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.

In this paper, we present an analysis of feature extraction methods via dimensionality reduction for the task of biomedical Word Sense Disambiguation (WSD). We modify the vector representations in the 2-MRD WSD algorithm, and evaluate four dimensionality reduction methods: Word Embeddings using Continuous Bag of Words and Skip Gram, Singular Value Decomposition (SVD), and Principal Component Analysis (PCA). We also evaluate the effects of vector size on the performance of each of these methods. Results are evaluated on five standard evaluation datasets (Abbrev.100, Abbrev.200, Abbrev.300, NLM-WSD, and MSH-WSD). We find that vector sizes of 100 are sufficient for all techniques except SVD, for which a vector size of 1500 is preferred. We also show that SVD performs on par with Word Embeddings for all but one dataset.

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