Relevance Ranking Based on Query-Aware Context Analysis

Word mismatch between queries and documents is a long-standing challenge in information retrieval. Recent advances in distributed word representations address the word mismatch problem by enabling semantic matching. However, most existing models rank documents based on semantic matching between query and document terms without an explicit understanding of the relationship of the match to relevance. To consider semantic matching between query and document, we propose an unsupervised semantic matching model by simulating a user who makes relevance decisions. The primary goal of the proposed model is to combine the exact and semantic matching between query and document terms, which has been shown to produce effective performance in information retrieval. As semantic matching between queries and entire documents is computationally expensive, we propose to use local contexts of query terms in documents for semantic matching. Matching with smaller query-related contexts of documents stems from the relevance judgment process recorded by human observers. The most relevant part of a document is then recognized and used to rank documents with respect to the query. Experimental results on several representative retrieval models and standard datasets show that our proposed semantic matching model significantly outperforms competitive baselines in all measures.