Vector representations of multi-word terms for semantic relatedness

• Published in: Journal of biomedical informatics Vol. 77; pp. 111 - 119
• Main Authors: Henry, Sam, Cuffy, Clint, McInnes, Bridget T.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2018
• Subjects: Biomedical Research; Distributional similarity; Humans; Machine Learning - standards; Natural Language Processing; Reproducibility of Results; Semantic similarity and relatedness; Semantics; Unified Medical Language System; 99-00; Natural language processing; Semantic similarity and relatedness; 00-01; Distributional similarity
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2017.12.006

[Display omitted] •Multi-word term aggregation methods of distributional context vectors are compared.•Vectors are applied to semantic similarity and relatedness tasks.•Several dimensionality reduction techniques and vector dimensionalities are compared.•State of the art results achieved and compared to similar authors’ works. This paper presents a comparison between several multi-word term aggregation methods of distributional context vectors applied to the task of semantic similarity and relatedness in the biomedical domain. We compare the multi-word term aggregation methods of summation of component word vectors, mean of component word vectors, direct construction of compound term vectors using the compoundify tool, and direct construction of concept vectors using the MetaMap tool. Dimensionality reduction is critical when constructing high quality distributional context vectors, so these baseline co-occurrence vectors are compared against dimensionality reduced vectors created using singular value decomposition (SVD), and word2vec word embeddings using continuous bag of words (CBOW), and skip-gram models. We also find optimal vector dimensionalities for the vectors produced by these techniques. Our results show that none of the tested multi-word term aggregation methods is statistically significantly better than any other. This allows flexibility when choosing a multi-word term aggregation method, and means expensive corpora preprocessing may be avoided. Results are shown with several standard evaluation datasets, and state of the results are achieved.