Multi-Perspective Semantic Information Retrieval
- URL: http://arxiv.org/abs/2009.01938v1
- Date: Thu, 3 Sep 2020 21:56:38 GMT
- Title: Multi-Perspective Semantic Information Retrieval
- Authors: Samarth Rawal and Chitta Baral
- Abstract summary: This work introduces the concept of a Multi-Perspective IR system, which combines multiple deep learning and traditional IR models to better predict the relevance of a query-sentence pair.
This work is evaluated on the BioASQ Biomedical IR + QA challenges.
- Score: 22.74453301532817
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Information Retrieval (IR) is the task of obtaining pieces of data (such as
documents or snippets of text) that are relevant to a particular query or need
from a large repository of information. While a combination of traditional
keyword- and modern BERT-based approaches have been shown to be effective in
recent work, there are often nuances in identifying what information is
"relevant" to a particular query, which can be difficult to properly capture
using these systems. This work introduces the concept of a Multi-Perspective IR
system, a novel methodology that combines multiple deep learning and
traditional IR models to better predict the relevance of a query-sentence pair,
along with a standardized framework for tuning this system. This work is
evaluated on the BioASQ Biomedical IR + QA challenges.
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