with the statistics in Table I that NYT has more relations
than GDS to predict ($53$ v.s. $5$), and both the training and test sets are
at large scale. We have the following key observations:

(a) NYT dataset

(b) GDS dataset

Figure 5: F1-scores with respect to different co-occurrences of entity pair in
Wikipedia.

  * •

Focusing on NYT, both of our models PA-TMR and PA-TRP outperform all of the
other baseline models, mainly because the implicit mutual relations enhance
the representations of entity pairs by introducing external text corpora. This
helps our model generalize to infrequent or even unseen cases. Compared to PA-
TMR, PA-TRP stably performs better, because it further learns the relation
prototypes from the referring entity pairs and improves the long-tail relation
extraction by transferring knowledge from the proximate relations with
sufficient training data.

  * •

For GDS, the performance of all the models is fluctuating and unstable.
Specifically, when the recall is lower than $0.3$, BGWA and PCNN+HATT achieve
the best precisions. The possible reason is that their hierarchical attention
mechanism can more efficiently filter out the noisy sentences from the bag,
and thus their precisions become higher. When the recall is larger than $0.3$,
PA-TMR and PA-TRP outperform other baseline models, mainly because the
unlabeled textual information largely benefits recall, while introducing
additional noise may decrease precision. PA-TRP is more robust to precision
drops when the recall becomes higher (i.e., larger than $0.8$) by capturing
the commonality of each relation in their prototypes.

Table III further demonstrates the above observations with detailed scores.
The P@100 and P@200 on the relatively easy dataset GDS almost reach $1.0$, so
we pay more attention to the performance on the NYT dataset than that on GDS
in the following analysis and ablation studies. We can see:

  * •

Our model PA-TRP achieves the best F1-score with $4.6$% and $3.6$% performance
gains over the second-best models on NYT and GDS, respectively. Particularly,
the improvements come from promising recall and competitive precision.
Notably, it is demonstrated effective in improving the precision via some
advanced mechanisms, such as by filtering out noisy sentences, or by selecting
the informative data in the bag. We can see the gradually increasing
precisions of the hierarchical attention mechanism in BGWA, the reinforcement
learning in CNN-RL, the adversarial training in DSGAN, and the prior relation
hierarchy in PCNN-HATT. Moreover, our proposed relation prototypes can be
integrated into different RE models for better performance. We leave it in the
future, and this paper focuses on the general approach of learning relation
prototypes for long-tail relation extraction.

  * •

It is also worth pointing out that the precision of PA-TMR consistently
outperforms PA-TRP, mainly because PA-TRP does not take the implicit mutual
relation between a pair of entities as direct input to the classification
layer, which clearly can provide more discriminative signals between proximate
relations and benefit the precise classification.

###  4.3 Long-tail Relation Extraction (RQ2)

In this section, we further study the capability of long-tail relation
extraction of our model to investigate the effect of relation prototypes. For
fair comparisons, the experiments are conducted on the old version of NYT,
because the state-of-the-art baseline models [8, 18] only report the
performance on this dataset, and we cannot find their codes for
reimplementation. The old NYT has about another 50,000 training sentences, and
the entity pairs in these sentences have overlap with the test dataset.
Following baselines, we evaluate the performance of long-tail relations with
training instances fewer than $100/200$, and utilize macro top $K$ hit ratio
accuracy (Hits@K) as the measurement.

TABLE IV: Overall performance for long-tail relation extraction on NYT.  #Instances | <100 | <200
---|---|---
Hits@K | 10 | 15 | 20 | 10 | 15 | 20
PCNN+ATT | <5.0 | 7.4 | 40.7 | 17.2 | 24.2 | 51.5
PCNN+HATT | 29.6 | 51.9 | 61.1 | 41.4 | 60.6 | 68.2
PCNN+KATT | 35.3 | 62.4 | 65.1 | 43.2 | 61.3 | 69.2
DPEN | 57.6 | 62.1 | 66.7 | 64.1 | 68.0 | 71.8
PA-RP | 62.0 | 70.3 | 70.3 | 65.1 | 72.3 | 72.3
PA-TRP | 63.9 | 70.3 | 72.2 | 66.7 | 72.3 | 73.8

Table IV shows the overall performance. We can see: (1) the long-tail issue of
relation extraction is severe, as the widely used RE model PCNN+ATT has less
5% Hits@10 accuracy for the relations with less than 100 training instances.
(2) Prior relation hierarchy (i.e., PCNN+HATT) can effectively boost the long-
tail relation extraction, which benefits from the shared training instances of
relations under the same branch, and the incorporation of external KG
structures (PCNN+KATT) and entity type information (DPEN) further improve the
performance. Especially, the significant improvements on Hits@10 of DPEN
demonstrate the importance of entity types. (3) We thus report the performance
of both our model and the variant without using type information PA-RP. Both
of them achieve satisfactory scores, which demonstrate the effectiveness of
our model on addressing the long-tail issue with a general framework of
relation prototypes. We will conduct further ablation study by replacing our
basic model with other RE models, to verify its generalization ability in
Section 4.5.

###  4.4 The Effect of Implicit Mutual Relations (RQ2)

In this section, we will further investigate the effectiveness of the implicit
mutual relations for dealing with the long-tail issue of entity pairs. We
consider the settings of entity pair frequency from two aspects. First, we
count them on the introduced unlabeled text corpora and evaluate the
performance of our method. Second, we count the co-occurrence frequencies of
entity pairs on training data only, aiming to reflect the effect of implicit
mutual relations when there is no insufficient training data.

(a) NYT dataset

(b) GDS dataset

Figure 6: F1-scores with respect to the entity pairs with different co-
occurrences in original datasets.

####  4.4.1 Improvement from implicit mutual relations

As illustrated in Figure 5, we sort entity pairs in ascending order according
to their co-occurrences in the unlabeled corpora (Wikipedia). We then evaluate
the performance for entity pairs with different co-occurrences, where the
x-axis denotes the quantile of co-occurrence times of entity pairs in
Wikipedia, and the y-axis denotes the corresponding F1-score. We have the
following key observations:

  * •

As the co-occurrences of entity pairs increase, F1-score demonstrates an
upwards synchronous trend. It reveals that no matter frequent or infrequent
entity pairs in the corpora help improve the performance of our PA-TMR and PA-
TRP models. This points to the positive effect of all implicit mutual
relations collected from the unlabeled corpora. Meanwhile, the implicit mutual
relations, which capture the semantic information of both the target entity
pair and the entity pairs with similar semantic, contribute to predicting
relations for the target entity pair.

  * •

The improvement on the small dataset GDS is much larger than that on NYT
dataset. This is due to the fact that: (1) we insufficiently train the
original RE model in the smaller dataset; (2) noisy data in a smaller training
dataset exacerbates the inadequate issue of the training process by utilizing
the attention mechanism. The better improvement illustrates that implicit
mutual relations can alleviate the negative impact of insufficient training
corpora.

####  4.4.2 The impacts of inadequate training sentences

As illustrated in Figure 6, we evaluate the impact of inadequate training
sentences, where the x-axis denotes the number of training sentences in the
distant supervision training corpora, and the y-axis denotes the F1-score of
relation extraction. We have the following key observations:

  * •

The performance of the original PCNN + ATT increases as an entity pair has
more training sentences in the distant supervision training corpora. It
reveals that inadequate training sentences indeed have a negative impact on
RE.

  * •

Our PA-TMR and PA-TRP methods outperform the PCNN+ATT for extracting relations
for the entity pairs with inadequate training sentences significantly. This is
due to the fact that implicit mutual relations capture extra semantics from
the unlabeled text corpora and contribute to predicting their relations.

  * •

The GDS is much sparser than NYT — most of the entity pairs have less than ten
training sentences. This leads to insufficient training on all entity pairs.
The effects of implicit mutual relations become more prominent, along with the
increasing frequency. Moreover, the improvements of implicit mutual relations
almost stay the same concerning varying training sentences of entity pairs.

###  4.5 Generalization of Our Method (RQ3)

(a) NYT

(b) GDS

Figure 7: Generalization ability of relation prototypes to various RE models.

To illustrate the flexibility of our relation prototypes and implicit mutual
relations, we incorporate them into different neural relation extraction
approaches, including GRU based model with sentence-level attention (GRU+ATT),
CNN + ATT [7], PCNN [19], and PCNN + ATT [7]. As elaborated in Figure 7, we
have the following key observations:

  * •

The performance of original RE models varies a lot on the two datasets. Among
them, the attention mechanism plays a critical role, no matter in which
datasets and in which types of neural networks. In terms of the neural
encoder, PCNN outperforms than GRU and CNN on NYT, while, the vanilla CNN
performs better on GDS.

  * •

The incorporation of both TMR and TRP achieves significant improvements across
all of these models. Especially, we stably have the most performance gains by
using PCNN+ATT as our basic neural encoder on two datasets. This demonstrates
the potential of PCNN that models entity mentions separately.

  * •

Compared to TMR, the improvements of TRP is larger on NYT than that on GDS.
This is mainly because, on GDS, the transferring learning among relations is
less important than the enhancements of entity pair representations with
implicit mutual relations. There are only five relations to classify (Table
I), and each relation has sufficient training instances. However, GDS still
suffers the long-tail issue with respect to entity pairs. This demonstrates
the effectiveness of our proposed method to deal with the long-tail issues
from both two aspects: the relations with insufficient training instances and
infrequent or even unseen entity pairs.

###  4.6 Impacts of Each Components

To verify the effectiveness of each main component in our proposed model, we
conduct an ablation study by removing each of them and present their
performance in Figure 8. We can see: (1). the utilization of the type (i.e.,
+T) or implicit mutual relations between pair of entities (i.e., +MR) can only
bring limited performance gains, while a noticeable increase of the
combination of them (i.e., +TMR) demonstrates that they are highly
complementary. (2). The learned relation prototypes (i.e., +RP) boost the
performance compared to using implicit mutual relations only (i.e., +MR) on
both datasets. They actually use the same information, since relation
prototypes are derived from the centroid of the referred entity pairs. (3). By
incorporating the prior relation hierarchy (i.e., +TRP/h v.s. +TRP), our
proposed model further improves the performance on NYT, and the improvements
on GDS is slight because of the limited prior information among five relations
only.

(a) NYT

(b) GDS

Figure 8: Impacts of each component by removing them from PA-TRP one by one
and presenting their ACU scores.

###  4.7 Case Study

In this section, we conduct qualitative analysis by presenting actual cases to
give an intuitive impression of our method. Still, we present cases from two
perspectives of relation prototypes and implicit mutual relations.

TABLE V: Top 5 proximate relations based on prototypes given entity pairs
(Namibia, Windhoek) and (General Motors, Flint).

Entity Pair | Top 5 proximate relations | #Training
---|---|---
(Namibia, Windhoek) | /location/country/capital | 170
/location/country/administrative_divisions | 389
/location/location/contains | 8321
/location/fr_region/capital | 1
/location/cn_province/capital | 2
(General Motors, Flint) | /bussiness/company/locations | 3
/business/company/place_founded | 179
/business/company/major_shareholders | 29
/time/event/locations | 2
/film/film_festival/location | 4

####  4.7.1 Proximity among relation prototypes

Relation prototypes aim to capture the meaning of relations and their
proximity relationships. Given any entity pair, we can compute its similarity
with these prototypes to provide possible relation candidates and transfer
knowledge among proximate relations. We adopt cosine similarity as
measurement. As demonstrated in Table V, we report top 5 proximate relations
with the highest scores to entity pairs (Namibia, Windhoek) and (General
Motors, Flint). Correct relations are denoted as bold fonts.

We can see that the relation prototypes already can provide a strong baseline
for ranking correct relations pretty high (i.e., $1$st and $2$nd). Besides,
all of the neighbors are proximate even they do not belong to the same branch,
which provides complementary signals to the prior relation hierarchy and
benefits the knowledge transfer. This demonstrates the effectiveness of our
relation prototype learning based on implicit mutual relations. Next, we also
wonder how the implicit mutual relations of entity pairs are well coherent
with each other if they share similar relations, in order to ensure the high
quality of relation prototypes and alleviate the negative impacts of
insufficient training of infrequent or even unseen entity pairs.

####  4.7.2 Similarity among implicit mutual relations

TABLE VI: Top 10 similar implicit mutual relations given entity pair (Stanford University, California) Entity pairs | Cosine similarity | Relation
---|---|---
(University of Chicago, Chicago) | 0.788 | locatedIn
| (University of Southern California,
---
Los Angeles)
0.758 | locatedIn
(Mikheil Saakashvili, Tbilisi) | 0.697 | bornIn
| (Columbia University,
---
New York City)
0.687 | locatedIn
(University of London, London) | 0.681 | locatedIn
(Abilene,Texas) | 0.681 | locatedIn
| (University of Pennsylvania,
---
Philadelphia)
0.668 | locatedIn
(Chapel Hill, North Carolina) | 0.667 | locatedIn
(Stanford, Florida) | 0.665 | locatedIn
(Central Florida, Florida) | 0.658 | locatedIn

Remember that the implicit mutual relation is represented as the difference
between the pair of entity embeddings, which are learned from the entity co-
occurrence graph. Thus, given any entity pair, e.g., (Stanford University,
California), we can compute the nearest neighbors according to their implicit
mutual relations. We adopt cosine similarity as the measurement in the
embedding space. As demonstrated in Table VI, we report the top 10 neighbors
with the highest cosine scores. If cosine similarity of the embedding offsets
is high, the corresponding entity pair is similar, and they tend to have
similar relations. We can observe that only one entity pair has different
relations to (Stanford University, California), and most entity pairs share
the same “locatedIn” relation. It indicates that the defined entity co-
occurrence graph is reasonable to capture the implicit mutual relations after
vertex embedding. Furthermore, the implicit mutual relations of entity pairs
are close to each other if they have the same relation, which benefits the
prototype learning.

##  5 RELATED WORK

Recently, neural network methods for relation extraction have made great
progress. Zeng et al. [24] propose a CNN-based model which can capture
features at both lexical and sentence levels. PCNN [19] introduces piecewise
max-pooling in CNN to separate the textual features from head entity, tail
entity, and relations. To further improve the performances, a host of work
focuses on the following aspects: advanced Neural Encoder, side Information,
and robustness to Poor Annotations. Next, we will describe the main progress
in each aspect.

###  5.1 Advanced Neural Encoder

The neural encoder that learns text representations plays a critical role in
relation extraction. A better neural encoder with strong feature abstraction
ability will result in superior performance [25, 26]. Therefore, many works
focus on improving the neural encoder to get more prominent relation
extraction models. Santos et al. [27] propose a ranking-based convolutional
neural network (CR-CNN), and Nguyen et al. [28] utilize multiple window sizes
for CNN filters to obtain features from various granularities. Miwa et al.
[26] stack bidirectional tree-structured LSTM-RNNs on bidirectional sequential
LSTM-RNNs to encode both word sequence and dependency tree. To encode multiple
entity pairs in a sentence simultaneously, Christopoulou et al. [25] regard
entities as nodes in a fully-connected graph and encode them with a walk-based
model.

Recently, pre-trained language models (LMs), such as BERT [29], can provide a
powerful neural encoder and achieve great success in many downstream tasks.
However, although they perform well on sentence-level RE [30], few studies
apply them to bag-level RE. This is because LMs have a promising finetuning
performance with only a few training data, while they suffer from
inefficiency. Bag-level RE datasets can utilize distant supervision to collect
adequate annotations for training, but inevitably introduce much noise.
Moreira et al. [31] presents unsatisfactory Precision-Recall curves of BERT-
based RE. Yu et al. [32] designs a time-decay selective attention mechanism to
deal with the noisy issue, and achieves improvements. That is, how to
efficiently model the long-tail bags of sentences and mitigate the negative
impacts of noise become key challenges of bag-level RE. More advanced neural
encoder is not our main focus. Instead, we aim to provide a general way to
transfer knowledge between proximate relations, which can be applied to
various neural architectures.

###  5.2 Side Information

There is some external knowledge that benefits the relation classification,
such as relation alias information [17], part-of-speech tags [33] and semantic
information [34]. Therefore, some works tend to introduce this useful
information as additional supervision signals for superior performance.

For relation information, Vashishth et al. [17] utilize the relation alias
information (e.g. founded and co-founded are aliases for the relation
founderOfCompany) to enhance the relation representation. Zeng et al. [35]
construct the relation path to facilitate longer dependency between entities,
which may be not in the same sentence. For entity information, Ji et al [36]
introduce entity descriptions to enhance entity representations, while Liu et
al. [37] utilize entity type information, which is directly related to
specific relations. For example, the relation capital must be between two
location entities, rather than between two persons. Another interesting work
directly employs the KG as the side information [6]. They utilize an end-to-
end neural network to complete the missing relations in KG and to reduce error
propagation between relation extraction and the upstream task named entity
disambiguation. Besides, additional information from related tasks are
demonstrated to be effective, such as NER [38, 39, 40, 41] and correlation
analytics [42].

The quality and quantity of the above-mentioned information highly depend on
their sources, which can not always be guaranteed. Therefore, we focus on the
unlabeled text that extensively exists on the web, mine implicit mutual
relations between entities, and utilize them to represent relation prototypes
for transfer learning. Furthermore, our proposed method can also employ other
additional information (i.e., types in experiments) for better performance.

###  5.3 Robustness to Poor Annotations

Neural network models usually require a large amount of training data, which
is expensive to obtain or only available in specific domains. This leads to
the problem of lacking annotations, which become more serious for large-scale
datasets. To address the issue, the distant supervision is proposed [43] to
annotate relation in texts automatically. The assumption is that if an entity
pair $(e_{h},e_{t})$ has a relation $r$, any sentence that contains $e_{h}$
and $e_{t}$ might express that relation. So labeled data can be obtained by
aligning sentences to KGs. However, the distant supervision will inevitably
introduce noise and bring long-tail problems (as discussed in Section 1).
Therefore, many works attempt to address how to alleviate the performance loss
caused by noisy data [7, 44] and by the relations without sufficient training
corpora [10, 8, 18]. We roughly classify them into two groups in terms of the
noise and long-tail problem.

####  5.3.1 Denoise in distant supervision

To mitigate the noise caused by distant supervision, some works [9][44]
utilize multi-instance learning, which allows different sentences to have at
most one shared label. The multi-instance learning combines all relevant
instances to determine the relation of the targeted entity pair, which thereby
alleviates the impact of incorrectly labeled instances. Surdeanu et al. [45]
get rid of the restrict that different sentences can only share one label by
utilizing a graphical model. It can jointly model multiple instances and
relations.

With the development of neural network, attention mechanism is proposed to
help neural models focus on important training data. In the field of relation
extraction, attention mechanism is widely used to mitigate the effects of
noisy data [16]. Existing attention approaches can be categorized into two
groups: sentence-level attention and word-level attention. Sentence-level
attention [7] aims at selecting the sentences w.r.t., the relational strength
between the target entity pair. Similarly, word-level attention [46] focuses
on high-quality words to measure the target relation. Furthermore, Wang et al.
[47] adopt hierarchical attention, which combines these two attention
mechanisms.

Alternatively, reinforcement learning can also alleviate the effects of noisy
data [22] [48]. The reinforcement learning methods mainly consist of two
modules: an instance selector module aims to select the high-quality
instances, and the other module of relation classifier makes the prediction
and provides a reward to the instance selector. The noisy data will be
eliminated by the instance selector, leading to a performance gain.

Adversarial training [49] is also a viable solution to address the noise
problem. Wu et al. [50] generate adversarial samples by adding noise of small
perturbations to the original data, then encourage the neural network to
correctly classify both unmodified examples and perturbed ones for
regularization. The resulting model becomes more robust and generalizable.
Furthermore, Qin et al. [23] utilize the Generative Adversarial Networks(GANs)
[51] to filter distant supervision training dataset and redistribute the false
positive instances into the negative training set.

####  5.3.2 Long-tail relation extraction

Although many works aim to alleviate the negative impacts of noise, only a few
works focus on improving long-tail relation extraction. Some recent works
highlight the importance of RE in few shot settings [52, 53] and achieve great
success. Differently, long-tail RE naturally includes relation types with
different numbers of training sentences, thus focuses on incorporating prior
relation hierarchy for knowledge transfer [10, 8]. The relations under the
same branch are regarded as closely correlated, and can take advantage of each
other’s training data. DPEN [11] incorporates entity type information to learn
relation-specific classifier dynamically.

In this paper, we aim to fill this blank of improving long relation
extraction. Especially, we try to minimize the reliance on additional
information, such as prior hierarchy and entity types. Instead, we only
utilize unlabeled texts, which are easy to obtain. We aim to capture the
commonality among relations for knowledge transfer and the differences between
entity pairs for discrimination.

##  6 CONCLUSION and FUTURE WORK

In conclusion, we have proposed a general approach to learn relation
prototypes from unlabeled texts. The prototype learning method can be applied
in current models for better relation extraction by transferring knowledge
from relations with sufficient training data to long-tail relations. We have
conducted extensive experiments to verify the effectiveness of the proposed
method on two publicly available datasets and compared them with eight state-
of-the-art baselines. The results present significant improvements, especially
in long-tail settings. Further ablation study and case study also demonstrate
the effectiveness of our proposed method and the generalization ability to
current RE models from both quantitative and qualitative perspectives. In the
future, we are interested in enhancing entity embeddings with KG including
structure and attribute information.

## Acknowledgments

This work has been supported by the National Key Research and Development
Program of China under grant 2016YFB1000905 , and the National Natural Science
Foundation of China under Grant No. U1811264 , 61877018 , 61672234 , 61672384
, and the Shanghai Agriculture Applied Technology Development Program, China
(Grant No.T20170303).

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|  Yixin Cao is a research assistant professor with Nanyang Technology
University. Before that, He was a research fellow with National University of
Singapore. He received his Ph.D. from Tsinghua University. His research
interests span NLP and KG. Various parts of his work have been published in
top conferences, such as ACL, EMNLP, AAAI, and WWW. Moreover, he has served as
PC/SPC for conferences including ACL, EMNLP, AAAI, and IJCAI, and invited
reviewers for journals including TKDE, TKDD, and IEEE Access.
---|---
|  Jun Kuang is currently a master student on School of Data Science and
Engineering at East China Normal University, China. His research interests
include knowledge engineering and nature language processing. His work appears
in the International Conference on Data Engineering.
---|---
|  Ming Gao is currently a full professor on School of Data Science and
Engineering at East China Normal University, China. He received his doctorate
from the School of Computer Science, Fudan University. His research interests
include knowledge engineering, user profiling, social network analysis and
mining. His works appear in major international journals and conferences,
including TKDE, KAIS, DMKD, ICDE, SIGIR, ICDM, etc.
---|---
|  Aoying Zhou is a full professor on School of Data Science and Engineering
(DaSE) at East China Normal University (ECNU), where he is the founding dean
of DaSE and the vice president of ECNU. He is the winner of the National
Science Fund for Distinguished Young Scholars supported by NSFC and the
professorship appointment under Changjiang Scholars Program of Ministry of
Education. His research interests include Web data management, data intensive
computing, in-memory cluster computing and benchmark for big data. His works
appear in major international journals and conferences, including TKDE, PVLDB,
SIGMOD, SIGIR, KDD, WWW, ICDE and ICDM, etc.
---|---
|  Yonggang Wen is a Professor with the School Of Computer Engineering,
Nanyang Technological University, Singapore. His research interests include
cloud computing, green data centers, big data analytics, multimedia networks,
and mobile computing. His latest work in multiscreen cloud social televisions
has been featured by global media and recognized with ASEAN ICT Award 2013 and
IEEE Globecom 2013 Best Paper Award. He serves on the editorial boards of IEEE
TRANSACTIONS ON MULTIMEDIA, IEEE ACCESS JOURNAL, and Elsevier Ad Hoc Networks.
---|---
|  Tat-Seng Chua is the KITHCT Chair Professor at the School of Computing,
National University of Singapore. He was the Acting and Founding Dean of the
School from 1998. Dr Chua’s main research interest is in multimedia
information retrieval and social media analytics. He is the co-Director of
NExT, a joint Center between NUS and Tsinghua University. Dr Chua is the 2015
winner of the prestigious ACM SIGMM award for Outstanding Technical
Contributions to Multimedia Computing, Communications and Applications. He is
the Chair of steering committee of ACM ICMR and MMM. Dr Chua is also the
General Co-Chair of ACM Multimedia’05, ICMR’05, SIGIR’08, and Web Science’15.
He serves in the editorial boards of four international journals.
---|---