Towards the first Maithili part of speech tagger: Resource creation and system development

Priyadarshi, Ankur; Saha, Sujan Kumar

doi:10.1016/j.csl.2019.101054

Cited by 21 publications

(4 citation statements)

References 2 publications

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“…To accomplish this, Python code was used to train three POS taggers, a default tagger, a unigram tagger, and a bigram tagger. This was done using the Brown corpus, a large corpus of text data [39]. Thereafter, the code uses the taggers to POS tag the lemmatized words in each dataset.…”

Section: Feature Extraction or Selectionmentioning

confidence: 99%

Effective Tweets Classification for Disaster Crisis based on Ensemble of Classifiers

Eke,

Mulenga,

Phiri

et al. 2023

Preprint

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In the field of disaster crisis management, the utilization of social media platforms has gained significant recognition. It helps in disseminating and gathering information during disasters, offering real-time updates on events, infrastructure damage reports, and casualty information. However, the information comes with a substantial amount of irrelevant content. Some researchers have utilized machine learning classifiers for classification, which has become ineffective. Thus, this study proposes an ensemble-based approach to disaster tweet classification, using a wide array of linguistic and word embedding features, Additionally, we investigate various supervised learning algorithms and ensemble classifiers for resolving this issue. Our findings reveal that the ensemble feature sets, specifically, the fusion of TF-IDF and word embeddings, when coupled with Bagging, achieved a classification accuracy of 98.92%. This research highlights the potential of leveraging machine learning and ensemble techniques on disaster tweet classification, providing insights for improving real-time disaster response efforts.

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Section: Feature Extraction or Selectionmentioning

confidence: 99%

Effective Tweets Classification for Disaster Crisis based on Ensemble of Classifiers

Eke,

Mulenga,

Phiri

et al. 2023

Preprint

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“…Accordingly, authors gained 48%, 66% and 86% accuracy respectively. Priyadarshi et al [27] proposed Maithili POS using CRF. Author himself annotated Maithili text and created a corpus which consisted of 52,190 words.…”

Section: Related Workmentioning

confidence: 99%

On State-of-the-art of POS Tagger, ‘Sandhi’ Splitter, ‘Alankaar’ Finder and ‘Samaas’ Finder for Indo-Aryan and Dravidian Languages

Gaikwad¹,

Jatinderkumar²

2021

IJACSA

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Computational Linguistic refers to the development of the computer systems that deal with human languages. In this paper, different Computational Linguistic Techniques such as Parts of Speech (POS) tagger, 'Sandhi' Splitter, 'Alankaar' Finder and 'Samaas' Finder were considered. After a thorough literature review, it was found that fifteen techniques were used for POS tagging, nine techniques were used for 'Sandhi' splitting, one work is done for 'Alankaar' finder and absolutely no techniques are available for 'Samaas' finder for the Indo-Aryan as well as Dravidian languages. Analysis shows that Rule Based Approach (RBA) and Hidden Markov Model (HMM) are frequently used for POS tagging, RBA is most frequently used for 'Sandhi' Splitter, the general Human Intelligence (HI) is used for 'Alankaar' Finder and no 'Samaas' finder technique is available for any Indian language.

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“…The first stemmer in Maithili was proposed by Priyadarshi and Saha using a hybrid approach (Priyadarshi and Saha, 2019). Barman (Priyadarshi and Saha, 2020). (Shah et al, 2020) worked on Marglishcode mixed Marathi text in a way that improves the performance on opinion mining.…”

Section: Background and Related Workmentioning

confidence: 99%

MTStemmer: A multilevel stemmer for effective word pre-processing in Marathi

Giri¹

2021

TURCOMAT

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In natural language processing, it is important that the context and the meaning of words are retained while also ensuring the efficacy of the data modelling process. During human-to-human interactions, special care is taken regarding the tense and phrasing of the words by taking into consideration the rules of grammar of the specific language. While this modification of words is necessary for framing consistent sentences, these appendages do not add significant value to the original meaning of the word. Stemming is the process of converting words back to their root form for efficient and accurate modelling of the data. In this paper, MTStemmer, a new stemmer for the Marathi language is proposed. It focuses on the stripping of suffixes for obtaining the root word form. The proposed stemmer applies a multilevel approach by taking into consideration both auxiliary verb-based suffixes and gender-based suffixes. The presented approach intends to improve upon the limitations of the previously proposed stemmers for this language. The stemming performed by the stemmer is found to be more accurate in terms of mapping to the root words. Stemming is often an important pre-processing step before processing the data further for the main task. The benefit of the proposed stemmer is demonstrated by using it for an extractive Marathi text summarization task. A significant improvement in the performance of multiple performance metrics is achieved owing to the stemming done by MTStemmer. The working of the proposed stemmer shows promising signs for the development of similar engines for other Indic languages.

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Towards the first Maithili part of speech tagger: Resource creation and system development

Cited by 21 publications

References 2 publications

Effective Tweets Classification for Disaster Crisis based on Ensemble of Classifiers

Effective Tweets Classification for Disaster Crisis based on Ensemble of Classifiers

On State-of-the-art of POS Tagger, ‘Sandhi’ Splitter, ‘Alankaar’ Finder and ‘Samaas’ Finder for Indo-Aryan and Dravidian Languages

MTStemmer: A multilevel stemmer for effective word pre-processing in Marathi

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