Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation [PDF]
arXiv, 2023 The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have been developed to overcome that challenge without relying on annotated data.
arxiv
Lexical Chain dan Word Sense Disambiguation Untuk Peringkasan Artikel Berbahasa Indonesia
Techno.Com, 2017 Text Summarization adalah sebuah proses untuk menghasilkan ringkasan suatu dokumen dengan tidak menghilangkan informasi utama dari artikel. Ada beberapa metode untuk melakukan peringkasan, seperti metode rantai leksikal atau lexical chain yang memiliki ...
Dika Muhammad Fazar +1 more
doaj
Analysis and Evaluation of Language Models for Word Sense Disambiguation
Computational Linguistics, 2021 Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context ...
Daniel Loureiro +3 more
doaj +1 more source
Language models in word sense disambiguation for Polish [PDF]
arXiv, 2021 In the paper, we test two different approaches to the {unsupervised} word sense disambiguation task for Polish. In both methods, we use neural language models to predict words similar to those being disambiguated and, on the basis of these words, we predict the partition of word senses in different ways. In the first method, we cluster selected similar
arxiv
Word Sense Disambiguation Based on RegNet With Efficient Channel Attention and Dilated Convolution
IEEE Access, 2023 Word sense disambiguation (WSD) is one of key problems in field of natural language processing. Ambiguous word often has different meanings in different contexts.
Chun-Xiang Zhang +2 more
doaj +1 more source
A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph [PDF]
arXiv, 2021 Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering.
arxiv
An Approach to Speed-up the Word Sense Disambiguation Procedure through Sense Filtering [PDF]
, 2015 In this paper, we are going to focus on speed up of the Word Sense Disambiguation procedure by filtering the relevant senses of an ambiguous word through Part-of-Speech Tagging. First, this proposed approach performs the Part-of-Speech Tagging operation before the disambiguation procedure using Bigram approximation.
arxiv +1 more source
Word Sense Disambiguation using WSD specific Wordnet of Polysemy Words [PDF]
arXiv, 2014 This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy word based on the clue words. The related words for each sense of a polysemy word as well as single sense word are referred to as the clue words. The conventional WordNet organizes nouns, verbs, adjectives and adverbs together into sets of synonyms ...
arxiv
Decomposing Word Embedding with the Capsule Network [PDF]
arXiv, 2020 Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-embeddings of word did not explore the power of the unsupervised word embedding sufficiently.
arxiv
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation [PDF]
arXiv, 2023 The goal of visual word sense disambiguation is to find the image that best matches the provided description of the word's meaning. It is a challenging problem, requiring approaches that combine language and image understanding. In this paper, we present our submission to SemEval 2023 visual word sense disambiguation shared task.
arxiv