Realtime Semantic Similarity Analysis of Bulk Outlook Emails Using BERT

Semantic similarity (SS) analysis is a technique for finding similarities between words/sentences/documents based on their meaning. In natural language processing (NLP), SS is an important element to find a suitable mail from a bulk inbox. As the number of mails and mail content increases, it become...

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Bibliographic Details
Main Authors: Sanjeev, Mithun M., Ramalingam, Balaji, Kumar T.K., Sunil
Format: Conference Proceeding
Language:English
Subjects:
BERT
Bit error rate
Cosine similarity
Electronic mail
Natural language processing
Postal services
Python
Semantic similarity
Semantics
Online Access:Request full text
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Description
Summary:Semantic similarity (SS) analysis is a technique for finding similarities between words/sentences/documents based on their meaning. In natural language processing (NLP), SS is an important element to find a suitable mail from a bulk inbox. As the number of mails and mail content increases, it becomes difficult to get the matches with keywords and nearly impossible for many cases. This paper presents a method to find SS between query statements and mail content using BERT (Bidirectional Encoder Representations from Transformers). BERT is a pre-trained unsupervised NLP model developed by Google. The results are presented and compared with the existing keyword-based search to prove the efficiency of the proposed approach.
ISSN:2642-7354
DOI:10.1109/ICACCM50413.2020.9212979