DEIM Forum 2017 G8-4

Cross-Language Record Linkage by Exploiting Semantic Matching of Textual
Metadata
Yuting SONG† Taisuke KIMURA† Biligsaikhan BATJARGAL‡ and Akira MAEDA†‡†Graduate School of Information Science and Engineering, Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 JAPAN‡Research Organization of Science and Engineering, Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 JAPAN†‡College of Information Science and Engineering, Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 JAPANE-mail:†{gr0260ff, is0013hh}@ed.ritsumei.ac.jp,‡biligsaikhan@gmail.com,†‡amaeda@is.ritsumei.ac.jp

Abstract Cross-language record linkage is a task of finding pairs of records that refer to the same entity across multiple
databases in different languages. It is crucial to various research fields, such as federated search and data integration. The
matching of textual values of metadata fields plays an important part in comparing record pairs. When matching textual values
across languages, one problem is that the mismatches between semantically related translations of metadata values in source
language and metadata values in target language, which refer to the same entity. For example, when comparing the records inJapanese (source language) and English (target language), the Japanese word “白雨” in metadata is translated into “rainfall”.
However, the corresponding word in English metadata is “storm”, which is semantically related to “rainfall”. As a consequence,the commonly used string-based matching cannot measure the relevance of semantically related words. In this paper, we
propose a method for semantic matching of textual metadata, which is based on word embedding that can capture the semantic
similarity relationships among words. The effectiveness of this method is evaluated on film related textual metadata inJapanese and English. Then, we use our method to link the identical Ukiyo-e prints between the databases in Japanese andEnglish.
Keywords Cross-language record linkage，word embedding，semantic matching

1. Introduction
Record linkage is a process of matching records fromdigitalized not only in Japanese digital museums with theseveral databases that refer to the same entities. It could

metadata information in Japanese, but also in digitalbe employed to integrate and combine the data frommuseums of foreign countries with metadata information

multiple sources, in order to improve data quality and toin their native languages [2]. This situation poses new

reduce costs and efforts in data acquisition [1].

challenges to the task of classical record linkage, since it

In recent years, as the World Wide Web becomes widely
matured in more and more countries, the information is

needs

to

link

identical

entities

acrosslanguages

boundaries.

being produced in variety of languages. Thus, the identical

In cross-lingual tasks, translation procedure is usuallyentities can exist in multiple databases in different

required to tackle the language barriers [14][20][21].

languages. For example, the identical Ukiyo-e prints 1 are

After the translating step, the record pairs are comparedwithin the same language, which is similar to monolingual

1

The Ukiyo-e is a type of Japanese traditional woodblock
printing, which is known as one of the popular arts ofthe Edo period (1603-1868).

record linkage that has been studied for a long time.
In monolingual record linkage, the mismatches of
metadata values are mainly due to the typographicalFigure 1. An example of mismatches between translated metadata values and metadata values in target language due tothe use of different wordings to express the same meaning
variations of string data, which can be measured by

the performance are presented in Section 6. Finally, thestring-based approximate comparisons [7][8]. However, inconclusion and future work follow in Section 7.

cross-language record linkage, the mismatches between
translated metadata values and metadata values in target

2. Related work

language are not only due to the typographical variationsof words but also the use of different wordings to express
the same meaning. Figure. 1 gives an example of this typeof mismatch. The word “白 雨 ” in Japanese is translated
into “rainfall” by a Japanese-English bilingual dictionary.

Cross-language entity linking [10][11] is related to ourwork to some extent, which aims to link the named entities
in the texts in one language to a knowledge base in anotherlanguage. In this task, much contextual information of

However, the corresponding word in English title isnamed entities in texts and content of articles in“storm”, which is translated by a human expert translator.

knowledge bases can be employed. However, our work

Such mismatches cannot be measured by string-based

focuses on the record linkage where only the metadata

comparison.

values can be utilized, which are usually short texts, andIn this paper, we propose a method for cross-language

sometimes in poor quality.

record linkage, which employs the semantic matching of

Cross-language knowledge linking [12][13] is anothertextual metadata. Our method is based on recently results

related task. Most methods are proposed using theinvectorstructural information of data, such as interlink andrepresentation of words. The learned word embedding canoutlink in the articles [12], to find the identical articles

capturee.g.

between knowledge bases in different languages. BabelNet

vector(“Berlin”) - vector(“Germany”) + vector(“France”)[13] is a large multilingual lexical knowledge base built

is close to vector(“Pairs”). By using this property of wordby combining Wikipedia and WordNet. However, ourembedding, we measure the semantic relevance of textual

approach aims at linking the records in several databases

metadata, which is based on matching of embedding of

in different languages that refer to the same real-world

words in metadata.

entity, not to find the identical lexicons or articles.

word

embeddingthesemantic

[9],

which

relationships

is
of

dense

words,The remainder of the paper is organized as follows.

Our work is also related to cross-language ontology

Section 2 outlines some related work of cross-language

matching. With the development of the Linked Data 2 ,record linkage. Word embedding and the general processontologyof cross-language record linkage are described in Section

researchers. Cross-language ontology matching is to findmatchingisattracting

3 and Section 4, respectively. Section 5 introduces our
proposed method. Experimental setup and evaluation of2

http://linkeddata.org/.

interests

of

some

equivalent elements between two semantic data sources

language are translated into target language. How to[14][15][16][17]. The difference between our goal andtranslate metadata values will be introduced in detail in

theirs is that our work focuses on general relational

Section 5.

databases.

Secondly, record pairs are compared by calculating thesimilarities between metadata values within the samelanguage, which is similar to the monolingual record

3. Word embedding
Word embedding, distributed representations for words,

linkage. Since several metadata values are compared for

were firstly proposed by Rumelhart et al. [18] and haveeach record pair, it results that numerical similarity values

achieved impressive results in many natural language

are calculated for that pair, which can be represented by aprocessing tasks [24], such as named entity recognition

vector.

[25], word sense disambiguation [16]. Mikolov et al. [9]The next step is to classify the compared record pairs

theinto three categories: matches, non-matches or possible

skip-gram and continuous bag-of-words models, which arematches. The possible matches will be further compared toprobably the most popular models and available in theclassified

introduced

word2vec

two

novel

toolkit

3

word

.

embeddingThese

models,models

learn

word

into

matchesornon-matches.

Finally,toevaluate the results of record linkage.

representations by employing a simple neural networkarchitecture.

Specifically,

theskip-gram

model

is

consisted of three layers, input, projection and output
layers, to predict contextual words of the input word
vector. The training objective is to learn word vector
representations that are good at predicting its context inthe same sentence [9]. Due to its simple architecture, theskip-gram model can be trained on a large amount of
unstructured text data in a short time (billions of words inhours) using a conventional desktop computer.
The

main

advantageoflearnedword

vectorrepresentations is that semantically similar words areclose in the vector space. Moreover, the complex word
relationships can be captured by performing simple
algebraic operations on the word vectors. For example,
vector(“King”) - vector(“Man”) + vector(“Woman”) is

Figure 2. The general process of cross-language record

closest to the vector representation of the word “Queen”

linkage[27]. In this paper, although we utilize word2vec to learn
word embedding, other word embedding models are also
considerable [28][29].

5. Methodology
In this paper, we deal with the scenarios where textual
metadata of records are descriptive metadata, which is

4. Cross-language record linkage
Compared with the classical monolingual record linkage[3][4][5][6], the task of cross-language record linkage

used to describe a data resource for purposes such as
discovery and identification, e.g. descriptive metadata for
an image may include: title, creator, and description.

requires a translation procedure, since the records that will
be matched are from databases in different languages.

5.1 Translating textual metadata values

The general process of cross-language record linkage is

As mentioned above, translation process is required inshown in Figure. 2. Firstly, the metadata values of a record,order to compare the textual metadata within the samee.g. title, author, publisher of an image record, in source

language. The two commonly used methods of translation
are dictionary based method and machine translation based
method [14][20][21][22][23].

3https://code.google.com/p/word2vec/

Dictionary

based

methodfortranslating

textualmetadata values is to find translations of each word inmetadata by using machine readable bilingual dictionaries.

regarded as a word. Therefore, the semantic similarity

Due to its simplicity, many previous studies employed

between a phrase and a word can also be measured bybilingualformula (1).

dictionaries

to

deal

with

cross-language

problems [20][21][22]. However, bilingual dictionaries

Our goal is to measure the semantic similarity of textual

pose some problems. One problem is that most terms in

metadata. Intuitively, if a translated textual metadata

dictionaries have more than one translations. In this case,contains more words that can match the words in metadata

word sense disambiguation is required. Another one is thatin target language, either exactly or semantically, it might

general bilingual dictionaries only contain common words.

have more possibility that they describe an identical entity.

As a consequence, the translations of rare proper names

We represent textual metadata as a set of embedded words.

cannot be found by using bilingual dictionaries.

The similarity between the translated textual metadata

Machine translation based method is also used to solve( 𝑀)=>$? ) and metadata in target language ( 𝑀)>=@A) ) iscross-language problems [14][23], which is to findformulated as the cumulative similarity of word pairs

translations by employing a machine translation engine,

between 𝑀)=>$? and 𝑀)>=@A) . First, for each word 𝑖 (𝑤, )such as well-known Google 4 and Bing 5 translators.

in 𝑀)=>$? , we calculate the similarities between 𝑤, andIn the Section 6, we will show the experimental results

each word 𝑗 (𝑤: ) in 𝑀)>=@A) , the similarity between 𝑤,

of cross-language record linkage by translating theand 𝑤: is calculated by formula (1). Then, the maximum

metadata values using both two methods explained above.

similarity between 𝑤, and each word j (𝑤: ) in 𝑀)>=@A) is
regarded as the similarity contribution of 𝑤, to similarity
between 𝑀)=>$? and 𝑀)>=@A) , which is formulated in (2).

5.2 Semantic matching of textual metadata
Assume we are provided with a word embedding matrix
𝑊 ∈ 𝑅 $×& for a finite size vocabulary of 𝑛 words. The
𝑖

)*

&

row, 𝑤, ∈ 𝑅 , represents the embedding of the 𝑖𝐶𝑜𝑛𝑡𝑟𝑖 𝑤, = 𝑚𝑎𝑥 𝑤𝑜𝑟𝑑?,G 𝑖, 𝑗∀𝑗 ∈ {1, … , 𝑛}

(2)

)*

word. The dimension of word embedding space is 𝑑.

𝐶𝑜𝑛𝑡𝑟𝑖 𝑤,

Our method is to incorporate the semantic similarity
between word pairs into the similarity of textual metadata.

represents the the similarity contribution of

𝑤, in 𝑀)=>$? to similarity between 𝑀)=>$? and 𝑀)>=@A) . n
represents the number of words in 𝑀)>=@A) .

We use the cosine similarity metric to measure the word

Finally, we can define the similarity between 𝑀)=>$?

similarity. Specifically, the semantic similarity between

and 𝑀)>=@A) as the cumulative similarity contribution ofword 𝑖 and word 𝑗 is formulated in (1).

each 𝑤, in 𝑀)=>$? , which is formulated in (3).

𝑤𝑜𝑟𝑑_𝑠𝑖𝑚 𝑖, 𝑗 = 𝑐𝑜𝑠𝑖𝑛𝑒(𝑤, , 𝑤: )𝑆𝑖𝑚 𝑀)=>$? , 𝑀)>=@A) =(1)

Note that in a bilingual dictionary, the corresponding

𝑆𝑖𝑚 𝑀)=>$? , 𝑀)>=@A)

G, 𝐶𝑜𝑛𝑡𝑟𝑖 𝑤,(3)

represents the similarity between

translations of a word are sometimes phrases. For example,𝑀)=>$? and 𝑀)>=@A) . m represents the number of words inthe English translation of Japanese word “時 空 ” is “space𝑀)=>$? .

and time”. As mentioned in Section 3, the word embedding

Figure3

illustrates

ourmethodtocalculatethehas the property that the complex word relationships cansimilarity between 𝑀)=>$? and 𝑀)>=@A) . In this example,

be captured by performing simple algebraic operations on

we would like to link the identical film by using the film

the-title. The film title “岸 辺 の 旅 ” is translated into “Thevector(“Man”) + vector(“Woman”) is closest to the vector

shore trip”, which is translated metadata 𝑀)=>$? . First,

representation

thisstop words (e.g. “to”, “the”) are removed, leaving shore,

property of word embedding, we represent a phrase by atrip in 𝑀)=>$? and journey, shore in 𝑀)>=@A) . The arrowcombination of embedded words (excluding the stop words)

from each word in 𝑀)=>$? to word in 𝑀)>=@A) are labeledin that phrase. Recalling the example above, the phrasewith their contributions to the similarity between 𝑀)=>$?

word

vectors.
of

For

the

instance,

word

vector(“King”)“Queen”.

Utilizing“space and time” is represented by vector(“space”) +and 𝑀)>=@A) . The word trip in 𝑀)=>$? has semanticallyvector(“time”). The combined vector for a phrase can be

related to the word journey in 𝑀)>=@A) . This semantic
relationship between words can be captured by word4

https://translate.google.com

5

http://www.bing.com/translator

embedding. Consequently, the similarity between 𝑀)=>$?
and 𝑀)>=@A) becomes higher than that without using wordFigure 3. An example of semantic matching of textual metadata
embedding.

based method (Dict) and machine translation based
method (MT), that are explained in Section 5.1. In

6. Experiments
In this section, we first evaluate our method on film
related textual metadata from Japanese and English

dictionary based method, we use EDR 7 Japanese-English
bilingual dictionary. In the machine translation based
method, we use Microsoft translator API 8.

DBpedia 6 . Then, we use our method to link the identicalIn the experiments, as the baseline method, we employ

Ukiyo-e prints between the databases in Japanese andstring based matching of textual metadata. This method isEnglish.

to link the records across languages by using the exact
string matching of textual metadata. After translating the

6.1 Experimental dataset

textual metadata into target language, the string based

In order to evaluate the effectiveness of our proposed

similarity between 𝑀)=>$? and 𝑀)>=@A) is measured by themethod, we construct a dataset that contains pairs of film

percent of exactly matched words in 𝑀)=>$? , which istitles from Japanese and English DBpedia. These pairs areformulated in (4).

the titles of article pairs that contain Japanese-English
cross-lingual links with Wikipedia, which is extracted by

𝑠𝑡𝑟_𝑠𝑖𝑚(𝑀)=>$? , 𝑀)>=@A) ) =DBpedia. The experimental dataset consists of 1,847 pairs)*A $NGOA= PQ ?)=,$@ G>)R*A& SP=&? ,$ TUVWXYof film titles. A small part of experimental dataset is)*A )P)>Z $NGOA= PQ SP=&? ,$ TUVWXY(4)shown in Table 1. Each row in the Table 1 represents a pair
To evaluate the experimental results of cross-languageof film title that describes an identical film.
Table 1. A small part of experimental dataset

record linkage, we utilize precision within Top-1, Top-5
and Top-10.
6.3 Experimental results
Table 2 show the performance of baseline method andour proposed method. According to the results, our methodthat employs semantic matching of textual metadataachieves better results than the comparison method ofstring based matching, especially in precision in Top-1

6.2 Experimental setup

and precision within Top-5. By using machine translation

In the experiments, word embedding is learned withbased method, our method gains the precision of 0.52 inword2vec by using the articles in English Wikipedia dump

Top-1, which is the strictest evaluation metric.

that contains more than 3 billion words.
In translation process, we translate the textual metadata

7

http://www2.nict.go.jp/out-promotion/techtransfer

8

https://www.microsoft.com/en-us/translator/transl

in Japanese to English by using two methods, dictionary6

http://wiki.dbpedia.org/EDR/index.html
atorapi.aspx

Comparing two translation methods, machine translation

Table 3. A small part of experimental dataset of Ukiyo-e

based method achieves much better results than dictionary

titlesbased method. This is because lots of words in Japanesefilm titles are not included in the bilingual dictionary.
The results of precision in Top-k (k=1, 5, 10) of themachine translation method is also shown in Table 2. The
precision grows as k increases. In the precision within
Top-10, our method achieves the precision of 0.64. Thus,
if we do not want to find the exact cross-language links,
ourmethodcan

also

providecross-language

linkcandidates.

Here we translate non-proper nouns of Japanese titles
into English by using EDR Japanese-English bilingual
dictionary. The proper nouns are transliterated by Hepburn

Table 2. Performance of cross-language record linkage

Romanization system 11. Both comparison method and ourwith two methods

method that are mentioned in Section 6.2 are used in thisexperiment.
Table 4 shows the performance of two methods forcross-language record linkage using descriptive titles andall titles of Ukiyo-e prints. From the results, it can be seenthat our method performs better than the string basedmatching method, especially for descriptive titles that

6.4 Linking the identical Ukiyo-e prints between the
databases in Japanese and English
We

also

conducted

experiments

contain one or more non-proper nouns. The reason is that
descriptive titles contain one or more non-proper nouns,

thewhich are translated based on the meaning of words.

effectiveness of the proposed method on Ukiyo-e prints intoverifyHowever, Japanese proper nouns are transliterated based

Japanese and English.

on the pronunciations, where our semantic matching

Ukiyo-e is a type of Japanese traditional woodblock

method is not suitable.

printing, which is known as one of the popular arts of theEdo period (1603-1868). These prints have been digitized

Table 4. Results of cross-language record linkage on

and exhibited on the Internet in many libraries andUkiyo-e prints

museums with textual metadata in various languages [19].
Thus, it is suitable to evaluate our method with Ukiyo-e
prints data.
The titles of Ukiyo-e prints are used to link records.
This dataset consists of 243 Japanese titles of Ukiyo-e
prints from the Edo-Tokyo Museum 9 and 3,293 English
titles from the Metropolitan Museum of Art 10 , in which

7. Conclusion

each Japanese title has at least one corresponding English

In this paper, we proposed a method that employs thetitle. A small part of Ukiyo-e titles is shown in Table 3.

distributed representations of words to measure semantic

Each row in the Table 3 represents a pair of Ukiyo-e title

similarities of textual metadata for cross-language recordthat describes an identical Ukiyo-e print. Among the 243

linkage.

Japanese titles, 143 titles are descriptive titles that containat least one non-proper noun.

The preliminary experimental results have shown that
this approach improves the precision of cross-language
record linkage.
In the future work, we plan to improve the precision ofcross-language record linkage by combining the word9

http://digitalmuseum.rekibun.or.jp/app/selected/e
do-tokyo10

http://www.metmuseum.org/

embedding to represent the metadata values. Besides, wewill also evaluate our method on the dataset in otherlanguages.

References[1] P. Christen, “A Survey of Indexing Techniques for
Scalable Record Linkage and Deduplication”, IEEE
Transaction on Knowledge and Data Engineering,24(9):1537-1555, 2012.
[2] T. Kimura, B. Batjargal, F. Kimura, and A. Maeda,“Finding the Same Artworks from Multiple Databasesin Different Languages”, In Conference Abstracts ofDigital Humanities, 2015.
[3] I. P. Fellegi and A. B. Sunter, “A Theory for Record
Linkage”, Journal of the American Statistical
Association, 64 (328), pp. 1183-1210, 1969.
[4] S. Sarawagi and A. Bhamidipaty, “Interactive
Deduplication using Active Learning”, In Proc. 8th
ACM SIGKDD Int. Conf. Knowledge Discovery andData Mining, pp. 269-278, 2002.
[5] M. Bilenko, R. Mooney, W. Cohen, P. Ravikumar and
S. Fienberg, “Adaptive Name Matching inInformation Integration”, IEEE Intelligent Systems,
18(5), pp. 16-23, 2003.
[6] A.K. Elmagarmid, P.G. Ipeirotis and V.S. Verykios,“Duplicate Record Detection: A Survey”, IEEE
Transactions on Knowledge and Data Engineering,19(1), 1-16, 2007.
[7] G. A. Stephen, “String Searching Algorithms”, World
Scientific, 1994.
[8] W. E. Winkler, “String Comparator Metrics andEnhanced Decision Rules in the Fellegi-Sunter Modelof Record Linkage”. In Proc. of the Section on
Survey Research Methodology, pp. 354-359, 1990.
[9] T. Mikolov, K. Chen, G. Corrado and J. Dean,“Efficient Estimation of Word Representations in
Vector Space”, arXiv preprint arXiv:1301.3781.,
2013.
[10] P. McNamee, J. Mayfield, D. Lawrie, D. W. Oard and
D. Doermann, “Cross-Language Entity Linking”, In
Proc. of the 5th International Joint Conference on
Natural Language Processing, pp. 255-263, 2011.
[11] J. Mayfield, D. Lawrie, P. McNamee and D. W. Oard,“Buildinga
Cross-Language
EntityLinking
Collection in Twenty-One Languages”, In Proc. of
the Cross Language Evaluate Forum, pp. 3-13, 2011.
[12] Z. Wang, J. Li, Z. Wang and J. Tang, “Cross-Lingual
Knowledge Linking across Wiki Knowledge Bases”,
In Proc. of the 21st international conference on
World Wide Web, pp. 459-468, 2012.
[13] R. Navigli and S. P. Ponzetto, “Babelnet: Building a
Very Large Multilingual Semantic Network”, In Proc.
of ACL, pp. 216–225, 2010.
[14] B.
Fu,
R.
Brennan,andD.
O’Sullivan,“Cross-Lingual Ontology Mapping – an Investigationof the Impact of Machine Translation”, In Proc. of
Asian Semantic Web Conference, pp. 1–15, 2009.
[15] J. Li, J. Tang, Y. Li and Q. Luo, “Rimom: A Dynamic
Multistrategy Ontology Alignment Framework”,
IEEE Transactions on Knowledge and Data

Engineering, 21(8), pp.1218–1232, 2009.
[16] J. Tang, J. Li, B. Liang, X. Huang, Y. Li, and K.
Wang, “Using Bayesian Decision for Ontology
Mapping”, Web Semantics: Science, Services andAgents on the World Wide Web, 4(4), pp.243–262,
2006.
[17] X. Zhang, Q. Zhong, F. Shi, J. Li, and J. Tang,“Rimom Results for OAEI 2009”, In Proc. of the 4thInternational Conference on Ontology Matching,pp.208-215, 2009.
[18] D.E. Rumelhnrt, G.E. Hinton, R.J. Williams,“Learning Representations by Back-Propagating
Errors”, Nature, 323, pp.533-536, 1986.
[19] B. Batjargal, T. Kuyama, F. Kimura and A. Maeda,“Identifying the Same Records across Multiple
Ukiyo-e Image Databases using Textual Data in
Different Languages”, In Proc. of the 14th
ACM/IEEE-CS Joint Conference on Digital Libraries,
pp. 193-196, 2014.
[20] L. Ballesteros and C. Bruce, “Dictionary Methods for
Cross-Lingual Information Retrieval”, In Proc. ofInternational Conference on Database and Expert
Systems Applications, pp. 791-801, 1996.
[21] G.A.
Levow,
D.W.
Oard
and
P.
Resnik,“Dictionary-based Techniques for Cross-Language
Information Retrieval”, Information Processing &Management, 41(3), pp.523-547, 2005.
[22] N. Ehsan, F.W. Tompa and A. Shakery, “Using a
Dictionary and N-gram Alignment to Improve
Fine-Grained Cross-Language Plagiarism Detection”,
In Proc. of the 2016 ACM Symposium on Document
Engineering, pp. 59-68, 2016.
[23] A. Barrón-Cedeño, P. Gupta and P. Rosso, “Methods
for
Cross-Language
Plagiarism
Detection”,
Knowledge-Based Systems, 50, pp.211-217, 2013.
[24] R. Collobert, J. Weston, L. Bottou, M. Karlen, K.
Kavukcuoglu and P. Kuksa, “Natural Language
Processing (Almost) from Scratch”, Journal of
Machine Learning Research, 12, pp. 2493–2537,
2011.
[25] P. S. Dhillon, D. Foster and L. Ungar, “Multi-view
Learning of Word Embeddings via CCA”, In
Advances in Neural Information Processing Systems,
2011.
[26] X. Chen, Z. Liu and M. Sun, “A Unified Model for
Word Sense Representation and Disambiguation”, In
Proc. of the 2014 Conference on Empirical Methods
in Natural Language Processing, pp. 1025–1035,
2014.
[27] T. Mikolov, S. W. Yih and G. Zweig, “Linguistic
Regularities
inContinuous
Space
Word
Representations”, In Proc. of the Conference of theNorth American Chapter of the Association for
Computational Liguistics, pp.746-751, 2013.
[28] J. Turian, L. Ratinov, Y. Bengio, and J. Turian,“Word Representations: A Simple and General
Method for Semi-supervised Learning”, In Proc. of
the 48th Annual Meeting of the Association for
Computational Linguistics, pp. 384–394, 2010.
[29] J. Pennington, R. Socher, and C. D. Manning,“Glove: Global Vectors for Word Representation”, In
Proc. of EMNLP, 14, pp. 1532–43, 2014.