CQL Grammars for Lexical and Semantic Information Extraction for Portuguese and Italian

Barbero, Chiara

doi:10.1007/978-3-030-98305-5_35

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2023

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“…In particular, the author focuses on the difference of an English 'word' used as the linguistic unit compared to the Chinese 'character' to express a complete meaning. Barbero (2022) presents a methodology that involves specialized corpora exploration in comparison to common language reference corpora. The authors base their work on the data collected from the compilation and treatment of a bilingual-European Portuguese/ Italian-comparable corpus of specialized texts on Public Art.…”

Section: Multilingualitymentioning

confidence: 99%

A systematic review of Automatic Term Extraction: What happened in 2022?

Nunzio

Marchesin

Silvello

2023

Digital Scholarship in the Humanities

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Automatic Term Extraction (ATE) systems have been studied for many decades as, among other things, one of the most important tools for tasks such as information retrieval, sentiment analysis, named entity recognition, and others. The interest in this topic has even increased in recent years given the support and improvement of the new neural approaches. In this article, we present a follow-up on the discussions about the pipeline that allows extracting key terms from medical reports, presented at MDTT 2022, and analyze the very last papers about ATE in a systematic review fashion. We analyzed the journal and conference papers published in 2022 (and partially in 2023) about ATE and cluster them into subtopics according to the focus of the papers for a better presentation.

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Section: Multilingualitymentioning

confidence: 99%