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Entity linking based on the co-occurrence graph and entity probability

Authors:

Alan Eckhardt,

Juraj Hreško,

Jan Procházka,

Otakar SmrſAuthors Info & Claims

ERD '14: Proceedings of the first international workshop on Entity recognition & disambiguation

Pages 37 - 44

https://doi.org/10.1145/2633211.2634349

Published: 11 July 2014 Publication History

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Abstract

This paper describes our system for the Entity Recognition and Disambiguation Challenge 2014. There are two tasks: one to find entities in queries (Short Track), the other to find entities in texts from web pages (Long Track).

We have participated in both tracks with the same system tuned to each of the tasks. On the final test set, we reached the f-measure of 71.9% on the Long Track and of 66.9% on the Short Track. We describe our system and its components in depth, together with their influence on performance. The specifics of each of the tasks are also discussed.

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Cited By

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Paschalides DPallis GDikaiakos M(2025)A Framework for the Unsupervised Modeling and Extraction of Polarization Knowledge from News MediaACM Transactions on Social Computing10.1145/37035948:1-2(1-38)Online publication date: 17-Jan-2025
https://dl.acm.org/doi/10.1145/3703594
Martinez-Rodriguez JHogan ALopez-Arevalo I(2020)Information extraction meets the Semantic WebSemantic Web10.3233/SW-18033311:2(255-335)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-180333
Rosales-Méndez H(2019)Towards Better Entity Linking EvaluationCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3314199(50-55)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3314199
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Index Terms

Entity linking based on the co-occurrence graph and entity probability
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning

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Published In

ERD '14: Proceedings of the first international workshop on Entity recognition & disambiguation

July 2014

134 pages

ISBN:9781450330237

DOI:10.1145/2633211

Conference Chairs:
David Carmel
Yahoo! Lab
,
Ming-Wei Chang
Microsoft Research
,
Evgeniy Gabrilovich
Google
,
Bo-June (Paul) Hsu
Microsoft Research
,
Kuansan Wang
Microsoft Research

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 the author(s) 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].

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Association for Computing Machinery

New York, NY, United States

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Published: 11 July 2014

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SIGIR '14

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SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11, 2014

Queensland, Gold Coast, Australia

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ERD '14 Paper Acceptance Rate 18 of 28 submissions, 64%;

Overall Acceptance Rate 18 of 28 submissions, 64%

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Paschalides DPallis GDikaiakos M(2025)A Framework for the Unsupervised Modeling and Extraction of Polarization Knowledge from News MediaACM Transactions on Social Computing10.1145/37035948:1-2(1-38)Online publication date: 17-Jan-2025
https://dl.acm.org/doi/10.1145/3703594
Martinez-Rodriguez JHogan ALopez-Arevalo I(2020)Information extraction meets the Semantic WebSemantic Web10.3233/SW-18033311:2(255-335)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-180333
Rosales-Méndez H(2019)Towards Better Entity Linking EvaluationCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3314199(50-55)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3314199
Cornolti MFerragina PCiaramita MRüd SSchütze H(2018)SMAPHACM Transactions on Information Systems10.1145/328410237:1(1-42)Online publication date: 6-Dec-2018
https://dl.acm.org/doi/10.1145/3284102
Mehrbod AGrilo A(2018)Tender calls search using a procurement product named entity recogniserAdvanced Engineering Informatics10.1016/j.aei.2018.04.00536(216-228)Online publication date: Apr-2018
https://doi.org/10.1016/j.aei.2018.04.005
Balog KBalog K(2018)Entity LinkingEntity-Oriented Search10.1007/978-3-319-93935-3_5(147-188)Online publication date: 3-Oct-2018
https://doi.org/10.1007/978-3-319-93935-3_5
Petz GWetzlinger WNedbal D(2017)Improving Language-Dependent Named Entity DetectionMachine Learning and Knowledge Extraction10.1007/978-3-319-66808-6_22(330-345)Online publication date: 24-Aug-2017
https://doi.org/10.1007/978-3-319-66808-6_22
Hasibi FBalog KBratsberg S(2017)Entity Linking in Queries: Efficiency vs. EffectivenessAdvances in Information Retrieval10.1007/978-3-319-56608-5_4(40-53)Online publication date: 8-Apr-2017
https://doi.org/10.1007/978-3-319-56608-5_4
Cornolti MFerragina PCiaramita MRüd SSchütze HBourdeau JHendler JNkambou RHorrocks IZhao B(2016)A Piggyback System for Joint Entity Mention Detection and Linking in Web QueriesProceedings of the 25th International Conference on World Wide Web10.1145/2872427.2883061(567-578)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.1145/2872427.2883061
Hasibi FBalog KBratsberg SAllan JCroft Bde Vries AZhai C(2015)Entity Linking in QueriesProceedings of the 2015 International Conference on The Theory of Information Retrieval10.1145/2808194.2809473(171-180)Online publication date: 27-Sep-2015
https://dl.acm.org/doi/10.1145/2808194.2809473
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Wikidata based Location Entity Linking

First Steps in Czech Entity Linking

Entity linking leveraging: automatically generated annotation

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