Results 1 to 10 of about 1,768,887 (276)
Applied SciencesIn this paper, a contrastive learning approach for morphological disambiguation (MD) using large language models (LLMs) is presented. A contrastive loss function is introduced for training the approach, which reduces the distance between the correct ...
Gulmira Tolegen +2 more
doaj +4 more sources
Unsupervised Named Entity Disambiguation for Low Resource Domains [PDF]
arXivIn the ever-evolving landscape of natural language processing and information retrieval, the need for robust and domain-specific entity linking algorithms has become increasingly apparent.
Debarghya Datta, Soumajit Pramanik
semanticscholar +6 more sources
InformationIn 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.
Hlaudi Daniel Masethe +4 more
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Minimalist Entity Disambiguation for Mid-Resource Languages [PDF]
Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), 2023 ,
B. Kruit
semanticscholar +4 more sources
Exploiting a lexical resource for discourse connective disambiguation in German [PDF]
Proceedings of the 28th International Conference on Computational Linguistics, 2020 In this paper we focus on connective identification and sense classification for explicit discourse relations in German, as two individual sub-tasks of the overarching Shallow Discourse Parsing task.
Peter Bourgonje, Manfred Stede
semanticscholar +4 more sources
Data sets for author name disambiguation: an empirical analysis and a new resource [PDF]
Scientometrics, 2017 Data sets of publication meta data with manually disambiguated author names play an important role in current author name disambiguation (AND) research. We review the most important data sets used so far, and compare their respective advantages and shortcomings. From the results of this review, we derive a set of general requirements to future AND data
Mark-Christoph Müller +2 more
semanticscholar +6 more sources
Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource [PDF]
arXiv.orgHuman language, while aimed at conveying meaning, inherently carries ambiguity. It poses challenges for speech and language processing, but also serves crucial communicative functions.
Pablo Ortega +4 more
semanticscholar +4 more sources
A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages [PDF]
arXiv, 2013 Word Sense Disambiguation (WSD), the process of automatically identifying the meaning of a polysemous word in a sentence, is a fundamental task in Natural Language Processing (NLP). Progress in this approach to WSD opens up many promising developments in the field of NLP and its applications.
Mohammad Nasiruddin
arxiv +5 more sources
Web Resource Sense Disambiguation in Web of Data
Journal of universal computer science (Online), 2013 This paper introduces the use of WordNet as a resource for RDF web resources sense disambiguation in Web of Data and shows the role of designed system in interlinking datasets in Web of Data and word sense disambiguation scope.
Farzam Matinfar +2 more
semanticscholar +4 more sources
A Literature Survey on Word Sense Disambiguation for the Hindi Language
Information, 2023 Word sense disambiguation (WSD) is a process used to determine the most appropriate meaning of a word in a given contextual framework, particularly when the word is ambiguous.
Vinto Gujjar +5 more
doaj +2 more sources