Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity

Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal, Madhavi Perera
2017 IEEE International Conference on Industrial and Information Systems (ICIIS)

Semantic similarity measures are an important part in Natural Language Processing tasks. However Semantic similarity measures built for general use do not perform well within specific domains. Therefore in this study we introduce a domain specific semantic similarity measure that was created by the synergistic union of word2vec, a word embedding method that is used for semantic similarity calculation and lexicon based (lexical) semantic similarity methods. We prove that this proposed methodology outperforms both, word embedding methods trained on a generic corpus and word embedding methods trained on a domain specific corpus, which do not use lexical semantic similarity methods to augment the results. Further, we prove that text lemmatization can improve the performance of word embedding methods.

Keywords: Natural Language Processing | Machine Learning / Deep Learning | Big Data | Law | Word Embedding | Semantic Similarity | Neural Networks | Lexicon | Word2vec |