Large sense-annotated datasets are increasingly necessary for training deep supervised systems in Word Sense Disambiguation. However, gathering high-quality sense-annotated data for as many instances as possible is a laborious and expensive task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this short survey we present an overview of sense-annotated corpora, annotated either manually- or (semi)automatically, that are currently available for different languages and featuring distinct lexical resources as inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide the reader with general statistics of each dataset and an analysis of their specific features.

A Short Survey on Sense-Annotated Corpora / Pasini, Tommaso; camacho-collados, Jose. - (2020), pp. 5759-5765. (Intervento presentato al convegno LREC 2020 tenutosi a Marseille).

A Short Survey on Sense-Annotated Corpora

pasini tommaso;camacho-collados jose
2020

Abstract

Large sense-annotated datasets are increasingly necessary for training deep supervised systems in Word Sense Disambiguation. However, gathering high-quality sense-annotated data for as many instances as possible is a laborious and expensive task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this short survey we present an overview of sense-annotated corpora, annotated either manually- or (semi)automatically, that are currently available for different languages and featuring distinct lexical resources as inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide the reader with general statistics of each dataset and an analysis of their specific features.
2020
LREC 2020
Survey, Word Sense Disambiguation, Natural Language Processing
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A Short Survey on Sense-Annotated Corpora / Pasini, Tommaso; camacho-collados, Jose. - (2020), pp. 5759-5765. (Intervento presentato al convegno LREC 2020 tenutosi a Marseille).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1431884
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