Explainable lexical entailment with semantic graphs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AYTUIB3G3" target="_blank" >RIV/00216208:11320/22:YTUIB3G3 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/23:TKQPFYTR

Výsledek na webu

<a href="https://www.cambridge.org/core/journals/natural-language-engineering/article/explainable-lexical-entailment-with-semantic-graphs/96A9C7F5B30D7DD5A955B54D9AF8ADF4" target="_blank" >https://www.cambridge.org/core/journals/natural-language-engineering/article/explainable-lexical-entailment-with-semantic-graphs/96A9C7F5B30D7DD5A955B54D9AF8ADF4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/S1351324922000092" target="_blank" >10.1017/S1351324922000092</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Explainable lexical entailment with semantic graphs

Popis výsledku v původním jazyce

We present novel methods for detecting lexical entailment in a fully rule-based and explainable fashion, by automatic construction of semantic graphs, in any language for which a crowd-sourced dictionary with sufficient coverage and a dependency parser of sufficient accuracy are available. We experiment and evaluate on both the Semeval-2020 lexical entailment task (Glavaš et al. (2020). Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 24–35) and the SherLIiC lexical inference dataset of typed predicates (Schmitt and Schütze (2019). Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 902–914). Combined with top-performing systems, our method achieves improvements over the previous state-of-the-art on both benchmarks. As a standalone system, it offers a fully interpretable model of lexical entailment that makes detailed error analysis possible, uncovering future directions for improving both the semantic parsing method and the inference process on semantic graphs. We release all components of our system as open source software.

Název v anglickém jazyce

Explainable lexical entailment with semantic graphs

Popis výsledku anglicky

We present novel methods for detecting lexical entailment in a fully rule-based and explainable fashion, by automatic construction of semantic graphs, in any language for which a crowd-sourced dictionary with sufficient coverage and a dependency parser of sufficient accuracy are available. We experiment and evaluate on both the Semeval-2020 lexical entailment task (Glavaš et al. (2020). Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 24–35) and the SherLIiC lexical inference dataset of typed predicates (Schmitt and Schütze (2019). Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 902–914). Combined with top-performing systems, our method achieves improvements over the previous state-of-the-art on both benchmarks. As a standalone system, it offers a fully interpretable model of lexical entailment that makes detailed error analysis possible, uncovering future directions for improving both the semantic parsing method and the inference process on semantic graphs. We release all components of our system as open source software.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Natural Language Engineering

ISSN

1351-3249

e-ISSN

1469-8110

Svazek periodika

—

Číslo periodika v rámci svazku

2022-2-28

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

1-24

Kód UT WoS článku

000762096000001

EID výsledku v databázi Scopus

2-s2.0-85125792088