Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents
Table 2: Mean average precision of tf-idf (TFIDF), BM25, word vector centroid (CENTROID) and our semantic approach (SEM) on the TREC set.
['[BOLD] Method', '[BOLD] TREC 2006', '[BOLD] TREC 2007']
CENTROID maps each query and document to a vector by taking a centroid of word embedding vectors, and the cosine similarity between two vectors is used for scoring and ranking documents. As mentioned earlier, this approach is not effective when multiple topics exist in a document. From the table, the embedding approach boosts the average precision of BM25 by 19% and 6% on TREC 2006 and 2007, respectively. However, CENTROID provides scores lower than BM25 and SEM approaches.