言語処理学会 第26回年次大会 発表論文集 (2020年3月)

Improving Distantly Supervised Relation Extraction via Textual
Representation of Multi-hop Inference over Text
Qin Dai1 , Naoya Inoue1,2 , and Kentaro Inui1,2
1
Tohoku University, Japan
2
RIKEN Center for Advanced Intelligence Project, Japan{daiqin, naoya-i, preisert, ryo.t, inui}@ecei.tohoku.ac.jp

Abstract
Distantly Supervised Relation Extraction (DSRE) is a widely applied approach to collect relational facts from unstructured text, but often
accompanies with the problem of fuzzy data.
To alleviate this issue, existing approach uses
multi-hop reasoning over text to facilitate therelation classification. However, the method
only represents the textual relation (e.g., themiddle context between an entity pair) as aunit, rather than a textual sequence of words
and so cannot utilize the semantic information (e.g., pretrained word embedding) of the
textual relation for DS-RE. Therefore, in thiswork, we propose a new DS-RE model that
represents the textual relation as a sequenceof words and encodes the sequence with aworld-level attention mechanism. Experimental results prove the effectiveness or the proposed model for DS-RE, because the proposedmodel achieves significant and consistent improvement as compared with baselines, and obtains a competitive results with the state-of-theart models on the NYT10 dataset.

1

Introduction

Knowledge Base (KB) provides large collectionsof relations between entities, typically stored as(eh , r, et ) triplets, e.g., (Tokyo, capital of, Japan).
KBs, such as Freebase (Bollacker et al., 2008) and
DBpedia (Lehmann et al., 2015), are extremely
crucial for many Natural Language Processing(NLP) tasks. However, KBs are often highly incomplete (Min et al., 2013) and this would impede
their usefulness in real-world applications. To enrich the KBs, it is important to turn unstructured
text into an well organized KB, and it belongs to
the task of Relation Extraction (RE).
One obstacle that is encountered when building a RE system is the generation of training instances. For coping with this difficulty, (Mintz

et al., 2009) proposes distant supervision to automatically generate training samples via aligning KBs with text. They assume that if two entities are connected by a relation in a KB, thenall sentences that contain those entity pairs will
express the relation. For instance, (David Yassky,/people/person/place lived, Brooklyn), (Washington Irving, /people/person/place lived, New York)and (Huber Humphrey, /people/person/place lived,
Minneapolis) are fact triplets in Freebase. Distantsupervision will automatically label all sentences,such as Example 1 below, which are taken from
NYT10 dataset, as positive instances for the relation /people/person/place lived. Although distant supervision could provide a large amount oftraining data at low cost, it always suffers fromfuzzy data. For instance, Example 1 should not beseen as the explicit evidences to support the /people/person/place lived relationship between corresponding entity pairs.
(1) Once predictably democratic in national
politics , the anchor of upper midwest liberal populism from the 1920s through Hubert Humphrey
and Walter Mondale, Minnesota is now considered a battleground, with the republicans scheduled to hold their national convention in 2008 in
Minneapolis and St. Paul around that declaration.
To identify relation from these fuzzy evidences,(Das et al., 2016) uses Multi-hop Inferences(MI) over text as the evidence to extract relation.
However, they only represent the textual relation
as a unit relation, rather than a sequence of words.
For instance, they represent the textual relationin T1 in Fig 1 as {e1 , lef t his at the, e2 },ratherthan
thetextualrepresentation{e1 , lef t, his, homestate, to, attend, college, at,the, e2 }. This, thus, cannot utilize the semantic
meaning of each words in the textual relation and aworld-level attention mechanism.
Contributions. We proposed a DS-RE model― 909 ―

Copyright(C) 2020 The Association for Natural Language Processing.
All Rights Reserved.

RC

Relation

Hubert HumphreyMP

Max PoolingRC

Relation
Classiﬁcation

Pointwise
Operation...

/people/person/place_lived

Minneapolis

MP

G&A

Example 1cbag"... Hubert Humphrey and Walter Mondale, Minnesota is now considered a battleground,with the republicans scheduled to hold their national convention in 2008 in Minneapolisand ..."

sbag

...

...

+

+

×

×
a'1

...

...

s1

am

...

G&A

...

sm

...

...

×
a1

...

G&A

cm

...

...

c1

×
a'm

...

Multi-hop Inference over Text

bi-GRU &word-level
Attention
Sentence-level
Attention

T1:
G&A

G&A

"Hubert Humphrey left his home state to attend college at the University of Minnesota."
...

sm

...

s1

...

cm

...

c1

T2:
Ch,t
Multi-hop Inference
Encoding Part

"The University of Minnesota is a highly rated public university located in Minneapolis "Sh,tSentence Encoding Part

Figure 1: An example of multi-hop inference over Text.

Figure 2: Overview of the proposed model.

that represents MI over text as a sequence of words
and conduct evaluation on NYT10 dataset. Our
method achieves a competitive result with the stateof-the-art models. The experimental results provethe effectiveness of the proposed model for DS-RE.

(i.e., edge) that containing the entity pair of interest
from the graph. We apply the random walk procedure 1 to extract MI over text between a given
entity pair. Each MI over text is represented as c ={eh , s1w1 , s1w2 , ...s1wn , s2w1 , s2w2 , ...et } ∈ Ch,t ,where, s1wn indicates the last word of the first hopof the MI.

2

Related Work

RE is a fundamental task in the NLP community.
However, annotation of training data for RE is expensive and time-consuming. To address this issue,
distant supervision is proposed by (Mintz et al.,2009). Recent methods utilize external Background
Knowledge (BK) for DS-RE. (Ji et al., 2017) applies entity descriptions as BK, (Lin et al., 2017)
utilizes multilingual text as BK and (Vashishth
et al., 2018) uses entity types and relation alias
information as BK for DS-RE and (Alt et al., 2019)
utilizes pre-trained language model as BK for DSRE. (Das et al., 2016) uses MI over text as thesource of relation extraction. However, none ofthese existing approaches mentioned above applies
textual representation of MI over text as BK for
DS-RE.

3

Proposed Model

3.1

Generation of Multi-hop Inference

Let (eh , et ) be an entity pair of interest. The text
with entity mention annotations is represented asa graph, where nodes indicates the entities. Since
all parts of the sentence (not just the middle context) are useful for relation classification (Vu et al.,2016; Xiao and Liu, 2016), the edges of the graphare represented by the sentence that contains theconnected entity pair. To reduce the semantic redundancy and computational cost, if there are multiple sentences containing a same entity pair, weselect the sentence with the least words as the edgeof the entity pair. To simulate the situation of reasoning across documents, we remove the sentence

3.2

Architecture

The proposed model consists of two parts: Sentence Encoding Part and Multi-hop Inference Encoding Part , as shown in Figure 2.
Sentence Encoding Part (SEP). SEP is the BiGRU based DS-RE model with word level and sentence level attention proposed by (Jat et al., 2018).
Specifically, given a sentence s with n words si ={w1 , ..., wn } from the bag Sh,t = {s1 , ..., sm }in which each sentence contains the entity pair(eh , et ), vector representation vt for each word wt
is calculated as vt = [vtw ; vtwp1 ; vtwp2 ], where vtw
is k-dimensional GloVe embedding (Pennington
et al., 2014), vtwp1 and vtwp1 are the word position
embedding (Zeng et al., 2014). Then, the hidden
representation for each word hi is obtained by using Bi-GRU (Cho et al., 2014) over the sentence.
The vector representation of the sentence si is calculated via the Equation 1, where rw is a random
query vector for calculating the word level attention
aw
i .
si =

m
X

aw
i hi ,

i=1

exp(hhi , rw i)aw
i = Pnk=1 exp(hhk , rw i)(1)

The vector representation of the entire bag sbag is
calculated via Equation 2, where rs is a random
1
Considering computational time and semantic drift, thecut-off criteria of random walk is manually set as 3.

― 910 ―

Copyright(C) 2020 The Association for Natural Language Processing.
All Rights Reserved.

P@100
52.3
73.0
81.8
68.0
81.0
83.0

P@200
50.2
68.0
75.4
67.0
73.0
75.0

P@300
45.0
67.3
74.3
65.3
70.0
72.3

P@500
39.7
63.6
69.7
65.0
64.2
68.6

Table 1: Precision@N from previous state-of-theart DS-RE models and our proposed model, where†represents that these results are quoted from (Alt et al.,2019), where “(Unit)” indicates the method of MI representation proposed by (Das et al., 2016), which justtakes the last hidden state of Bi-GRU as the vector representation of MI.
1.0

SEP+MIEP(Textual)
SEP+MIEP(Unit)
SEP

0.9

Precision

0.8
0.7
0.6
0.5
0.4
0.3
0.00

0.05

0.10

0.15

Recall

0.20

0.25

0.30

Figure 3:
Precision-Recall curves of
SEP+MIEP(Unit) and SEP+MIEP(Textual).

0.35

SEP,

query vector for calculating the sentence level attention asi .
sbag =

mX

asi si ,i=1

exp(hsi , rs i)
asi = Pm
k=1 exp(hsk , rs i)(2)

Multi-hop Inference Encoding Part (MIEP).
Since a MI could be seen as a sequence of words,
or a special sentence, we apply the similar Bi-GRU
based model used in the SEP to encode a bag 2 of
MI Ch,t = {c1 , ..., cm } into vector representation
cbag . Finally, we use a max pooling layer oversbag and cbag to generate final feature vector andcalculate the probability distribution over the target
relations via the Equation 3.
p(y) = Wmax-pool(sbag , cbag ) + b

4

1.0

Mean
46.8
68.0
75.3
66.3
72.1
74.7

(3)

Experiments

Dataset. We evaluate our model on NYT10
dataset (Riedel et al., 2010), which is created by
2
Considering computational cost, the maximum size ofbag is manually set as 10.

SEP+MIEP(Textual)
DISTRERESIDE
BGWA
PCNN+ATTPCNNMIMLRE
MultiR
Mintz

0.9
0.8

Precision

SystemMintz†
PCNN+ATT†RESIDE†
DISTRE†
SEP+MIEP(Unit)
SEP+MIEP(Textual)

0.7
0.6
0.5
0.4
0.3
0.00

0.05

0.10

0.15

Recall

0.20

0.25

0.30

0.35

Figure 4:
Precision-Recall curves of previous
state-of-the-art method and our proposed model
SEP+MIEP(Textual).

aligning Freebase relational facts with the New
York Times Corpus. Sentences from the year 20052006 are used for training and the sentences from2007 are used for testing. We use ClueWeb12 with
Freebase entity mention annotations (Gabrilovich
et al., 2013) to extract MI over text.
Evaluation Metrics. We follow (Mintz et al.,2009) and conduct the held-out evaluation, in
which a DS-RE model is evaluated by comparing
the fact triplets identified from textual data (i.e.,the set of sentences containing the target entity
pairs) with those in KB. We report Precision-Recall
curves, Precision@N and AUC score, which measures of the area under the Precision-Recall curve.
Results. The Precision-Recall (PR) curves areshown in Figure 4 and Figure 3. Precision@N
are listed in Table 1. The results show that, SEPMIEP(Textual) significantly outperforms the mostof of previous models over almost entire rangeof recall, and achieve a competitive results withthe state-of-the-art models. This proves that our
proposed model is effective for DS-RE.

5

Conclusion and Future WorkIn this work, we hypothesize that textual representation of MI over text is useful for DS-RE. Experimental results validates our hypothesis, and moreover, the proposed model obtains a competitive
results with the state-of-the-art models on NYT10
dataset. Although the MI over text gathered via therandom walk algorithm are proved to be useful for
DS-RE, the randomness of the MI would hinder
its effectiveness. Therefore, in the future, We will
apply more sophisticated strategy such as reinforcement learning (Xiong et al., 2017; Das et al., 2017;
Lin et al., 2018) to search MI for DS-RE.

― 911 ―

Copyright(C) 2020 The Association for Natural Language Processing.
All Rights Reserved.

References
Christoph Alt, Marc Hübner, and Leonhard Hennig.
2019. Fine-tuning pre-trained transformer language
models to distantly supervised relation extraction.
arXiv preprint arXiv:1906.08646.
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim
Sturge, and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM
SIGMOD international conference on Managementof data, pages 1247–1250. AcM.
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger
Schwenk, and Yoshua Bengio. 2014. Learningphrase representations using rnn encoder-decoder
for statistical machine translation. arXiv preprint
arXiv:1406.1078.
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer,
Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2017.
Go for a walk and arrive at the answer: Reasoning
over paths in knowledge bases using reinforcement
learning. arXiv preprint arXiv:1711.05851.
Rajarshi Das, Arvind Neelakantan, David Belanger,
and Andrew McCallum. 2016. Chains of reasoning
over entities, relations, and text using recurrent neural networks. arXiv preprint arXiv:1607.01426.
Evgeniy Gabrilovich, Michael Ringgaard, and Amarnag Subramanya. 2013. Facc1: Freebase annotationof clueweb corpora, version 1 (release date 2013-0626, format version 1, correction level 0).
Sharmistha Jat, Siddhesh Khandelwal, and Partha
Talukdar. 2018. Improving distantly supervised relation extraction using word and entity based attention.
arXiv preprint arXiv:1804.06987.
Guoliang Ji, Kang Liu, Shizhu He, and Jun Zhao.
2017. Distant supervision for relation extraction
with sentence-level attention and entity descriptions.
In Thirty-First AAAI Conference on Artificial Intelligence.
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch,
Dimitris Kontokostas, Pablo N Mendes, SebastianHellmann, Mohamed Morsey, Patrick Van Kleef,Sören Auer, et al. 2015. Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia.
Semantic Web, 6(2):167–195.
Xi Victoria Lin, Richard Socher, and Caiming Xiong.
2018. Multi-hop knowledge graph reasoning withreward shaping. arXiv preprint arXiv:1808.10568.

Bonan Min, Ralph Grishman, Li Wan, Chang Wang,
and David Gondek. 2013. Distant supervision for relation extraction with an incomplete knowledge base.
In Proceedings of the 2013 Conference of the North
American Chapter of the Association for Computational Linguistics: Human Language Technologies,
pages 777–782.
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the
Joint Conference of the 47th Annual Meeting of the
ACL and the 4th International Joint Conference on
Natural Language Processing of the AFNLP: Volume 2-Volume 2, pages 1003–1011. Association for
Computational Linguistics.
Jeffrey Pennington, Richard Socher, and Christopher
Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conferenceon empirical methods in natural language processing (EMNLP), pages 1532–1543.
Sebastian Riedel, Limin Yao, and Andrew McCallum.
2010. Modeling relations and their mentions without labeled text. In Joint European Conference
on Machine Learning and Knowledge Discovery in
Databases, pages 148–163. Springer.
Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga,
Chiranjib Bhattacharyya, and Partha Talukdar. 2018.
Reside: Improving distantly-supervised neural relation extraction using side information. In Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing, pages 1257–1266.
Ngoc Thang Vu, Heike Adel, Pankaj Gupta, and Hinrich Schütze. 2016. Combining recurrent and convolutional neural networks for relation classification.
arXiv preprint arXiv:1605.07333.
Minguang Xiao and Cong Liu. 2016. Semantic relation classification via hierarchical recurrent neuralnetwork with attention. In Proceedings of COLING
2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1254–
1263.
Wenhan Xiong, Thien Hoang, and William Yang Wang.
2017. Deeppath: A reinforcement learning method
for knowledge graph reasoning. arXiv preprint
arXiv:1707.06690.
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou,
Jun Zhao, et al. 2014. Relation classification via convolutional deep neural network. In COLING, pages
2335–2344.

Yankai Lin, Zhiyuan Liu, and Maosong Sun. 2017.
Neural relation extraction with multi-lingual attention. In Proceedings of the 55th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers), pages 34–43.

― 912 ―

Copyright(C) 2020 The Association for Natural Language Processing.
All Rights Reserved.