@inproceedings{chen-gao-2021-monotonicity,
title = "Monotonicity Marking from {U}niversal {D}ependency Trees",
author = "Chen, Zeming and
Gao, Qiyue",
editor = "Zarrie{\ss}, Sina and
Bos, Johan and
van Noord, Rik and
Abzianidze, Lasha",
booktitle = "Proceedings of the 14th International Conference on Computational Semantics (IWCS)",
month = jun,
year = "2021",
address = "Groningen, The Netherlands (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.iwcs-1.12",
pages = "121--131",
abstract = "Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics. However, there is hardly any work that connects dependency parsing to monotonicity, which is an essential part of logic and linguistic semantics. In this paper, we present a system that automatically annotates monotonicity information based on Universal Dependency parse trees. Our system utilizes surface-level monotonicity facts about quantifiers, lexical items, and token-level polarity information. We compared our system{'}s performance with existing systems in the literature, including NatLog and ccg2mono, on a small evaluation dataset. Results show that our system outperforms NatLog and ccg2mono.",
}
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<abstract>Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics. However, there is hardly any work that connects dependency parsing to monotonicity, which is an essential part of logic and linguistic semantics. In this paper, we present a system that automatically annotates monotonicity information based on Universal Dependency parse trees. Our system utilizes surface-level monotonicity facts about quantifiers, lexical items, and token-level polarity information. We compared our system’s performance with existing systems in the literature, including NatLog and ccg2mono, on a small evaluation dataset. Results show that our system outperforms NatLog and ccg2mono.</abstract>
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%0 Conference Proceedings
%T Monotonicity Marking from Universal Dependency Trees
%A Chen, Zeming
%A Gao, Qiyue
%Y Zarrieß, Sina
%Y Bos, Johan
%Y van Noord, Rik
%Y Abzianidze, Lasha
%S Proceedings of the 14th International Conference on Computational Semantics (IWCS)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Groningen, The Netherlands (online)
%F chen-gao-2021-monotonicity
%X Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics. However, there is hardly any work that connects dependency parsing to monotonicity, which is an essential part of logic and linguistic semantics. In this paper, we present a system that automatically annotates monotonicity information based on Universal Dependency parse trees. Our system utilizes surface-level monotonicity facts about quantifiers, lexical items, and token-level polarity information. We compared our system’s performance with existing systems in the literature, including NatLog and ccg2mono, on a small evaluation dataset. Results show that our system outperforms NatLog and ccg2mono.
%U https://aclanthology.org/2021.iwcs-1.12
%P 121-131
Markdown (Informal)
[Monotonicity Marking from Universal Dependency Trees](https://aclanthology.org/2021.iwcs-1.12) (Chen & Gao, IWCS 2021)
ACL
- Zeming Chen and Qiyue Gao. 2021. Monotonicity Marking from Universal Dependency Trees. In Proceedings of the 14th International Conference on Computational Semantics (IWCS), pages 121–131, Groningen, The Netherlands (online). Association for Computational Linguistics.