Abstract
Previous researches on semantic similarity calculating have been mainly focused on documents, sentences or concepts. In this paper, we study the semantic similarity of words and compositional phrases. The task is to judge the semantic similarity of a word and a short sequence of words. Based on structured resource (WordNet), semi-structured resource (Wikipedia) and unstructured resource (Web), this paper extracts rich effective features to represent the word-phrase pair. The task can be treated as a binary classification problem and we employ Support Vector Machine to estimate whether the word and phrase is similar given a word-phrase pair. Experiments are conducted on SemEval 2013 Task5a. Our method achieves 82.9% in accuracy, and outperforms the best system (80.3%) that participates in the task. Experimental results demonstrate the effectiveness of our proposed approach.
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Jin, X., Sun, C., Lin, L., Wang, X. (2014). Exploiting Multiple Resources for Word-Phrase Semantic Similarity Evaluation. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_5
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DOI: https://doi.org/10.1007/978-3-319-12277-9_5
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