An N-gram based model for predicting of word-formation in Assamese language

Bhuyan, Manasjyoti; Sarma, Shikhar Kumar

doi:10.1080/02522667.2019.1580883

Cited by 11 publications

(5 citation statements)

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“…From the above discussion it is seen that the word prediction model are useful in any language and workings are going on not only the English language but also the local languages like Bangla and other languages, so it will be very much helpful if such types of accurate predictive models are designed for Assamese language. In addition, this research work will also enhance the work done by Bhuyan and Sarma in [4] which is the only work in Assamese to predict words while writing, and also, able to bring a new direction in the field of Assamese writing software or tools.…”

Section: Related Workmentioning

confidence: 83%

“…Bhuyan, S.K. Sarma In [4], authors have used an N-gram model to predict word for Assamese language using Unigram, Bigram, Trigram, Quadrigram language models. Keystrokes saving values are tested for these models for both in-domain data and users' data, and the maximum keystrokes saving is found 74.04% and 48.28% for in-domain data and users' data.…”

Section: Related Workmentioning

confidence: 99%

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A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

2019

ijeat

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Word prediction is a technique which tries to suggest the users’ words after knowing the few input letters of the user. This predictive model also tries to generate the future words or next words of a sentence by observing earlier words of the sentence. In this research, two problems are combined, one is word prediction and the next is handling of ambiguous words. A word prediction model predicts the future words of a sentence by using n-gram based model. In general, predictive models use unigram, bigram or trigram models to predict the next words. In case of sentences consisting of ambiguous words, the predictive model by using only bigram or trigram cannot perform well to predict the next words. To enhance this prediction for ambiguous words, maximum of six previous input words are observed and try to predict almost the exact words after the ambiguous words in those particular contexts. Different level of experiments are done and the results are compared for modified or enhanced prediction model with the traditional prediction model, improvement on accuracy and failure rate are found in the enhanced model. The accuracy of the Traditional Model is 60.68% on the hand the accuracy of the Enhanced Model is 66.88%. The failure rate of the Traditional Model is 32.35% and the Enhanced Model is 29.17%

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Section: Related Workmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

2019

ijeat

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“…There are some works published for next word prediction in several other languages apart from English. For example, an n-gram based [23] and an RNN based [24] next word prediction model for the Assamese language has been proposed. In [25], some sequence prediction models were explored to evaluate the performance of the next word prediction for Hindi.…”

Section: Literature Reviewmentioning

confidence: 99%

Context-based Bengali Next Word Prediction: A Comparative Study of Different Embedding Methods

Mahbub

Akhter

Kabir

et al. 2023

Dhaka Uni. J. of Applied Sci. and Eng.

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Next word prediction is a helpful feature for various typing subsystems. It is also convenient to have suggestions while typing to speed up the writing of digital documents. Therefore, researchers over time have been trying to enhance the capability of such a prediction system. Knowledge regarding the inner meaning of the words along with the contextual understanding of the sequence can be helpful in enhancing the next word prediction capability. Theoretically, these reasonings seem to be very promising. With the advancement of Natural Language Processing (NLP), these reasonings are found to be applicable in real scenarios. NLP techniques like Word embedding and sequential contextual modeling can help us to gain insight into these points. Word embedding can capture various relations among the words and explain their inner knowledge. On the other hand, sequence modeling can capture contextual information. In this paper, we figure out which embedding method works better for Bengali next word prediction. The embeddings we have compared are word2vec skip-gram, word2vec CBOW, fastText skip-gram and fastText CBOW. We have applied them in a deep learning sequential model based on LSTM which was trained on a large corpus of Bengali texts. The results reveal some useful insights about the contextual and sequential information gathering that will help to implement a context-based Bengali next word prediction system. DUJASE Vol. 7 (2) 8-15, 2022 (July)

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“…Finally, the probability of the sentence was calculated by placing different words of the confusion set and the list of words giving the highest sentence probabilities was displayed as a suggestion. Performance of the spell checker was 76% against a corpus size of 220,743.In [7], authors have designed a word prediction model for Assamese language using bigram, trigram and quadrigram models for spontaneous sentence completion in Assamese. In their prediction model if a user wrote a character on the editor window then the prediction system starts predicting the word related to that character and once a word was written, the predictive model has tried to show the next word as a suggestion.…”

Section: Related Workmentioning

confidence: 99%

A Statistical Model for Automatic Error Detection and Correction of Assamese Words

Bhuyan¹,

Sarma²

2019

IJRTE

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Digitization of local languages is getting importance in the present scenario and the Language Processing task is also becoming popular among the Linguistic and IT people. It is very common that most of the people are comfortable with their native mother tongue. Writing of corrected word-form is also an important task in the digital platforms for the future existence of a language. In this research work, the Assamese language is taken as a Natural Language which is processed in the experiments. The Assamese language is one of the Indian languages and the research & development of the Assamese language is going on; from the computational point of view, Assamese is in the development phase. In Assamese, there are some similar characters which are phonetically same but their glyphs are different these characters or symbols often cause confusion to the users while writing, these types of characters are specially taken into consideration in this research work. A list of 14 confusing characters pairs of Assamese letters is taken for experimental purpose. In addition, this research work has focused on errors of Assamese words, which are checked by using bigram and trigram models. Moreover, the proposed model also tries to find the erroneous character which causes the incorrectness and shows the suggestions for that incorrect character. A score based system is designed for the Assamese characters and each character is assigned a score from their probability of occurrences by using bigram and trigram language models. Different types of experiments are performed to check the correctness of the Assamese words and the proposed model is able to check the correctness of the Assamese word with accuracy ranging from 81% to 86%. Error rate in Assamese can be reduced by using this model in any digital platform where a user can type in Assamese

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An N-gram based model for predicting of word-formation in Assamese language

Cited by 11 publications

References 5 publications

A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

Context-based Bengali Next Word Prediction: A Comparative Study of Different Embedding Methods

A Statistical Model for Automatic Error Detection and Correction of Assamese Words

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