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Conversational Entity Linking: Problem Definition and Datasets

Authors:

Faegheh Hasibi,

Krisztian Balog,

Arjen P. de VriesAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2390 - 2397

https://doi.org/10.1145/3404835.3463258

Published: 11 July 2021 Publication History

Abstract

Machine understanding of user utterances in conversational systems is of utmost importance for enabling engaging and meaningful conversations with users. Entity Linking (EL) is one of the means of text understanding, with proven efficacy for various downstream tasks in information retrieval. In this paper, we study entity linking for conversational systems. To develop a better understanding of what EL in a conversational setting entails, we analyze a large number of dialogues from existing conversational datasets and annotate references to concepts, named entities, and personal entities using crowdsourcing. Based on the annotated dialogues, we identify the main characteristics of conversational entity linking. Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting. The resources released with this paper include annotated datasets, detailed descriptions of crowdsourcing setups, as well as the annotations produced by various EL systems. These new resources allow for an investigation of how the role of entities in conversations is different from that in documents or isolated short text utterances like queries and tweets, and complement existing conversational datasets.

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Cited By

Sekulic IBalog KCrestani F(2024)Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational SearchProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638300(209-218)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638300
Mo FNie JHuang KMao KZhu YLi PLiu YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Learning to Relate to Previous Turns in Conversational SearchProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599411(1722-1732)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599411
Iurshina APan JBoutalbi RStaab SAl Hasan MXiong L(2022)NILK: Entity Linking Dataset Targeting NIL-linking CasesProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557659(4069-4073)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557659
Show More Cited By

Index Terms

Conversational Entity Linking: Problem Definition and Datasets
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction
      2. Question answering
    2. Users and interactive retrieval

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Sekulic IBalog KCrestani F(2024)Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational SearchProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638300(209-218)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638300
Mo FNie JHuang KMao KZhu YLi PLiu YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Learning to Relate to Previous Turns in Conversational SearchProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599411(1722-1732)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599411
Iurshina APan JBoutalbi RStaab SAl Hasan MXiong L(2022)NILK: Entity Linking Dataset Targeting NIL-linking CasesProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557659(4069-4073)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557659
Chakraborty PDutta SSanyal D(2022)Personal Research Knowledge GraphsCompanion Proceedings of the Web Conference 202210.1145/3487553.3524654(763-768)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524654
Christmann PSaha Roy RWeikum GAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Conversational Question Answering on Heterogeneous SourcesProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531815(144-154)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531815
Xu ZChen YShi SHu B(2022)Enhancing Entity Linking with Contextualized Entity EmbeddingsNatural Language Processing and Chinese Computing10.1007/978-3-031-17189-5_19(228-239)Online publication date: 24-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17189-5_19

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