A Partial String Matching Approach for Named Entity Recognition in Unstructured Bengali Data

Ibtehaz, Nabil; Satter, Abdus

doi:10.5815/ijmecs.2018.01.04

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…A partial string matching approach, based on Breadth First Search (BFS), was proposed by Ibtehaz et al [31] to identify NE's from an unstructured Bengali text corpus.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

Lima,

Md Hasib,

Azam

et al. 2023

PLoS ONE

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Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art performance. A unique dataset was created for this study demonstrating the impact of a good quality dataset on accuracy. We proposed a method for developing a high quality NER dataset for any language. We have used our dataset to evaluate the performance of various Deep Learning models. A hybrid model performed with the exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%. Our proposed model reduces the need for feature engineering and utilizes minimal resources.

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“…A partial string matching approach, based on Breadth First Search (BFS), was proposed by Ibtehaz et al [31] to identify NE's from an unstructured Bengali text corpus.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

Lima,

Md Hasib,

Azam

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

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“…Named entities extraction is required to improve tasks such as question answering, referring expressions or pronouns in a sentence to the same entity (coreference resolution), and discovering semantic relationships between entities in sentences (relation extraction). Handcrafted methods were employed to tackle this problem in the past, but, with the development of data mining, and machine learning methods availability of large datasets, we now have the opportunity to leverage the power of machine learning to solve it [41]. There are different approaches for NEG, as shown in Figure 4…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Markov Models Applications in Natural Language Processing: A Survey

Almutiri¹,

Nadeem²

2022

IJITCS

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Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state. The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.

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“…Banerjee et al proposed the Margin Infused Relaxed Algorithm for NER, where they used the IJCNLP-08 NERSSEAL dataset for the experiment [23]. In [105], Ibtehaz et al reported a partial string matching technique for Bangla NER.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models

Alam¹,

Hasan²,

Alam³

et al. 2021

Preprint

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Bangla -ranked as the 6 𝑡ℎ most widely spoken language across the world, 1 with 230 million native speakersis still considered as a low-resource language in the natural language processing (NLP) community. With three decades of research, Bangla NLP (BNLP) is still lagging behind mainly due to the scarcity of resources and the challenges that come with it. There is sparse work in different areas of BNLP; however, a thorough survey reporting previous work and recent advances is yet to be done. In this study, we first provide a review of Bangla NLP tasks, resources, and tools available to the research community; we benchmark datasets collected from various platforms for nine NLP tasks using current state-of-the-art algorithms (i.e., transformer-based models). We provide comparative results for the studied NLP tasks by comparing monolingual vs. multilingual models of varying sizes. We report our results using both individual and consolidated datasets and provide data splits for future research. We reviewed a total of 108 papers and conducted 175 sets of experiments. Our results show promising performance using transformer-based models while highlighting the trade-off with computational costs. We hope that such a comprehensive survey will motivate the community to build on and further advance the research on Bangla NLP.

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A Partial String Matching Approach for Named Entity Recognition in Unstructured Bengali Data

Cited by 7 publications

References 13 publications

A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

Markov Models Applications in Natural Language Processing: A Survey

A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models

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