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Evaluating the helpfulness of linked entities to readers

Published: 01 September 2014 Publication History

Abstract

When we encounter an interesting entity (e.g., a person's name or a geographic location) while reading text, we typically search and retrieve relevant information about it. Entity linking (EL) is the task of linking entities in a text to the corresponding entries in a knowledge base, such as Wikipedia. Recently, EL has received considerable attention. EL can be used to enhance a user's text reading experience by streamlining the process of retrieving information on entities. Several EL methods have been proposed, though they tend to extract all of the entities in a document including unnecessary ones for users. Excessive linking of entities can be distracting and degrade the user experience. In this paper, we propose a new method for evaluating the helpfulness of linking entities to users. We address this task using supervised machine-learning with a broad set of features. Experimental results show that our method significantly outperforms baseline methods by approximately 5.7%-12% F1. In addition, we propose an application, Linkify, which enables developers to integrate EL easily into their web sites.

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Cited By

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  • (2022)Multi-source Representation Enhancement for Wikipedia-style Entity Annotation2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892289(01-08)Online publication date: 18-Jul-2022
  • (2019)XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and ApplicationData Intelligence10.1162/dint_a_000031:1(77-98)Online publication date: Mar-2019
  • (2018)Measuring Helpful Aspect of User Experience: The Development of Q-iCalHRegional Conference on Science, Technology and Social Sciences (RCSTSS 2016)10.1007/978-981-13-0074-5_10(107-117)Online publication date: 27-May-2018
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cover image ACM Conferences
HT '14: Proceedings of the 25th ACM conference on Hypertext and social media
September 2014
346 pages
ISBN:9781450329545
DOI:10.1145/2631775
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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Published: 01 September 2014

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Author Tags

  1. entity linking
  2. knowledge base
  3. wikipedia

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HT '14 Paper Acceptance Rate 49 of 86 submissions, 57%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%

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View all
  • (2022)Multi-source Representation Enhancement for Wikipedia-style Entity Annotation2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892289(01-08)Online publication date: 18-Jul-2022
  • (2019)XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and ApplicationData Intelligence10.1162/dint_a_000031:1(77-98)Online publication date: Mar-2019
  • (2018)Measuring Helpful Aspect of User Experience: The Development of Q-iCalHRegional Conference on Science, Technology and Social Sciences (RCSTSS 2016)10.1007/978-981-13-0074-5_10(107-117)Online publication date: 27-May-2018
  • (2017)XLink: An Unsupervised Bilingual Entity Linking SystemChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data10.1007/978-3-319-69005-6_15(172-183)Online publication date: 7-Oct-2017

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