Relevance Ranking Based on Query-Aware Context Analysis

Abstract

Word mismatch between queries and documents is a longstanding 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.

Natural Language Processing for Smart Healthcare

“…Information retrieval (IR) obtains materials that meet the query requirements from numerous documents, and is a core of search engines for all applications. To ease the retrieval process [80], [81], improve the relevance and diversity of the retrieval [82]- [84] or reduce the query time [85], current works aim to develop fast and efficient information retrieval methods to obtain useful retrieval from a large collection of data sources, ranging from internal health information system (HIS) systems and other digital documents to online resources.…”