Evaluation of Punjabi Named Entity Recognition using Context Word Feature

Kaur, Amandeep; Josan, Gurpreet Singh

doi:10.5120/16913-7011

Cited by 4 publications

(5 citation statements)

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“…CRFs work by defining a set of features that capture the characteristics of each word and then it assigns weight to these features based on a training dataset [31], [43], [108]. During, inference, given a sequence of words, the CRF calculates the probability of all possible NER tags using defined features and corresponding weighs [38]. The sequence having high probability will be selected.…”

Section: (B) Conditional Random Fieldmentioning

confidence: 99%

“…(a) Information Extraction (IE): Named Entity Recognition is widely used in information extraction systems to identify and extract specific pieces of information from unstructured text [38], [39]. For example, extracting names of people, organizations, and locations from news articles or social media posts [40], [41], [42], [43], [44].…”

Section: Application Of Nermentioning

confidence: 99%

“…It is a statistical model that assumes there is an underlying hidden sequence of states that generates observable symbols. In case of NER, the hidden states could represent different entities and the observable symbols are the words in the text [38].…”

Section: (A) Hidden Markov Modelmentioning

confidence: 99%

“…It creates a tree like structure where each internal node represents decision based on a feature, and each leaf node represents an outcome. The Decision Tree stands as a prevalent algorithm in machine learning, serving classification and regression tasks alike [31], [38]. This model takes the shape of a tree, deriving decisions from input data features [31], [108].…”

Section: (C) Decision Treementioning

confidence: 99%

“…Its primary efficacy lies in binary classification scenarios where the objective is to categorize data points into two classes based on their inherent features [77]. SVMs are rooted in principles encompassing linear algebra, optimization theory, and the notion of identifying an optimal hyperplane that effectively distinguishes data points [38]. Here's a breakdown of the fundamental mathematical principles underpinning SVMs [109].…”

Section: (D) Support Vector Machinementioning

confidence: 99%

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State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

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Named Entity Recognition (NER) is a fundamental task of natural language processing (NLP) that focuses on the identification and classification of named entities such as name of individual persons, location, organization and dates within the text. NER plays a pivotal role in various NLP applications, including information extraction, question answering, text summarization and sentiment analysis. Natural language processing's (NLPs) fundamental issue is named entity recognition (NER). While extensive research has been conducted on NER for English and Hindi, the complexities of Indian languages present unique challenges that require customized solutions. Working with NER for Indian languages is a difficult endeavor with limited resources available. This article provides a comprehensive review of NER approaches tailored for Indian languages. Indian languages pose unique challenges to NER due to their rich morphological and syntactic variations, script diversity and limited annotated data availability. This paper reviews the various techniques and methodologies employed in NER for Indian languages, including rule-based, machine learning and deep learning approaches. It analyzes the strengths and limitations of each approach. Additionally, this article examines the recent advancements in transfer learning and multilingual models, showcasing their potential in improving NER performance across Indian languages. This paper aims to guide researchers and practitioners in the development of NER systems for Indian languages and foster further advancements in this field. This article also provides a comprehensive review of the diverse approaches employed for NER in Indian languages, highlighting the strength and limitations as well.

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