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Evaluating Feature Extraction Methods For Biomedical Word Sense Disambiguation, Clint A. Cuffy, Sam Henry, Bridget T. Mcinnes

Undergraduate Research Posters

Evaluating Feature Extraction Methods for Biomedical WSD

Clint Cuffy, Sam Henry and Bridget McInnes, PhD

Virginia Commonwealth University, Richmond, Virginia, USA

Introduction. Biomedical text processing is currently a high active research area but ambiguity is still a barrier to the processing and understanding of these documents. Many word sense disambiguation (WSD) approaches represent instances of an ambiguous word as a distributional context vector. One problem with using these vectors is noise -- information that is overly general and does not contribute to the word’s representation. Feature extraction approaches attempt to compensate for sparsity and reduce noise by transforming the data …

Vector Representations Of Multi-Word Terms For Semantic Relatedness, Clint A. Cuffy, Sam Henry, Bridget T. Mcinnes

Undergraduate Research Posters

Vector Representations of Multi-Word Terms for Semantic Relatedness

Sam Henry, Clint Cuffy and Bridget T. McInnes, PhD

Introduction: Semantic similarity and relatedness measures quantify the degree to which two concepts are similar (e.g. liver-organ) or related (e.g. headache-aspirin). These metrics are critical to improving many natural language processing tasks involving retrieval and clustering of biomedical and clinical documents and developing biomedical terminologies and ontologies. Numerous ways exist to quantify these measures between distributional context vectors but no direct comparison between these metrics and exploration of representing multi-word context vectors. We explore several multi-word aggregation methods of distributional context vectors for …