@inproceedings{wu-tsai-2018-cross,
title = "Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding",
author = "Wu, Chun-Kai and
Tsai, Richard Tzong-Han",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2054",
doi = "10.18653/v1/N18-2054",
pages = "334--339",
abstract = "Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62{\%}. Tested 30 times, our system achieved an average improvement of 2.76{\%} over the current best system (26.86{\%} over baseline), a statistically significant result.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wu-tsai-2018-cross">
<titleInfo>
<title>Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chun-Kai</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Richard</namePart>
<namePart type="given">Tzong-Han</namePart>
<namePart type="family">Tsai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marilyn</namePart>
<namePart type="family">Walker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Heng</namePart>
<namePart type="family">Ji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amanda</namePart>
<namePart type="family">Stent</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">New Orleans, Louisiana</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62%. Tested 30 times, our system achieved an average improvement of 2.76% over the current best system (26.86% over baseline), a statistically significant result.</abstract>
<identifier type="citekey">wu-tsai-2018-cross</identifier>
<identifier type="doi">10.18653/v1/N18-2054</identifier>
<location>
<url>https://aclanthology.org/N18-2054</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>334</start>
<end>339</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding
%A Wu, Chun-Kai
%A Tsai, Richard Tzong-Han
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F wu-tsai-2018-cross
%X Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62%. Tested 30 times, our system achieved an average improvement of 2.76% over the current best system (26.86% over baseline), a statistically significant result.
%R 10.18653/v1/N18-2054
%U https://aclanthology.org/N18-2054
%U https://doi.org/10.18653/v1/N18-2054
%P 334-339
Markdown (Informal)
[Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding](https://aclanthology.org/N18-2054) (Wu & Tsai, NAACL 2018)
ACL
- Chun-Kai Wu and Richard Tzong-Han Tsai. 2018. Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 334–339, New Orleans, Louisiana. Association for Computational Linguistics.