Zobrazeno 1 - 10
of 3 593
pro vyhledávání: '"sense disambiguation"'
Publikováno v:
Cogent Arts & Humanities, Vol 11, Iss 1 (2024)
Understanding queer language, which includes knowing its meaning and its applications, is essential not only for understanding sexuality and fostering language inclusivity but also for acknowledging the multifaceted uses of language across all societ
Externí odkaz:
https://doaj.org/article/7943bf7d88b04b3b82e6eeea0bb05c01
Publikováno v:
Journal of Language Modelling, Vol 12, Iss 1 (2024)
We develop a three-part approach to Verb Sense Disambiguation (VSD) in German. After considering a set of lexical resources and corpora, we arrive at a statistically motivated selection of a subset of verbs and their senses from GermaNet. This sub-in
Externí odkaz:
https://doaj.org/article/2665a3794f3c448d895e66686668168f
Autor:
Sanaa Kaddoura, Reem Nassar
Publikováno v:
Data in Brief, Vol 55, Iss , Pp 110591- (2024)
This data paper introduces a comprehensive dataset tailored for word sense disambiguation tasks, explicitly focusing on a hundred polysemous words frequently employed in Modern Standard Arabic. The dataset encompasses a diverse set of senses for each
Externí odkaz:
https://doaj.org/article/113e075898734230b67846a2e7197319
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract This study aims to explore the research methodology of applying the Transformer model algorithm to Chinese word sense disambiguation, seeking to resolve word sense ambiguity in the Chinese language. The study introduces deep learning and des
Externí odkaz:
https://doaj.org/article/62a02354bfe345e7907b9f95f1240fc1
Autor:
Woon-Kyo Lee, Ja-Hee Kim
Publikováno v:
Tehnički Vjesnik, Vol 31, Iss 6, Pp 1845-1858 (2024)
Abbreviation ambiguity poses significant challenges when searching academic literature. This study evaluated the accuracy of clustering algorithms on imbalanced datasets with varying ratios of target groups. A corpus consisting of 1052 papers focused
Externí odkaz:
https://doaj.org/article/57e6e7c7a39e4f0d9509280604edd9cc
Publikováno v:
IEEE Access, Vol 12, Pp 126329-126343 (2024)
Ambiguity in word meanings presents a significant challenge in natural language processing, necessitating robust techniques for Word Sense Disambiguation (WSD). While research in WSD has predominantly focused on widely spoken languages like English a
Externí odkaz:
https://doaj.org/article/f56667915771436091cf0587f1f41391
Publikováno v:
IEEE Open Journal of the Computer Society, Vol 5, Pp 268-277 (2024)
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. Howev
Externí odkaz:
https://doaj.org/article/5e952fa903b9469d9aece604050fa438
Autor:
Geeraerts, Dirk, author, Speelman, Dirk, author, Heylen, Kris, author, Montes, Mariana, author, De Pascale, Stefano, author, Franco, Karlien, author, Lang, Michael, author
Publikováno v:
Lexical Variation and Change : A Distributional Semantic Approach, 2023, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780198890676.003.0010
Autor:
Hlaudi Daniel Masethe, Mosima Anna Masethe, Sunday Olusegun Ojo, Fausto Giunchiglia, Pius Adewale Owolawi
Publikováno v:
Information, Vol 15, Iss 9, p 540 (2024)
In natural language processing, word sense disambiguation (WSD) continues to be a major difficulty, especially for low-resource languages where linguistic variation and a lack of data make model training and evaluation more difficult. The goal of thi
Externí odkaz:
https://doaj.org/article/59ff08e378a34bed97fc2430a5705f86
Publikováno v:
Frontiers in Digital Health, Vol 6 (2024)
Clinical narratives commonly use acronyms without explicitly defining their long forms. This makes it difficult to automatically interpret their sense as acronyms tend to be highly ambiguous. Supervised learning approaches to their disambiguation in
Externí odkaz:
https://doaj.org/article/0631eecbf4cb44608b4b52920bd2108c