Comparison of different POS Tagging Techniques (n-gram, HMM and Brill’s tagger) for Bangla

Hasan, Fahim; UzZaman, Naushad; Khan, Mumit

doi:10.1007/978-1-4020-6264-3_23

Cited by 44 publications

(23 citation statements)

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“…The two taggers were built on the basis of different approaches: rule‐based and probabilistic approaches. The Monty Tagger, a rule‐based POS tagger, implements Eric Brill's transformational‐based learning POS tagger (Brill, 1994) that is known as an effective tagger (Hasan, UzZaman, Khan, 2007). TreeTagger (Schmid, 1994), developed at the Institute for Computational Linguistics of the University of Stuttgart, is one of the popular probabilistic approach‐based POS taggers (Marcus, Santorini, & Marcinkiewicz, 1993).…”

Section: Analysis Of Lcsh: Resultsmentioning

confidence: 99%

Revisiting the syntactical and structural analysis of Library of Congress Subject Headings for the digital environment

Chan

2010

J. Am. Soc. Inf. Sci.

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With the current information environment characterized by the proliferation of digital resources, including collaboratively created and shared resources, Library of Congress Subject Headings (LCSH) is facing the challenges of effective and efficient subject-based organization and retrieval of digital resources. To explore the feasibility of utilizing LCSH in a digital environment, we might need to revisit its basic characteristics. The objectives of our study were to analyze LCSH in both syntactic and relational structures, to discover the structural characteristics of LCSH, and to identify problems and issues for the feasibility of LCSH as an effective subject access tool. This study reports and discusses issues raised by the syntactic and hierarchical structures of LCSH that present challenges to its use in a networked environment. Given the results of this study, we recommend a number of provisional future directions for the development of LCSH towards further becoming a viable system for digital and networked resources.

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Section: Analysis Of Lcsh: Resultsmentioning

confidence: 99%

Revisiting the syntactical and structural analysis of Library of Congress Subject Headings for the digital environment

Chan

2010

J. Am. Soc. Inf. Sci.

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“…They proposed a way to implement a corpus by collecting data from online resources [2]. They implemented a POS tagger based on HMM, n-gram and Brill's tagger [3]. The result is analyzed with a small corpus of 5000 words giving an accuracy of only 55%.…”

Section: Related Previous Workmentioning

confidence: 99%

Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique

Rahman¹,

Haque²,

Saurav³

2020

IJCA

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Identifying and categorizing opinions in a sentence is the most prominent branch of natural language processing. It deals with the text classification to determine the intention of the author of the text. The intention can be for the presentation of happiness, sadness, patriotism, disgust, advice, etc. Most of the research work on opinion or sentiment analysis is in the English language. Bengali corpus is increasing day by day. A large number of online News portals publish their articles in Bengali language and a few News portals have the comment section that allows expressing the opinion of people. Here a research work has been done on Bengali Sports news comments published in different newspapers to train a deep learning model that will be able to categorize a comment according to its sentiment. Comments are collected and separated based on immanent sentiment.

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“…Because a term can represent more than one part of speech at different sentences, and some parts of speech are complex or indistinct, it becomes difficult to perform the process exactly. However, research has improved the accuracy of POS tagging, giving rise to various effective POS taggers such as TreeTagger, TnT (based on the Hidden Markov model), Stanford tagger [4,10,12]. State of the art taggers highlight accuracy of circ 93% compared to the human's tagging results.…”

Section: Part-of-speech Taggingmentioning

confidence: 99%

Augmenting Bug Localization with Part-of-Speech and Invocation

Zhou

Tong

Chen

et al. 2017

Int. J. Soft. Eng. Knowl. Eng.

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Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of our approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e., ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.

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Comparison of different POS Tagging Techniques (n-gram, HMM and Brill’s tagger) for Bangla

Cited by 44 publications

References 10 publications

Revisiting the syntactical and structural analysis of Library of Congress Subject Headings for the digital environment

Revisiting the syntactical and structural analysis of Library of Congress Subject Headings for the digital environment

Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique

Augmenting Bug Localization with Part-of-Speech and Invocation

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