Building an Industry Standard Novel Language Model Using Named Entities

Title

Building an Industry Standard Novel Language Model Using Named Entities

Creator

Ananth K.; Kirubanand V.B.

Description

In every Industry, there is a significant amount of text used in their specific domains. As these are less prevalent in the testing set, anticipating entity names in a language model is a problem faced by the entire industry. In this research a unique and very effective strategy for creating exclusionary classification models that could map entity names based on entity type information is provided. A group of benchmark datasets based on Mortgage is presented, which we used to test the below-presented model. According to experimental findings, our model achieves a perplexity level that is 64% higher than that of the most advanced language models. 2022 IEEE.

Source

2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

CTC(Connectionist Temporal Classification); FSM(Finite State Machine); LM(Language Model); NER(Named Entity Recognition); NLP(Natural Language Processing); PTB(Pen Tree Bank)

Coverage

Ananth K., CHRIST (Deemed to Be University), Department of Computer Science, India; Kirubanand V.B., CHRIST (Deemed to Be University), Department of Computer Science, India

Rights

Restricted Access

Relation

ISBN: 978-166545361-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT54229.2022.10041678

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149177678&doi=10.1109%2fTQCEBT54229.2022.10041678&partnerID=40&md5=8b883391cc85d667ad3de2b63c34d38a