Towards Word Sense Disambiguation for Latvian

Paikens, Peteris; Rituma, Laura; Pretkalniņa, Lauma

doi:10.22364/bjmc.2022.10.3.13

Cited by 2 publications

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“…Tālāk teksti tiek automātiski morfoloģiski analizēti, izmantojot atvērtā pirmkoda morfoloģisko marķētāju, kas sasniedz 92,7 % precizitāti morfoloģiskajām pazīmēm (tagam) un 97,6 % precizitāti pamatformu un vārdšķiru noteikšanai (Paikens et al, 2013;Paikens, 2016). Korpusu morfoloģiskajā marķēšanā izmantota LU MII izstrādātā un latviešu valodai pielāgotā morfoloģisko pazīmju kopa 6 , 5 kas pamatā atbilst MULTEXT-EAST standartam (Erjavec, 2012).…”

Section: Korpusu Gramatiskais Marķējums: Morfoloģija Un Universālās A...unclassified

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2024

LINGUALET

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Section: Korpusu Gramatiskais Marķējums: Morfoloģija Un Universālās A...unclassified

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2024

LINGUALET

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“…In [11], the focus was on the Baltic Language of Latvia, the Latvian language, where they presented the recent scenario on word sense disambiguation, reviewing the available data and possible obstacles, exposing the use of BERT through word sense disambiguation methods in the Latvian language. The authors recently conducted the training using a dataset developed with example sentence examples of meanings.…”

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A Word Sense Disambiguation Method Applied to Natural Language Processing for the Portuguese Language

do Nascimento,

Garcia,

Araújo

2024

IEEE Open J. Comput. Soc.

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Natural language processing (NLP) and artificial intelligence (AI) have advanced significantly in recent years, enabling the development of various tasks, such as machine translation, text summarization, sentiment analysis, and speech analysis. However, there are still challenges to overcome, such as natural language ambiguity. One of the problems caused by ambiguity is the difficulty of determining the proper meaning of a word in a specific context. For example, the word "mouse" can mean a computer peripheral or an animal, depending on the context. This limitation can lead to an incorrect semantic interpretation of the processed sentence. In recent years, language models (LMs) have provided a new impetus to NLP and AI, including in the task of word sense disambiguation (WSD). LMs are capable of learning and generating texts as they are trained on large amounts of data. However, in the Portuguese language, there are still few studies on WSD using LMs. Given this scenario, this article presents a method for WSD for the Portuguese language. To do this, it uses the BERTimbau language model, which is specific to the Portuguese. The results will be evaluated using the metrics established in the literature.

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Towards Word Sense Disambiguation for Latvian

Cited by 2 publications

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A Word Sense Disambiguation Method Applied to Natural Language Processing for the Portuguese Language

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