Prospecting the Effect of Topic Modeling in Information Retrieval

Aakanksha Sharaff; Dilip Singh Sisodia; Jitesh Kumar Dewangan

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Prospecting the Effect of Topic Modeling in Information Retrieval

机译：Prospecting the Effect of Topic Modeling in Information Retrieval

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Enormous records and data are gathered every day. Organization of this data is a challenging task. Topic modeling provides a way to categorize these documents, where high dimensionality of the corpus affects the result of topic model, making it important to apply feature selection or information retrieval process for dimensionality reduction. The requirement for efficient topic modeling includes the removal of unrelated words that might lead to specious coexistence of the unrelated words. This paper proposes an efficient framework for the generation of better topic coherence, where term frequency-inverse document frequency (TF-IDF) and parsimonious language model (PLM) are used for the information retrieval task. PLM extracts the important information and expels the general words from the corpus, whereas TF-IDF re-estimates the weightage of each word in the corpus. The work carried out in this paper improved the topic coherence measure to provide a better correlation among the actual topic and the topics generated from PLM.

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来源
《International journal on Semantic Web and information systems》 |2021年第3期|18-34|共17页
作者
Aakanksha Sharaff; Dilip Singh Sisodia; Jitesh Kumar Dewangan;
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作者单位

National Institute of Technology;

Samsung Research Institute;

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原文格式 PDF
正文语种英语
中图分类计算机网络;
关键词
Information Retrieval; Latent Dirichlet Allocation; Latent Semantic Indexing; Parsimonious Language Model; Topic Modeling;

Prospecting the Effect of Topic Modeling in Information Retrieval

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