Word Completion and Sequence Prediction in Bangla Language Using Trie and a Hybrid Approach of Sequential LSTM and N-gram

Sarker, Soumik; Islam, Md. Ekramul; Saurav, Jillur Rahman; Nahid, Mahadi Hasan

doi:10.1109/icaict51780.2020.9333518

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…It cannot reliably predict contextrelevant words. In [22], the authors propose both a word completion and sentence completion using trie and sequential LSTM model. Their proposed trie model is supposed to be dynamic.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

“…In [5], the proposed method consists of a GRU model extended on the n-gram model to give the prediction in context. Another hybrid approach of sequential LSTM and n-gram along with trie implementation [6] is proposed for Bengali word completion and sequence prediction.…”

Section: Introductionmentioning

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.

View full text Add to dashboard Cite

show abstract

“…Sarker, Soumik, Jillur Rahman Saurav, Md Mahadi Hasan Nahid, and Md Ekramul Islam [5], suggested a trie, sequential LSTM, and N-gram integrated methodology for word completion and sequence prediction in Bangla. To store Bangla vocabulary and get the term from a user-input prefix, the trie data structure was implemented.…”

Section: Literature Surveymentioning

confidence: 99%

Autocomplete recommendation plugin and Summarizing Text using Natural Language Processing

Shaikh,

Newalkar,

Gaikwad

et al. 2023

jinita

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Expert-caliber documents, reports, letters, and resumes can be easily developed using Microsoft Office. Microsoft Office offers capabilities such as grammar check, text and font checking & formatting, HTML compatibility, advanced page layout, image support, and more in contrast to a plain text editor, however, it does not have the autocomplete abbreviations feature. The paper proposes an Autocomplete abbreviation Recommendation System that will integrate the benefits of getting automatic suggestions of either full forms, abbreviations, or both by clicking on the option that is being suggested. This will provide more flexibility to the user using existing Microsoft Office platforms. To create this feature, we have examined the JavaScript JQuery functions to implement a basic autocomplete feature. Information overloading is also one of the most important problems brought on by the Internet's explosive expansion. Massive quantities of text are difficult for people to manually summarise. Thus, there is now a greater need for summarizers that are more sophisticated and potent. Hence, Python's packages, methods, and NLP are used in this work to implement Text Summarization. By using this technique, the phrase's overall meaning is enhanced and the reader's comprehension is enhanced.

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An RNN Based Approach to Predict Next Word in Bangla Language

Mahmud

Rony

Bhowmik

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Word Completion and Sequence Prediction in Bangla Language Using Trie and a Hybrid Approach of Sequential LSTM and N-gram

Cited by 8 publications

References 16 publications

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

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

Autocomplete recommendation plugin and Summarizing Text using Natural Language Processing

An RNN Based Approach to Predict Next Word in Bangla Language

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