PDF] Query Rewriting using Automatic Synonym Extraction for E

Por um escritor misterioso

Last updated 10 novembro 2024

PDF] Query Rewriting using Automatic Synonym Extraction for E

One version of the approaches to query rewriting taken at search is described, using a machine learned binary classifier trained on human judged binary relevance labels to filter the candidate synonyms that are truly useful as query expansions without compromising result set precision. Query rewriting is a critical component in modern search engines. It is the process of altering and enhancing raw user queries using synonymous keywords or structured metadata to improve search recall and relevance using data mining techniques applied on tex-tual data and user behavioral signals. For example, the query bicycle is rewritten to match (bicycle OR bike) - i.e. all items that either contain the word bicycle or bike in their title are returned for this query. Choosing the right set of synonymous terms for a given query term is essential to ensure the quality of search results, especially in the context of e-commerce where buyer needs can be very specific. As an example, shoe is a good synonym for shoes , whereas sandals , while related, is not a good synonym for shoes . In this work, we describe one version of the approaches to query rewriting taken at search. At a high level, we use a two step process to generate and apply synonyms for query expansions - 1. offline token level synonym generation and 2. runtime search query rewriting. In the offline phase, we first generate a large pool of candidate synonyms for query tokens using various techniques leveraging user behavioral data, inventory and taxonomy information and open source knowledge bases; then, we leverage a machine learned binary classifier trained on human judged binary relevance labels to filter the candidate synonyms that are truly useful as query expansions without compromising result set precision; this classifier allows us to leverage a wide variety of sources and techniques to generate synonym candidates by providing a scientific and scalable method to evaluate their effectiveness for query rewriting. This filtered set of token level synonyms is stored in a dictionary for runtime query rewriting. In the online phase, we rewrite user search queries by combining the token level synonyms in the dictionary, creating a boolean recall expression. We empirically demonstrate the value of this approach to enhance e-commerce search recall and relevance.