关键词: 近义词/同义词, 查询短语, 语义相似性, 上下文, 评分函数

Abstract:

In order to find near-synonyms/synonyms phrases in large corpus, a near-synonym/synonym phrase finding model based on common surrounding context is proposed in this paper. It captures semantic similarity via [n]-gram distribu-
ted method, and implicitly preserves local syntactic structure without parsing, making the underlying method language independent. The specific implementation is divided into two phases：The first phase is context collection and filtering, that is, it uses the local contexts surrounding the query phrase as features to the conditional model to capture both semantic and syntactic information. The second phase is the collection and screening of candidate phrases, that is, it iterates over all the instances of each “left”, “right” and “pairing” in the data to collect a set of near-synonym/synonym candidate phrases. And the elements that make up the model and the scoring functions used to evaluate the performance of the model are also given. The experimental results based on different large corpus show that the proposed modeling method is superior to other common finding method models in terms of statistical scoring finding performance and overall scalability.

Key words: near-synonyms/synonyms, query phrases, semantic similarity, context, scoring function