Machine Learning-based approach to automatic POS tagging of Macedonian language

Bonchanoski, Martin; Zdravkova, Katerina

doi:10.1145/3136273.3136275

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…The results in Table 6 are an improvement to the last obtained results [37] because of the revision and correction of some mistakes that were made during the initial process of manual tagging of the corpus.…”

Section: Comparative Analysis Of the Obtained Resultsmentioning

confidence: 76%

“…The results have shown that by having larger corpora, the tagger would be more robust. By doing a review of the manual tagged corpora, an improvement of 1% has been achieved compared to the previous results [37] on the same dataset and with the same methods. However, they have also revealed that with the larger corpora, one should not expect an improved accuracy higher than 1%.…”

Section: Conclusion and Further Workmentioning

confidence: 78%

“…This paper presents the creation of a fully automated POS tagging of Macedonian language. It is an extension of the previous research done by the same authors, which resulted in a POS tagger with an accuracy of 96.37% [29]. New contribution encompasses both, the theoretical and the practical aspects.…”

Section: Introductionmentioning

confidence: 76%

“…2.). The tool was introduced in 2017 [37]. All the unique words from the corpus, which existed in the electronic lexicon [27] and in the digital dictionary [28] in only one class have been used to assign all the possible tags to the words in the corpus.…”

Section: Semiautomatic Pos Taggingmentioning

confidence: 99%

“…Thus, the examination of the errors is very beneficial for further improvements of the tagger. The whole confusion matrix included all the word classes, and it was already presented in the ACM paper [37]. Not every mistake that the tagger made had an equal weight.…”

Section: Error Analysismentioning

confidence: 99%

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Learning syntactic tagging of Macedonian language

Bonchanoski

Zdravkova

2018

COMSIS J

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This paper presents the creation of machine learning based systems for Part-of-speech tagging of Macedonian language. Four well-known PoS tagger systems implemented for English and Slavic languages: TnT, cyclic dependency network, guided learning framework for bidirectional sequence classification, and dynamic features induction were trained. Orwell's novel "1984" was manually tagged from the authors and it was used split into training and test set. After the training of the models, a comparison between the models was made. At the end, a POS tagger with an accuracy that reaches 97.5% was achieved, making it very appropriate for the future grammatical tagging of the National corpus of Macedonian language, which is currently in its initial stage. The Part-of-speech tagger that was create is published online and free to use.

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Section: Comparative Analysis Of the Obtained Resultsmentioning

confidence: 76%

Section: Conclusion and Further Workmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 76%

Section: Semiautomatic Pos Taggingmentioning

confidence: 99%

Section: Error Analysismentioning

confidence: 99%

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Learning syntactic tagging of Macedonian language

Bonchanoski

Zdravkova

2018

COMSIS J

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A Novel Machine Annotated Balanced Bangla OCR Corpus

Rifat¹,

Banik²,

Hasan³

et al. 2021

Communications in Computer and Information Science

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We present a balanced and 100% machine annotated Bangla OCR corpus of nearly eight and a half million characters. This is a "first-of-its-kind" effort for the Bangla language. Although Bangla is a top five most frequently used language in the world, it is still considered to be a resource-poor language since even the most basic language processing tools are not available. This corpus goes a long way in mitigating this shortcoming. Also, it is important to mention that this is a continuation of our previous work in building a synthetic corpus for training Bangla OCRs and is an intermediate step in our effort to build a true gold-standard corpus for Bangla OCRs-our ultimate goal. The paper not only discusses the corpus, but also the entire processing pipeline-from scanning pages to identifying lines to segmenting words and finally characters-which are then annotated using a homegrown OCR. We also present a comprehensive corpus characteristics and specification review-demonstrating that we have built a corpus which is very nearly balanced-a very desirable characteristic of any corpus design.

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Part of speech tagging: a systematic review of deep learning and machine learning approaches

2022

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Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

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Machine Learning-based approach to automatic POS tagging of Macedonian language

Cited by 3 publications

References 7 publications

Learning syntactic tagging of Macedonian language

Learning syntactic tagging of Macedonian language

A Novel Machine Annotated Balanced Bangla OCR Corpus

Part of speech tagging: a systematic review of deep learning and machine learning approaches

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