A Systematic Literature Review on Spell Checkers for Bangla Language

Mandal, Prianka; Hossain, Belal

doi:10.5815/ijmecs.2017.06.06

Cited by 6 publications

(3 citation statements)

References 11 publications

(13 reference statements)

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“…There are many methods of spell checking errors dedicated to the Bengali language [19]. A system for checking the spelling of an English word transliterated to Bengali, based on Levenshtein distance and unigram is presented in [18].…”

Section: Related Workmentioning

confidence: 99%

Polish Word Recognition Based on n-Gram Methods

Wojcicki,

Zientarski

2024

IEEE Access

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Word recognition of Slavic languages is not an easy task due to the complicated declension of words and a variety of diacritical signs. Polish is a representative of West Slavic languages, which are written in Latin characters. Automatic handwritten word recognition in Slavic languages is not easy, due to the poor recognition rate of letters with diacritical signs and lack of good handwritten text corpora for languages with declension. The main aim of the research is to investigate the possibility of correcting typos made in the final phase of recognizing Polish. The method developed is based on letter recognition by means of convolutional neural networks (CNNs) and text matching algorithms for resulting words. At the first stage, we use a designed convolutional neural network for character recognition. At the second stage, after combining letters into words we apply a post-processing error correction method, which improves the efficiency of recognition of the misspelled words. We checked the efficiency of word matching for a few measures of similarity of words, i.e: edit distance (Damerau-Levenshtein), string matching (Sorensen-Dice) and list of candidates. In addition, we examine how word length and the number of misplaced letters affect the behaviour of the algorithms used. The analysis is carried out for bigram and trigram methods. By combining different methods to assess the similarity of words, better selection of lists of proposed words has been achieved. The article proposes an innovative method for correcting post-processing errors in recognizing Polish words with the efficiency of correct word matching ranging from 76% to 99%, depending on the measure and word length used.

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Section: Related Workmentioning

confidence: 99%

Polish Word Recognition Based on n-Gram Methods

Wojcicki,

Zientarski

2024

IEEE Access

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“…A Systematic Literature Review (SLR) is the technique of recognizing, appraising, and interpreting all available investigations relevant to the specific study topic. SLR can be used to review the available data on a certain research issue, identify any research gaps, provide recommendations for further research, and collaborate to generate new ideas (Mandal & M Mainul Hossain, 2017). SLR necessitates a greater amount of effort than ordinary literature evaluations.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Sentiment Analysis from Bengali Text using NLP

Hira¹,

Akhond²,

Chowdhury³

et al. 2022

Am. J. Agric. Sci. Eng. Technol.

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Sentiment Analysis (SA) is the method of studying a person’s comments and statements through computational means. It is a sub-domain of Natural Language Processing. A sentiment analysis (SA) system is created by training a significant number of positive, negative or neutral sentence datasets. Although there are many research papers on this subject in English, the work of sentiment analysis in Bengali has not become very popular due to the complexity of the Bangla language and the insufficient presence of the Bangla language online. But now the use of Bengali language has increased in online news, social media, and blogs. At the same time, the number of researches on Bengali NLP is also increasing. But what is the current state of sentiment analysis, its limitations and is there still room for improvement in some places, are not being properly reviewed and research is lagging behind. We have conducted a review on sentiment analysis so that future researchers of sentiment analysis can easily find out about the current state of sentiment analysis. In this research, we have tried to survey the current context of sentiment analysis (SA) and at the same time, we have created a sequence of comparatively better research from the existing ones. To create this sequence we followed a method called is TOPSIS. We have also discussed the challenges to overcome for improving the Sentiment analyzer.

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“…Recent relevant studies are selected based on a set of significant criteria. Their results indicate that there are research gaps in this research topic and has a potential for further investigation [5]. Ibrahim et al, 2017 presented a good literature review about Arabic Script Languages (ASL).…”

Section: Introductionmentioning

confidence: 99%

Design an Accurate Algorithm for Alias Detection

Alsurori¹,

Al-Sanabani²,

Al-Hagree³

2018

IJIEEB

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An improvement in detection of alias names of an entity is an important factor in many cases like terrorist and criminal network. Accurately detecting these aliases plays a vital role in various applications. In particular, it is critical to detect the aliases that are intentionally hidden from the real identities, such as those of terrorists and frauds. Alias Detection (AD) as the name suggests, a process undertaken in order to quantify and identify different variants of single name showing up in multiple domains. This process is mainly performed by the inversion of one-to-many and many-to-one mapping. Aliases mainly occur when entities try to hide their actual names or real identities from other entities i.e.; when an object has multiple names and more than one name is used to address a single object. N-gram distance algorithm (N-DIST) have find wide applicability in the process of AD when the same is based upon orthographic and typographic variations. Kondrak approach, a popular N-DIST works well and fulfill the cause, but at the same time we uncover that (N-DIST) suffers from serious inabilities when applied to detect aliases occurring due to the transliteration of Arabic name into English. This is the area were we have tried to hammer in this paper. Effort in the paper has been streamlined in extending the N-gram distance metric measure of the approximate string matching (ASM) algorithm to make the same evolve in order to detect aliases which have their basing on typographic error. Data for our research is of the string form (names & activities from open source web pages). A comparison has been made to show the effectiveness of our adjustment to (N-DIST) by applying both forms of (N-DIST) on the above data set. As expected we come across that adjusted (A-N-DIST) works well in terms of both performance & functional efficiency when it comes to matching names based on transliteration of Arabic into English language from one domain to another.

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A Systematic Literature Review on Spell Checkers for Bangla Language

Cited by 6 publications

References 11 publications

Polish Word Recognition Based on n-Gram Methods

Polish Word Recognition Based on n-Gram Methods

A Systematic Review of Sentiment Analysis from Bengali Text using NLP

Design an Accurate Algorithm for Alias Detection

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