Resolving Ambiguity in Hedge Detection by Automatic Generation of Linguistic Rules

Conference paper

Goodluck Constance, T., Bajaj, N., Rajwadi, M., Maltby, H., Wall, J., Moniri, M., Woodruff, C., Laird, T., Laird, J., Glackin, C. and Cannings, N. 2021. Resolving Ambiguity in Hedge Detection by Automatic Generation of Linguistic Rules. 30th International Conference on Artificial Neural Networks (ICANN). Online 14 - 17 Sep 2021 Springer. https://doi.org/10.1007/978-3-030-86383-8_30
AuthorsGoodluck Constance, T., Bajaj, N., Rajwadi, M., Maltby, H., Wall, J., Moniri, M., Woodruff, C., Laird, T., Laird, J., Glackin, C. and Cannings, N.
TypeConference paper
Abstract

An understanding of natural language is key in order to robustly extract the linguistic features indicative of deceptive speech. Hedging is a key indicator of deceptive speech as it can indicate a speaker's lack of commitment in a conversation. Hedging is characterised by words and phrases that display a sense of vagueness or that lack precision, such as suppose, about. The identifi cation of hedging terms in speech is a challenging task, due to the ambiguity of natural language, as a phrase can have multiple meanings. This paper proposes to automate the process of generating rules for hedge detection in transcripts produced by an automatic speech recognition system using explainable decision tree models trained on syntactic features. We have extracted syntactic features through dependency parsing to capture the grammatical relationship between hedging terms and their surrounding words based on meaning and context. We tested the effectiveness of our model on a dataset of conversational speech, for 75 different hedging terms, and achieved an F1 score of 0.88. The result of our automated process is comparable to existing solutions for hedge detection.

KeywordsHedge Detection; Resolving Ambiguity; Linguistic Indicators; Linguistic Cues
Year2021
Conference30th International Conference on Artificial Neural Networks (ICANN)
PublisherSpringer
Accepted author manuscript
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Anyone
Publication dates
Online07 Sep 2021
Publication process dates
Accepted15 Jun 2021
Deposited01 Jul 2021
Journal citationpp. 369-380
ISSN0302-9743
Book titleArtificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V
Book editorFarkaš, I.
Masulli, P.
Otte, S.
Wermter, S.
ISBN978-3-030-86383-8
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-86383-8_30
Web address (URL)https://www.springer.com/gb/book/9783030863821
Copyright holder© Springer Nature Switzerland AG 2021
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