Word sense disambiguation of Bangla sentences using statistical approach

Nazah, Saiba; Hoque, Mohammed Moshiul; Hossain, Gahangir

doi:10.1109/eict.2017.8275157

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…the NER is the RNN model [78]. In case of word sense disambiguation different machine learning techniques have been used like KNN-based algorithm, Pal et al [46], [79], Naive Bayes classifier and ANN [80].…”

Section: B Classical Methodsmentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Sen,

Fuad,

Islam

et al. 2021

Preprint

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The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories,

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Section: B Classical Methodsmentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Sen,

Fuad,

Islam

et al. 2021

Preprint

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“…The most popular Hindi WordNet has been utilized which is Developed by the CFILT, IIT, Bombay. Saiba Nazah et al [32] Word Sense Disambiguation of Bangla sentences [32] This WSD system is based on Bengali words. Naïve Bayes classifier and artificial neural network (ANN) have been used to disambiguate the Bengali Words.…”

Section: Authors Wsd Based Systems Technique(s) Used In Similar Type ...mentioning

confidence: 99%

“…A WSD system has been described by the Author [30] which is unsupervised approach and supervised approach has been bypassed by this system using simulation of the semantic inference process that is performed by human language users as in [31]. Another Bengali WSD system has been proposed by the Author [32] that uses Naïve Bayes classifier and Artificial Neural Network (ANN) to disambiguate the ambiguous Bengali words in Bengali Language. Naïve Bayes classifier has been used for the training phase and ANN has been utilized to predict and detect the correct sense of an ambiguous Bengali word as in [32].…”

Section: Authors Wsd Based Systems Technique(s) Used In Similar Type ...mentioning

confidence: 99%

“…Another Bengali WSD system has been proposed by the Author [32] that uses Naïve Bayes classifier and Artificial Neural Network (ANN) to disambiguate the ambiguous Bengali words in Bengali Language. Naïve Bayes classifier has been used for the training phase and ANN has been utilized to predict and detect the correct sense of an ambiguous Bengali word as in [32]. Adapted Lesk method has been used by the Author [33] in their WSD system.…”

Section: Authors Wsd Based Systems Technique(s) Used In Similar Type ...mentioning

confidence: 99%

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Word Sense Disambiguation in Hindi Language Using Score Based Modified Lesk Algorithm

Tripathi¹,

Mukherjee²,

Hendre³

et al. 2021

IJCDS

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Hindi is the widely used spoken language in the Indian subcontinent, and is used by more than 260 million Indians citizens. Indian governments has many digital initiatives to serve Indian citizen better, hence Hindi language becomes one of the important languages to serve Indian citizen. The Government initiatives are like smart city, Hospital Services, Common Service Centers, Digital Payment Ecosystem, Pensioners Scheme, Digital Locker and many more. These all initiative are served using mobile and web based applications, which citizens can access easily instead of visiting various government departments. To serve the large Hindi speaking population, it is necessary to handle the ambiguous words which have multiple connotations in any natural language processing task. In this paper, word sense disambiguation for Hindi language is proposed. Proposed method makes use of Lesk algorithm to disambiguate the Hindi words. Novel scoring method is used to assign a sense score to each token of the Hindi sentence. The sense score is calculated based on the gloss, hypernym, hyponym and synonym of the combinations of different sense of tokens. Hindi WordNet database created by CFILT, IIT Bombay is used in the proposed system. The proposed algorithm takes a natural language (NL) sentence in Hindi (Devanagari script) and process the sentence according to the score based approach modeled on the basic Lesk algorithm with the help of Hindi WordNet designed by CFILT IIT Bombay. The solution provided in this paper can be used vividly in various web based applications like Query-Response Systems, Question-Answer Systems, Sentiment analysis, Recommendation systems etc.

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“…Nazah et al [32] have proposed an algorithm for WSD in Bengali language based on Naive Bayes classifier and Artificial Neural Network (ANN). The authors claim an accuracy of 82% in WSD task for some selective Bengali ambiguous words.…”

Section: 2i Bengalimentioning

confidence: 99%

Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications

Pal

Saha

2019

Sādhanā

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In this work, Word Sense Disambiguation (WSD) in Bengali language is implemented using unsupervised methodology. In the first phase of this experiment, sentence clustering is performed using Maximum Entropy method and the clusters are labelled with their innate senses by manual intervention, as these sense-tagged clusters could be used as sense inventories for further experiment. In the next phase, when a test data comes to be disambiguated, the Cosine Similarity Measure is used to find the closeness of that test data with the initially sense-tagged clusters. The minimum distance of that test data from a particular sense-tagged cluster assigns the same sense to the test data as that of the cluster it is assigned with. This strategy is considered as the baseline strategy, which produces 35% accurate result in WSD task. Next, two extensions are adopted over this baseline strategy: (a) Principal Component Analysis (PCA) over the feature vector, which produces 52% accuracy in WSD task and (b) Context Expansion of the sentences using Bengali WordNet coupled with PCA, which produces 61% accuracy in WSD task. The data sets that are used in this work are obtained from the Bengali corpus, developed under the Technology Development for the Indian Languages (TDIL) project of the Government of India, and the lexical knowledge base (i.e., the Bengali WordNet) used in the work is developed at the Indian Statistical Institute, Kolkata, under the Indradhanush Project of the DeitY, Government of India. The challenges and the pitfalls of this work are also described in detail in the pre-conclusion section.

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Word sense disambiguation of Bangla sentences using statistical approach

Cited by 12 publications

References 8 publications

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Word Sense Disambiguation in Hindi Language Using Score Based Modified Lesk Algorithm

Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications

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