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Título del libro: 14th International Workshops On Semantic Evaluation, Semeval 2020 - Co-Located 28th International Conference On Computational Linguistics, Coling 2020, Proceedings
Título del capítulo: MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual Word Similarity Using a Centroid based Approach and Word Embeddings

Autores UNAM:
HELENA MONTSERRAT GOMEZ ADORNO; GEMMA BEL ENGUIX; LUIS RAMON CASILLAS PEREZ SOTO;
Autores externos:

Idioma:

Año de publicación:
2020
Palabras clave:

Computational linguistics; Semantics; Contextual words; Croatians; Embeddings; Finnish; Human perception; Language model; Similarity of words; Subtask; Word similarity; Word vectors; Forecasting


Resumen:

This paper presents our systems to solve Task 3 of Semeval-2020, which aims to predict the effect that context has on human perception of similarity of words. The task consists of two subtasks in English, Croatian, Finnish and Slovenian: (1) predicting the change of similarity, and (2) predicting the human scores of similarity, both of them for a pair of words within two different contexts. We tackled the problem by developing two systems, the first one uses a centroid approach and word vectors. The second one uses the ELMo language model, which is trained for each pair of words with the given context. Our approach achieved the highest score in subtask 2 for the English language. © 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.


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