research-article

Implicit entity linking in tweets: : An ad-hoc retrieval approach

Authors: Hawre Hosseini, Tam T. Nguyen, Jimmy Wu, Ebrahim BagheriAuthors Info & Claims

Applied Ontology, Volume 14, Issue 4

Pages 451 - 477

https://doi.org/10.3233/AO-190215

Published: 01 January 2019 Publication History

Abstract

Within the context of Twitter analytics, the notion of implicit entity linking has recently been introduced to refer to the identification of a named entity, which is central to the topic of the tweet, but whose surface form is not present in the tweet itself. Compared to traditional forms of entity linking where the linking process revolves around an identified surface form of a potential entity, implicit entity linking relies on contextual clues to determine whether an implicit entity is present within a given tweet and if so, which entity is being referenced. The objective of this paper, while introducing and publicly sharing a comprehensive gold standard dataset for implicit entity linking, is to perform the task of implicit entity linking. The dataset consists of 7,870 tweets, which are classified as either containing implicit entities, explicit entities, both, or neither. The implicit entities are then linked to three levels of entities on Wikipedia, namely coarse-grained level, e.g., Person, Fine-grained level, e.g., Comedian, and the actual entity, e.g., Seinfeld. The proposed model in this work formulates the problem of implicit entity linking as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of textual descriptions of entities in the knowledge base. The novel contributions of our work include: 1) designing and collecting a gold standard dataset for the task of implicit entity linking; 2) defining the implicit entity linking process as an ad-hoc document retrieval task; and 3) proposing a neural embedding-based feature function that is interpolated with prior term dependency and entity-based feature functions to enhance implicit entity linking. We systematically compare our work with existing work in this area and show that our method is able to provide improvements on a number of retrieval measures.

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Cited By

Arabzadeh NBigdeli ABagheri E(2024)LaQuE: Enabling Entity Search at ScaleAdvances in Information Retrieval10.1007/978-3-031-56060-6_18(270-285)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_18
Feher DIbrahim FCheng ZSchlegel VMaidment TBagshaw JBatista-Navarro R(2023)Few-shot entity linking of food namesInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346360:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103463
Hosseini HMansouri MBagheri E(2022)A systemic functional linguistics approach to implicit entity recognition in tweetsInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10295759:4Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.102957
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Index Terms

Implicit entity linking in tweets: An ad-hoc retrieval approach
1. Information systems
  1. Information retrieval
  2. Information systems applications

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cover image Applied Ontology

Applied Ontology Volume 14, Issue 4

Meaning in Context: Ontologically and linguistically motivated representations of objects and events

2019

140 pages

ISSN:1570-5838

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© 2019 – IOS Press and the authors. All rights reserved.

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IOS Press

Netherlands

Publication History

Published: 01 January 2019

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Cited By

Arabzadeh NBigdeli ABagheri E(2024)LaQuE: Enabling Entity Search at ScaleAdvances in Information Retrieval10.1007/978-3-031-56060-6_18(270-285)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_18
Feher DIbrahim FCheng ZSchlegel VMaidment TBagshaw JBatista-Navarro R(2023)Few-shot entity linking of food namesInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10346360:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103463
Hosseini HMansouri MBagheri E(2022)A systemic functional linguistics approach to implicit entity recognition in tweetsInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10295759:4Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.102957
Hosseini HBagheri E(2021)Learning to rank implicit entities on TwitterInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10250358:3Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.ipm.2021.102503
Huang LYuan BZhang RLu QHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Towards Linking Camouflaged Descriptions to Implicit Products in E-commerceProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401067(901-910)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401067
Basile VCaselli TRadicioni DBasile VCaselli TRadicioni D(2019)Meaning in ContextApplied Ontology10.3233/AO-19022114:4(335-341)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/AO-190221

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