Naslov
Automatic Predicate Sense Disambiguation Using Syntactic and Semantic Features

Autori
Žitko, Branko ; Bročić, Lucija ; Gašpar, Angelina ; Grubišić, Ani ; Vasić, Daniel ; Ines Šarić-Grgić

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the Conference on Language Technologies and Digital Humanities / - , 2022, 227-234

Skup
Language Technologies and Digital Humanities Conference

Mjesto i datum
Ljubljana, Slovenija, 15.09.2022. - 16.09.2022

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
predicate sense disambiguation ; multiple multiclass classifiers ; light verb construction

Sažetak
This paper focuses on Predicate Sense Disambiguation (PSD) based on PropBank guidelines. Different approaches to this task have been proposed, from purely supervised or knowledge-based, to recently hybrid approaches that have shown promising results. We introduce one of the hybrid approaches - a PSD pipeline based on both supervised models and handcrafted rules. To train three supervised POS, DEP and POS DEP models we used syntactic features (lemma, part-of-speech tag, dependency parse) and semantic features (semantic role labels). These features enable per-token classification, which to be applied to unseen words, requires handcrafted rules to make predictions specifically for nouns in light verb constructions, unseen verbs and unseen phrasal verbs. Experiments were done on newly- developed dataset and the results show a token-level accuracy of 96% for the proposed PSD pipeline.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA