Relevance-Ranked Domain-Specific Synonym Discovery

Interest in domain-specific search is growing rapidly, creating a need for domain-specific synonym discovery. The best-performing methods for this task rely on query logs and are thus difficult to use in many circumstances. We propose a method for domain-specific synonym discovery that requires only a domain-specific corpus. Our method substantially outperforms previously proposed methods in realistic evaluations. Due to the difficulty of identifying pairs of synonyms from among a large number of terms, methods have traditionally been evaluated by their ability to choose a target term's synonym from a small set of candidate terms. We generalize this evaluation by evaluating methods’ performance when required to choose a target term's synonym from progressively larger sets of candidate terms. We approach synonym discovery as a ranking problem and evaluate the methods' ability to rank a target term's candidate synonyms. Our results illustrate that while our proposed method substantially outperforms existing methods, synonym discovery is still a difficult task to automate and is best coupled with a human moderator.