Degree
Master of Science in Computer Science
Department
Department of Computer Science
Faculty / School
Faculty of Computer Sciences (FCS)
Date of Submission
2020-06-30
Supervisor
Dr. Sajjad Haider, Professor, Department of Computer Science
Document type
MSCS Survey Report
Keywords
Named Entity Recognition, Semantic, Maximum Entropy, Computational linguistics, Personally identifiable information
Abstract
Named Entity Recognition (NER) aims to find mentions from text belonging to predefined semantic types like a person, location, organization and others. NER not only acts as a standalone tool for information extraction but also plays a vital role in natural language processing applications including but not limited to text understanding, information retrieval, automatic text summarization, question answering computational linguistics and Personally identifiable information (PII) discovery.
This survey aims to summarize some of the popular techniques developed for Named Entity Recognition. Chapter 1 gives a brief introduction on NER, Chapter 2 explores the Maximum Entropy (ME) models and its usage in NER, Chapter 3 focuses on Hidden Markov Models (HMM). Chapters 4 and 5 describes the Neural models-based techniques, which is the most recent advancement in this field. The work in Chapter 2 - 4 focuses on the English language while in Chapter 5, we discus some of the research done for the Urdu NER.
Recommended Citation
Khan, M. Y. (2020). A survey on named entity recognition (Unpublished MSCS survey report). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/survey-reports-mscs/19
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