Semantic similarity between short paragraphs using Deep Learning

Dhruv Verma, S. N. Muralikrishna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Textual semantic similarity plays an increasingly important role in tasks such as information retrieval, text mining and text-based searches. Multiple approaches have been presented to enhance methods for information retrieval by understanding the underlying meaning of sentences. However, most of these focus on single line sentences. In this paper, we try to evaluate the effectiveness of these approaches to understand the semantic meaning of short paragraphs. We use an existing recurrent neural network architecture and train it using document embedding vectors to try and infer the meaning of small paragraphs consisting of one, two or three sentences. We use three different methods - Manhattan distance, Euclidean distance and cosine distance - to evaluate the performance and effectiveness of measuring the semantic similarity. The conclusion compares the performance of all three methods.

Original languageEnglish
Title of host publicationProceedings of CONECCT 2020 - 6th IEEE International Conference on Electronics, Computing and Communication Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728168289
DOIs
Publication statusPublished - 07-2020
Event6th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2020 - Bangalore, India
Duration: 02-07-202004-07-2020

Publication series

NameProceedings of CONECCT 2020 - 6th IEEE International Conference on Electronics, Computing and Communication Technologies

Conference

Conference6th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2020
Country/TerritoryIndia
CityBangalore
Period02-07-2004-07-20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Control and Optimization

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