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Support for Interactive Identification of Mentioned Entities in Conversational Speech

Authors:

Ning Gao,

Douglas W. Oard,

Mark DredzeAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 953 - 956

https://doi.org/10.1145/3077136.3080688

Published: 07 August 2017 Publication History

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Abstract

Searching conversational speech poses several new challenges, among which is how the searcher will make sense of what they find. This paper describes our initial experiments with a freely available collection of Enron telephone conversations. Our goal is to help the user make sense of search results by finding information about mentioned people, places and organizations. Because automated entity recognition is not yet sufficiently accurate on conversational telephone speech, we ask the user to transcribe just the name, and to indicate where in the recording it was heard. We then seek to link that mention to other mentions of the same entity in a variety of sources (in our experiments, in email and in Wikipedia). We cast this as an entity linking problem, and achieve promising results by utilizing social network features to help compensate for the limited accuracy of automatic transcription for this challenging content.

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Cited By

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Gao NSell GOard DDredze M(2017)Leveraging side information for speaker identification with the Enron conversational telephone speech collection2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)10.1109/ASRU.2017.8268988(577-583)Online publication date: Dec-2017
https://doi.org/10.1109/ASRU.2017.8268988

Index Terms

Support for Interactive Identification of Mentioned Entities in Conversational Speech
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Task models

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Published In

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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National Science Foundation

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

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

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Gao NSell GOard DDredze M(2017)Leveraging side information for speaker identification with the Enron conversational telephone speech collection2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)10.1109/ASRU.2017.8268988(577-583)Online publication date: Dec-2017
https://doi.org/10.1109/ASRU.2017.8268988

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Recommendations

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NEREL: a Russian information extraction dataset with rich annotation for nested entities, relations, and wikidata entity links

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