Kannpos-Kannada Parts of Speech Tagger Using Conditional Random Fields

Pallavi, K; Pillai, Anitha S.

doi:10.1007/978-81-322-2553-9_43

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…The pos tagger developed by the author achieved an accuracy of 92.8% (Pallavi and Pillai, 2015) and the new NP chunker with 95.32% accuracy was developed for the task were used to improve Kannada NER system. The system was trained using pos and chunk information in the beginning.…”

Section: Resultsmentioning

confidence: 99%

“…POS tagger using CRFs has been developed. There are three noun categories (common noun, proper noun, location) in the pos tagset (Pallavi and Pillai, 2015) which helped to identify named entities. Often, NE is always represented as a proper noun.…”

Section: Features Used For Kannada Ner Posmentioning

confidence: 99%

“…Currently there are no freely available large online corpus and gazetteer lists for Kannada. There are only few NERs, pos taggers and chunk taggers reported (Amarappa and Sathyanarayana, 2013a;Bhuvaneshwari, 2014;Pallavi and Pillai, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Named Entity Recognition for Kannada using Gazetteers list with Conditional Random Fields

Pallavi

Sobha

Ramya

2018

Journal of Computer Science

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Named Entities (NEs) that exist in the sentences are essential to build Natural Language Processing (NLP) applications for Information Extraction (IE) from large corpora. However, generating a large corpus is challenging for resource poor languages, such as Kannada. Further, there is no annotated corpus available online. The challenges faced in annotating NEs with pre-defined classes are: It is morphologically joined with other words and the spelling variations are more frequent for Kannada words. Sentence structure varies according to morphology, parts of speech (pos) and chunking of a language. These parameters differ from one language to another. To address these challenges, a novel application system is proposed to identify NEs in Kannada using a large corpus of 73,676 tokens. The Named Entity Recognition (NER) system consist of a robust pos tagger and Noun Phrase (NP) chunker developed for generic data. Five gazetteer lists were created from many orthographic patterns for each word. Context information such as previous two words, next two words, word morphology and gazetteer lists were added to feature lists. An unigram-bigram template was designed and incorporated into Conditional Random Fields (CRFs) to generate conditional feature functions. The proposed system resulted in 86.85% and 71.01% f-measure for gold test data and newspaper data respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Features Used For Kannada Ner Posmentioning

confidence: 99%

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Named Entity Recognition for Kannada using Gazetteers list with Conditional Random Fields

Pallavi

Sobha

Ramya

2018

Journal of Computer Science

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“…In SVM based POS tagging, multi-class classification is tackled by taking one POS tag at a time as a positive class, and the rest as negative (Fernando et al 2016). Conditional Random Fields (CRF) (Lafferty, McCallum, and Pereira 2001) are used to build probabilistic models that are able to segment and label the sequence data (Pallavi and Pillai 2014), and are in fact random fields globally conditioned on the observations that might range over natural sentences (Lafferty, McCallum, and Pereira 2001). They are often used in various natural language processing tasks (e.g.…”

Section: Lemmatizationmentioning

confidence: 99%

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

Braović,

Krstinić,

Štula

et al. 2024

Computational Linguistics

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This paper provides a detailed insight into computational approaches for deciphering Bronze Age Aegean and Cypriot scripts, namely the Archanes script and the Archanes formula, Phaistos Disk, Cretan hieroglyphic (including the Malia Altar Stone and Arkalochori Axe), Linear A, Linear B, Cypro-Minoan and Cypriot scripts. The unique contributions of this paper are threefold: 1) a thorough review of major Bronze Age Aegean and Cypriot scripts and inscriptions, digital data and corpora associated with them, existing computational decipherment methods developed in order to decipher them, and possible links to other scripts and languages; 2) the definition of 15 major challenges that can be encountered in computational decipherments of ancient scripts; and 3) an outline of a computational model that could possibly be used to simulate traditional decipherment processes of ancient scripts based on palaeography and epigraphy. In the context of this paper the term decipherment denotes the process of discovery of the language and/or the set of symbols behind an unknown script, and the meaning behind it.

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“…It is also inflexible to the evolution of the language. K. P. Pallavi and Anitha S. Pillai [4] also use CRFs for POS tagging. They develop a tagger using an 80,000-word corpus created from Kannada Wikipedia.…”

Section: Related Workmentioning

confidence: 99%

A comparison of features for POS tagging in Kannada

Shriya

Shahi

Rao

et al. 2018

IJET

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This paper proposes a system of part of speech tagging for the South Indian language Kannada using supervised machine learning. POS tagging is an important step in Natural Language Processing and has varied applications such as word sense disambiguation, natural language understanding etc. Based on extensive research into methods used for POS tagging, Conditional Random fields have been chosen as our algorithm. CRFs are used for sequence modeling in POS tagging, named entity recognition and as an alternative to Hidden Markov Models. Three very large corpora are used and their results are compared. The feature sets for all three corpora are also varied. The best method for the task is determined using these results.

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Kannpos-Kannada Parts of Speech Tagger Using Conditional Random Fields

Cited by 10 publications

References 9 publications

Named Entity Recognition for Kannada using Gazetteers list with Conditional Random Fields

Named Entity Recognition for Kannada using Gazetteers list with Conditional Random Fields

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

A comparison of features for POS tagging in Kannada

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