Abstract
Neural word embeddings have been widely used in modern NLP applications as they provide vector representation of words and capture the semantic properties of words and the linguistic relationship between the words. Many research groups have released their own version of word embeddings. However, they are trained on generic corpora, which limits their direct use for domain specific tasks. In this paper, we evaluate a set of pretrained word embeddings which were provided to us, on a standard NLP task - Sentiment Polarity Identification Task.
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Indurthi, V., Oota, S.R. (2018). Evaluating Quality of Word Embeddings with Sentiment Polarity Identification Task. In: Buscaldi, D., Gangemi, A., Reforgiato Recupero, D. (eds) Semantic Web Challenges. SemWebEval 2018. Communications in Computer and Information Science, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-00072-1_18
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DOI: https://doi.org/10.1007/978-3-030-00072-1_18
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