A Systematic Study of Semantic Vector Space Model Parameters

摘要

We present a systematic study of parameters used in the construction of semantic vector space models. Evaluation is carried out on a variety of similarity tasks, including a compositionality dataset, using several source corpora. In addition to recommendations for optimal parameters, we present some novel findings, including a similarity metric that outperforms the alternatives on all tasks considered.