POS Tagger for Malayalam using Hidden Markov Model

Nambiar, Sindhya K; Leons, Antony; Jose, Soniya; Arunsree,

doi:10.1109/icssit46314.2019.8987786

Cited by 9 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, Unigram showed an accuracy of 88.37% [3]. POS Tagging is also applied to the Malayalam Language by Sindhya K Nambiar, et al using the Hidden Markov Model [4], which can show improvisation results by being 85% of the 13 sentence tags for performance.…”

Section: Introductionmentioning

confidence: 67%

See 1 more Smart Citation

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

Setyono

Richasdy

Purbolaksono

2022

bits

View full text Add to dashboard Cite

News is a medium of daily information usually obtained by the public. The news consists of a lot of information in it and is composed of sentence structures. Each language is unique with its own sentence structure, like Indonesian and other foreign languages. But nowadays, many media mix Indonesian with foreign languages, making the sentence structure different from Bahasa Indonesia. To classify these words, Part Of Speech Tagging needed to determine the class of words composed of sentences by learning from the Corpus of each language. With the new sentence structure, POS Tagger requires a larger Corpus to learn. The language structure can determine the results of tagging from the POS Tagger. If there are words that are not in the Corpus, it can reduce the accuracy of the POS Tagger. We conducted to enhance the research results by adding data with a different sentence structure from the Indonesian Language Corpus using sentences from online media. Added about 242 sentences with 7,043 tokens on Corpus focused on Foreign Word tags, which total 3819 tags. After doing some testing and scenarios, the results of the accuracy of POS Tagger show an accuracy of 94.7% using the Hidden Markov Model method with the F1-Score tag FW 78%.

show abstract

Section: Introductionmentioning

confidence: 67%

“…This method is a statistical model of a system imagined by a Markov process using unknown parameters, selecting hidden state parameters from parameters that can be seen and observed state. As in POS Tagging, the word class tag is invisible, but the word can be seen [4].…”

Section: Introductionmentioning

confidence: 99%

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

Setyono

Richasdy

Purbolaksono

2022

bits

View full text Add to dashboard Cite

show abstract

“…It has featured in a limited number of NLP tasks, including morphological analysis (Bhavukam et al, 2018), POS tagging (Akhil et al, 2020) and NER (Ajees and Idicula, 2018). However, many studies use small locally generated data sets (Nambiar et al, 2019) or domain specific data sets (Kumar et al, 2019), (Devi et al, 2016), which usually are not freely available.…”

Section: Malayalammentioning

confidence: 99%

EmployingWikipedia as a Resource for Named Entity Recognition in Morphologically Complex Under-Resourced Languages

Krishnan¹,

Ziehe²,

Pannach³

et al. 2021

Proceedings of the 14thWorkshop on Building and Using Comparable Corpora (BUCC 2021)

View full text Add to dashboard Cite

We propose a novel approach for rapid prototyping of named entity recognisers through the development of semi-automatically annotated data sets. We demonstrate the proposed pipeline on two under-resourced agglutinating languages: the Dravidian language Malayalam and the Bantu language isiZulu. Our approach is weakly supervised and bootstraps training data from Wikipedia and Google Knowledge Graph. Moreover, our approach is relatively language independent and can consequently be ported quickly (and hence cost-effectively) from one language to another, requiring only minor language-specific tailoring.

show abstract

“…In another study conducted by Sindhya KN, et al in 2019 [17]. The purpose of this study is to examine the Malayalam language because the existing research so far is the only research on common languages such as English.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Telkom University News Subjects on Popular Indonesian News Portals Using a Combination of Hidden Markov Model (HMM) and Rule Based Methods

Al-Farrel

Richasdy²,

Purbolaksono³

2022

mib

View full text Add to dashboard Cite

News media are often found in everyday life as a means of information for the public about something that is happening. In news articles, it is common to see several sentences that support the object to increase its popularity by being promoted by the subject. Part of Speech Tagging can determine the class of words in the sentence according to Tagsets provided by the corpus. That way, the search for the subject in the news article can be found from the word class obtained from a corpus. This research was focused on finding the subject "who" repeatedly spreading the news about Telkom University by using Part of Speech Tagging with the Hidden Markov Model and Rule Based on a news dataset from popular news portals about Telkom University. The process is taking all news about Telkom University on popular news portals and classifying it using the Hidden Markov Model and Rule-Based. We conducted to enhance the research results by changing the probability estimator on Hidden Markov Model. After running some scenarios, the best results obtained by the Hidden Markov Model and Rule-Based are the Accuracy of 94.96%, the Precision of 94.99%, the Recall of 94.96%, and the F1-Score of 94.95%.

show abstract

POS Tagger for Malayalam using Hidden Markov Model

Cited by 9 publications

References 3 publications

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News

EmployingWikipedia as a Resource for Named Entity Recognition in Morphologically Complex Under-Resourced Languages

Analysis of Telkom University News Subjects on Popular Indonesian News Portals Using a Combination of Hidden Markov Model (HMM) and Rule Based Methods

Contact Info

Product

Resources

About