Navegando por Palavras-chave "Word Sense Disambiguation"

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    Desambiguação de sentidos de palavras por meio de aprendizado semissupervisionado e word embeddings
    (Universidade Federal de São Paulo (UNIFESP), 2020-01-27) Sousa, Samuel Bruno Da Silva [UNIFESP]; Berton, Lilian [UNIFESP]; Universidade Federal de São Paulo
    Words naturally present more than one meaning and ambiguity is a recurrent feature in natural languages. Consequently, the task of Word Sense Disambiguation (WSD) aims at defining which word sense is the most adequate in a given context by using computers. WSD is one of the main problems in the field of Natural Language Processing (NLP) since many other tasks, such as Machine Translation and Information Retrieval, may have their results enhanced by accurate disambiguation systems. To solve this problem, several Machine Learning (ML) approaches have been used, such as unsupervised, supervised, and semi-supervised learning. However, the lack of labeled data to train supervised algorithms made models which combine labeled and unlabeled data in the learning process appear as a potential solution. Additionally, a comparative study of semi-supervised learning (SSL) approaches for WSD was not done before, as well as the combined employment of SSL algorithms with efficient word representations known as word embeddings, which became popular in the literature of NLP. Hence, the main goal of this work concerns the investigation of the performance of several semi-supervised algorithms applied to the problem of WSD, using word embeddings as features. To do so, four graph-based SSL algorithms were compared to each other on the main benchmark datasets for WSD. In order to check the word embeddings influence on the final results of the algorithms, six different setups for the Word2Vec model were trained and employed. The experimental results show that SSL models present competitive performances against supervised approaches, reaching over 80% of F1 score when only 25% of labeled data are input. Furthermore, these algorithms have the advantage of avoiding a new training step to classify new words.