Coupled intrinsic and extrinsic human language resource-based query expansion

• URL: http://arxiv.org/abs/2004.11083v1
• Date: Thu, 23 Apr 2020 11:22:38 GMT
• Title: Coupled intrinsic and extrinsic human language resource-based query expansion
• Authors: Bhawani Selvaretnam, Mohammed Belkhatir
• Abstract summary: We present here a query expansion framework which capitalizes on both linguistic characteristics for query constituent encoding, expansion concept extraction and concept weighting. A thorough empirical evaluation on real-world datasets validates our approach against unigram language model, relevance model and a sequential dependence based technique.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Poor information retrieval performance has often been attributed to the query-document vocabulary mismatch problem which is defined as the difficulty for human users to formulate precise natural language queries that are in line with the vocabulary of the documents deemed relevant to a specific search goal. To alleviate this problem, query expansion processes are applied in order to spawn and integrate additional terms to an initial query. This requires accurate identification of main query concepts to ensure the intended search goal is duly emphasized and relevant expansion concepts are extracted and included in the enriched query. Natural language queries have intrinsic linguistic properties such as parts-of-speech labels and grammatical relations which can be utilized in determining the intended search goal. Additionally, extrinsic language-based resources such as ontologies are needed to suggest expansion concepts semantically coherent with the query content. We present here a query expansion framework which capitalizes on both linguistic characteristics of user queries and ontology resources for query constituent encoding, expansion concept extraction and concept weighting. A thorough empirical evaluation on real-world datasets validates our approach against unigram language model, relevance model and a sequential dependence based technique.

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