A Review on Automatic Person Attribute Information Extraction and Disambiguation from Unstructured text

Authors

  • Yadnesh Charekar  Computer Department, Marathwada Mitra Mandal's College of Engineering, Savitribai Phule Pune University, Maharashtra, India
  • Ruchita Abhang  Computer Department, Marathwada Mitra Mandal's College of Engineering, Savitribai Phule Pune University, Maharashtra, India
  • Rutvij Joshi  Computer Department, Marathwada Mitra Mandal's College of Engineering, Savitribai Phule Pune University, Maharashtra, India
  • Shreyas Kulkarni  Computer Department, Marathwada Mitra Mandal's College of Engineering, Savitribai Phule Pune University, Maharashtra, India
  • Ila Savant  Assistant Professor, Computer Department, Marathwada Mitra Mandal's College of Engineering, Savitribai Phule Pune University, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT2390216

Keywords:

Natural Language Processing (NLP), Named Entity Recognition (NER), Entity Attribute Extraction, Conditional Random Fields (CRF), Deep Learning, SpaCy, LUKE

Abstract

Entity attribute extraction is the process of identifying and extracting attributes, or characteristics, of entities from a given text. The objective is to create a model that can automatically perform person-attribute information extraction from unstructured text. Entity attribute extraction's primary goal is to locate and extract attributes of entities from a supplied text. As a result, information from the unstructured text may now be represented in a structured way. By extracting attributes of entities, a computer program can gain a better understanding of the information contained in the text and can use this information for various purposes such as building a knowledge base or for information retrieval. In this way, entity attribute extraction can help to improve the ability of computer programs to process and understand natural language text. All the essential tools and algorithms are researched and discussed in this paper. This study is divided into two main sections that explore published works and modern tools and technologies working in the field of Entity attribute extraction. It also identifies critical research gaps in the literature under assessment. The gap analysis reveals potential for improved textual event prediction algorithms in the future.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-04-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

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How to Cite

[1]
Yadnesh Charekar, Ruchita Abhang, Rutvij Joshi, Shreyas Kulkarni, Ila Savant, " A Review on Automatic Person Attribute Information Extraction and Disambiguation from Unstructured text, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.195-201, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2390216