Part of Speech Tagging in Manipuri: A Rule based Approach

Singha, Kh Raju; Purkayastha, Bipul Syam; Singha, Kh. Dhiren

doi:10.5120/8111-1727

Cited by 9 publications

(10 citation statements)

References 7 publications

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“…The existing POS tagging systems for Manipuri languages along with the techniques adopted are given in the following table. A set of 3 types of rules-orthographic, morphological and disambiguation has been applied along with the use of a lexicon by [24]. A 3-tier tagset comprising of major category, sub-category and the attributes has been designed.…”

Section: Pos Taggingmentioning

confidence: 99%

Advancements on NLP Applications for Manipuri Language

Devi¹,

Purkayastha²

2018

IJNLC

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Manipuri is both a minority and morphologically rich language with genetic features similar to Tibeto Burman languages. It has Subject-Object-Verb (SOV) order, agglutinative verb morphology and is monosyllabic. Morphology and syntax are not clearly distinguished in this language. Natural Language Processing (NLP) is a useful research field of computer science that deals with processing of a large amount of natural language corpus. The NLP applications encompass E-Dictionary, Morphological Analyzer, Reduplicated Multi-Word Expression (RMWE), Named Entity Recognition (NER), Part of Speech (POS) Tagging, Machine Translation (MT), Word Net, Word Sense Disambiguation (WSD) etc. In this paper, we present a study on the advancements in NLP applications for Manipuri language, at the same time presenting a comparison table of the approaches and techniques adopted and the results obtained of each of the applications followed by a detail discussion of each work.

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Section: Pos Taggingmentioning

confidence: 99%

Advancements on NLP Applications for Manipuri Language

Devi¹,

Purkayastha²

2018

IJNLC

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“…However, in this way, the particularities of this kind of communication are removed, and not studied. Other procedures experienced in adapting a POS-tagger for noisy texts are described in the papers [8,9,17].…”

Section: Related Workmentioning

confidence: 99%

Social Media – Processing Romanian Chat and Discourse Analysis

Mărănduc

Perez

Simionescu

2016

CyS

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In order to obtain a balanced corpus, a subcorpus of 2,576 sentences illustrating contemporary social media language has been added to the Dependency Treebank for Romanian. The texts were taken from the chat. The subject of this paper is to describe the second step of processing non-standard texts with a hybrid POS-tagger for Romanian and with a Malt parser, both until now trained on standard language and on other styles of communication. The results obtained show that the UAIC tools are comparable with the tools for other languages trained on similar corpora. Another purpose is to develop this resource, the Dependency Treebank for Romanian, not only quantitatively, doubling its dimension in a year, but also changing its format with a new one, compatible with other similar foreign corpora, and adding new, more complex annotation layers. A semantic layer and a discursive annotation will be added, permitting the study of discursive and conversational particularities. Finally, examples illustrating discursive particularities of the chat communication are discussed.

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“…Jeff et al [25] added the term to the class similarity computation, tending to have a higher priority for smaller classes to be merged. In our experiments we set ≈ 0.…”

Section: Developing Part-of-speech Set Methodsmentioning

confidence: 99%

“…Mart [23] used 47 tags to build a Spanish treebank in Spanish. For developed part-of-speech tagger, Avontuur et al [24] used 25 tags for Dutch, Singha et al [25] used 97 tags for Manipuri, Neunerdt et al [26] used 54 tags for German.…”

Section: Introductionmentioning

confidence: 99%

A Novel Part-of-Speech Set Developing Method for Statistical Machine Translation

Sujaini¹,

Kuspriyanto²,

Arman³

et al. 2014

TELKOMNIKA

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Part of speech (PoS) IntroductionThe dream of automatically translating documents between two languages is one of the oldest pursuits of artificial intelligence research. Now, armed with vast amounts of example translations and powerful computers, we can witness significant progress toward achieving that dream. Statistical analysis of bilingual parallel corpora allow for the automatic construction of machine translation systems. Already, for some language pairs, statistical systems are the best machine translation systems currently available.Statistical Machine Translation is corpus-based and consequently requires a parallel corpus to learn a model [1], [2]. Parallel corpora are different from normal text corpora in that they are not just a collection of texts, but are bilingual or multilingual and structured so that every sentence is linked to its translations.Some works have shown that the translation quality can be increased by using additional features such as lemma, part of speech (PoS), gender and others. In their research, Koehn and Hoang [3] explained that by adding a factor of part-of-speech in English-German translator system, the quality of the translation was increased from 18.04% to 18.15%. They also showed that by using morphological factors and part-of-speech, the English-Spanish translator system quality was increased from 23.41% to 24.25%.Youssef et al.[4] examined the factors on adding part-of-speech on statistical translation system for English-Arabic. Research results showed that the addition of a factor of part-of-speech can improve the quality of translation from 0.6095% to 0.6394%. Razavian and Vogel [5] examined the factors on adding to the statistics based interpreter systems, for EnglishIraqi interpreter system, the quality of the translation was improved from 15.62% to 16.41%; for the Spanish-English translator system, the quality of the translation was improved from 32.53% to 32.84%; and for Arabic-English translator system, the quality of the translation was improved from 41.70% to 42.74%.For English-Indonesian, Sujaini et al.[6] conducted a study of the addition of PoS factors based on a statistical translator system factors. The results of these studies indicated that the PoS factor increased the quality of the English-Indonesian translation of 2%, from 31.26% to 33.26%.Grammatically, words can be divided into two categories: open class and closed class. Open class is a class category which number of words always increases over time, while closed class is a class category whose words are fixed. Grammatically different categories of words, commonly called Part of Speech [1].

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Part of Speech Tagging in Manipuri: A Rule based Approach

Cited by 9 publications

References 7 publications

Advancements on NLP Applications for Manipuri Language

Advancements on NLP Applications for Manipuri Language

Social Media – Processing Romanian Chat and Discourse Analysis

A Novel Part-of-Speech Set Developing Method for Statistical Machine Translation

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