Relevant subsection retrieval for law domain question answer system
Lecture Notes on Data Engineering and Communications Technologies, ISSN: 2367-4520, Vol: 32, Page: 299-319
2020
- 8Citations
- 15Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
Intelligent and instinctive legal document subsection information retrieval system is much needed for the appropriate jurisprudential system. To satisfy the law stakeholders’ need, the system should be able to deal with the semantics of law domain content. In this chapter, a sophisticated legal Question-Answer (QA) system is developed specifically for law domain which will be able to retrieve the relevant and best suitable document for any specific law domain queries posted by users’. Legal QA system is developed with the help of two computational areas— Natural Language Processing and Information Retrieval. This system is developed in an amenable way to retrieve the relevant subsection in accordance with the legal terminology embedded inquiry entered by the user. Syntactic and semantic analysis of legal documents followed by query processing helps in retrieving inferences from the knowledge base to answer the query. In our research, various models have been analyzed in the opinion of the document matching threshold value. Satisfactory results are obtained by 0.5 threshold value.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85083289576&origin=inward; http://dx.doi.org/10.1007/978-3-030-25797-2_13; http://link.springer.com/10.1007/978-3-030-25797-2_13; https://dx.doi.org/10.1007/978-3-030-25797-2_13; https://link.springer.com/chapter/10.1007/978-3-030-25797-2_13
Springer Science and Business Media LLC
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