research-article

Discovering and disambiguating named entities in text

Author:

Johannes HoffartAuthors Info & Claims

SIGMOD'13 PhD Symposium: Proceedings of the 2013 SIGMOD/PODS Ph.D. symposium

June 2013

Pages 43 - 48

https://doi.org/10.1145/2483574.2483582

Published: 22 June 2013 Publication History

Abstract

Disambiguating named entities in natural language texts maps ambiguous names to canonical entities registered in a knowledge base such as DBpedia, Freebase, or YAGO. Knowing the specific entity is an important asset for several other tasks, e.g. entity-based information retrieval or higher-level information extraction. Our approach to named entity disambiguation makes use of several ingredients: the prior probability of an entity being mentioned, the similarity between the context of the mention in the text and an entity, as well as the coherence among the entities. Extending this method, we present a novel and highly efficient measure to compute the semantic coherence between entities. This measure is especially powerful for long-tail entities or such entities that are not yet present in the knowledge base. Reliably identifying names in the input text that are not part of the knowledge base is the current focus of our work.

References

[1]

D. A. Ferrucci. Introduction to "This is Watson". IBM Journal of Research and Development, 2012.

Digital Library

[2]

J. R. Finkel, T. Grenager, and C. D. Manning. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In ACL, 2005.

Digital Library

[3]

T. Heath and C. Bizer. Linked Data. Morgan & Claypool Publishers, 2011.

Digital Library

[4]

J. Hoffart, S. Seufert, D. B. Nguyen, M. Theobald, and G. Weikum. KORE: Keyphrase Overlap Relatedness for Entity Disambiguation. In CIKM, 2012.

Digital Library

[5]

J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum. YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence, 2013.

Digital Library

[6]

J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G. Weikum. Robust Disambiguation of Named Entities in Text. In EMNLP, 2011.

Digital Library

[7]

P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In the thirtieth annual ACM symposium, 1998.

Digital Library

[8]

S. Kulkarni, A. Singh, G. Ramakrishnan, and S. Chakrabarti. Collective Annotation of Wikipedia Entities in Web Text. In KDD, 2009.

Digital Library

[9]

D. Milne and I. H. Witten. An Effective, Low-Cost Measure of Semantic Relatedness Obtained from Wikipedia Links. In WIKIAI, 2008.

[10]

N. Nakashole, M. Theobald, and G. Weikum. Scalable knowledge harvesting with high precision and high recall. In WSDM, 2011.

Digital Library

[11]

L.-A. Ratinov, D. Roth, D. Downey, and M. Anderson. Local and Global Algorithms for Disambiguation to Wikipedia. In ACL, 2011.

Digital Library

[12]

E. F. T. K. Sang and F. De Meulder. Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In CONLL, 2003.

[13]

M. Sozio and A. Gionis. The community-search problem and how to plan a successful cocktail party. In KDD, 2010.

Digital Library

[14]

C. Unger, L. Bühmann, J. Lehmann, A.-C. N. Ngomo, D. Gerber, and P. Cimiano. Template-based question answering over RDF data. In WWW, 2012.

Digital Library

[15]

M. Yahya, K. Berberich, S. Elbassuoni, M. Ramanath, V. Tresp, and G. Weikum. Natural Language Questions for the Web of Data. In EMNLP-CoNLL, 2012.

Digital Library

Cited By

Han HViriyothai PLim SLameter DMussell B(2019)Yet Another Framework for Tweet Entity Linking (YAFTEL)2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2019.00053(258-263)Online publication date: Mar-2019
https://doi.org/10.1109/MIPR.2019.00053
Mai TShi BNicholson PAjwani DSala A(2017)Scalable Disambiguation System Capturing Individualities of MentionsLanguage, Data, and Knowledge10.1007/978-3-319-59888-8_31(365-379)Online publication date: 27-May-2017
https://doi.org/10.1007/978-3-319-59888-8_31
Mishra ABerberich KPerego RSebastiani FAslam JRuthven IZobel J(2016)Event DigestProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911526(493-502)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2911526

Index Terms

Discovering and disambiguating named entities in text
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

Recommendations

Discovering emerging entities with ambiguous names
WWW '14: Proceedings of the 23rd international conference on World wide web

Knowledge bases (KB's) contain data about a large number of people, organizations, and other entities. However, this knowledge can never be complete due to the dynamics of the ever-changing world: new companies are formed every day, new songs are ...
Read More
KORE: keyphrase overlap relatedness for entity disambiguation
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically ...
Read More
Search-based entity disambiguation with document-centric knowledge bases
i-KNOW '15: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business

Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. One possibility to describe these entities within a knowledge base is via entity-annotated documents (document-centric knowledge ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD'13 PhD Symposium: Proceedings of the 2013 SIGMOD/PODS Ph.D. symposium

June 2013

78 pages

ISBN:9781450321556

DOI:10.1145/2483574

Program Chairs:
Lei Chen
Hong Kong University of Science and Technology, China
,
Xin Luna Dong
Google Inc., USA

Copyright © 2013 ACM.

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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGMOD/PODS'13

Sponsor:

SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 23, 2013

New York, New York, USA

Acceptance Rates

SIGMOD'13 PhD Symposium Paper Acceptance Rate 12 of 26 submissions, 46%;

Overall Acceptance Rate 40 of 60 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
213
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Other Metrics

View Author Metrics

Citations

Cited By

Han HViriyothai PLim SLameter DMussell B(2019)Yet Another Framework for Tweet Entity Linking (YAFTEL)2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2019.00053(258-263)Online publication date: Mar-2019
https://doi.org/10.1109/MIPR.2019.00053
Mai TShi BNicholson PAjwani DSala A(2017)Scalable Disambiguation System Capturing Individualities of MentionsLanguage, Data, and Knowledge10.1007/978-3-319-59888-8_31(365-379)Online publication date: 27-May-2017
https://doi.org/10.1007/978-3-319-59888-8_31
Mishra ABerberich KPerego RSebastiani FAslam JRuthven IZobel J(2016)Event DigestProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911526(493-502)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2911526

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents