A Topic Modeling Based Approach for Enhancing Corpus Querying

A Topic Modeling Based Approach for Enhancing Corpus Querying

Nouh Talal Alhindawi, Belal Abu Ata, Lana Mahmoud Obeidat, Mohammad Subhi Al-Batah, Muad Abu-Ata
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJOSSP.2019070103
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In information retrieval, the accuracy of the retrieval process is mainly dependent on query terms selection; therefore, the user must choose the needed terms carefully and selectively. Traditionally, the process of selecting query terms is done manually. However, in the last two decades, a lot of research has been directed towards automating the process of choosing and enhancing query terms. In this article, a new novel approach is presented, which relies on topic modeling in query building and expansion. Two open source systems were selected to perform the experiments, results show that adding the topic's term to the user's query clearly improves its quality and thus, improves the ranking results.
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In this section, we present an overview of the works related to query expansion. More specifically, we talk briefly about the enhancement achieved by other research. In Serizawa and Kobayashi (2013), the author used the topic distribution of the retrieved document for query expansion.

Typically, the process of query expansion is done by the user. The user investigates the retrieved relevant document in order to re-formulate his original query, this is called relevance feedback technique (RF) (Serizawa and Kobayashi, 2013). In the last two decades, a lot of research and approaches were presented to enhance the process of information retrieval by optimizing the process of query expansion (Alhindawi et al., 2013).

The authors He et al. (2009), presented an empirical study on which factors can affect the process of query expansion. Their results show that the first-pass retrieval list has only a reasonable impact on the usefulness of query expansion.

An approach for clinical document query expansion is presented in Serizawa and Kobayashi (2013), the author employed the synonym, topics, and a dictionary of clinical documents in order to expand query.

In Huang et al. (2007) the authors presented an approach for query expansion by focusing on categorizing the queries whose results are location-sensitive, their approach expanded the original query by using the terms of similar queries from similar locations.

An approach for enhancing the process of querying the biomedical database was presented by Vita et al. (2013), the authors presented an approach which builds ontology for biomedical investigations (OBI), the presented approach tried to enhance the functionality of search for a biomedical database.

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