skip to main content
10.1145/3231830.3231845acmotherconferencesArticle/Chapter ViewAbstractPublication PagesawictConference Proceedingsconference-collections
research-article

Detecting Counterpart Word Pairs across Time

Published: 13 November 2017 Publication History

Abstract

Most search engines require users to input specific words to obtain useful results from the Web. However, this requirement is sometimes challenging for users who want to search for information regarding unknown words. It may be especially difficult to learn about the past without knowing suitable words, such as answering the question "Who was the counterpart of the Prime Minister of the UK in the Ottoman Empire in 1900?" We propose a novel search framework for finding counterpart relationships represented by word pairs across time. This framework detects the counterparts by arithmetic operations as well as Word2Vec. To improve the accuracy, our framework groups news articles to develop context for words. After embedding the words into vector spaces, we map a given relationship to another vector space, then perform arithmetic operations. We show our algorithm outputs better results compared with simply applying Word2Vec.

References

[1]
Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, and Wei Zhang. 2015. TimeMachine: Timeline Generation for Knowledge-Base Entities. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). ACM, New York, NY, USA, 19--28.
[2]
Ching-man Au Yeung and Adam Jatowt. 2011. Studying How the Past is Remembered: Towards Computational History Through Large Scale Text Mining. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM '11). ACM, New York, NY, USA, 1231--1240.
[3]
Veronica Boix-Mansilla. 2000. Historical Understanding: Beyond the Past and into the Present. New York University Press. 390--418 pages.
[4]
Ricardo Campos, Gaël Dias, Alípio M.Jorge, and Adam Jatowt. 2014. Survey of Temporal Information Retrieval and Related Applications. ACM Comput. Surv. 47, 2, Article 15 (Aug. 2014), 41 pages.
[5]
Christopher Cieri, Stephanie Strassel, David Graff, Nii Martey, Kara Rennert, and Mark Liberman. 2002. Topic Detection and Tracking. Kluwer Academic Publishers, Norwell, MA, USA, Chapter Corpora for Topic Detection and Tracking, 33--66. http://dl.acm.org/citation.cfm?id=772260.772264
[6]
Joachim Daiber, Max Jakob, Chris Hokamp, and Pablo N. Mendes. 2013. Improving Efficiency and Accuracy in Multilingual Entity Extraction. In Proceedings of the 9th International Conference on Semantic Systems (I-Semantics).
[7]
Quang Xuan Do, Wei Lu, and Dan Roth. 2012. Joint Inference for Event Timeline Construction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL '12). Association for Computational Linguistics, Stroudsburg, PA, USA, 677--687. http://dl.acm.org/citation.cfm?id=2390948.2391023
[8]
Helge Holzmann and Thomas Risse. 2014. Named Entity Evolution Analysis on Wikipedia. In Proceedings of the 2014 ACM Conference on Web Science (WebSci '14). ACM, New York, NY, USA, 241--242.
[9]
Thomas Huet, Joanna Biega, and Fabian M. Suchanek. 2013. Mining History with Le Monde. In Proceedings of the 2013 Workshop on Automated Knowledge Base Construction (AKBC '13). ACM, New York, NY, USA, 49--54.
[10]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. 3111--3119.
[11]
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013. Efficient estimation of word representations in vector space. In NAACL.
[12]
Kira Radinsky and Eric Horvitz. 2013. Mining the Web to Predict Future Events. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM '13). ACM, New York, NY, USA, 255--264.
[13]
Yating Zhang, Adam Jatowt, Sourav S. Bhowmick, and katsumi Tanaka. 2016. The Past is Not a Foreign Country: Detecting Semantically Similar Terms across Time. IEEE Transactions on Knowledge and Data Engineering 28, 10 (Oct 2016), 2793--2807.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
AWICT 2017: Proceedings of the Second International Conference on Advanced Wireless Information, Data, and Communication Technologies
November 2017
116 pages
ISBN:9781450353106
DOI:10.1145/3231830
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • CNRS: Centre National De La Rechercue Scientifique

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Temporal analogy
  2. across-time search
  3. similar relationship detection

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

AWICT 2017

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 41
    Total Downloads
  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media