An Automatic Summarizer for a Low-Resourced Language

Pattnaik, Sagarika; Nayak, Ajit Kumar

doi:10.1007/978-981-15-1081-6_24

Cited by 2 publications

(1 citation statement)

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“…As Odia is morphologically rich (Pattnaik and Nayak, 2020) the model should be approached with feature based methodologies to have a performance comparison and maximum entropy concept exploits the features effectively in POS tag prediction (Ekbal et al, 2008;Ratnaparkhi, 1996). Thus these concepts are further experimented in the article on Odia language.…”

Section: Transition Probabilitymentioning

confidence: 99%

A Modified Markov-Based Maximum-Entropy Model for POS Tagging of Odia Text

Pattnaik

Nayak

2022

International Journal of Decision Support System Technology

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POS (Parts of Speech) tagging, a vital step in diverse Natural Language Processing (NLP) tasks has not drawn much attention in case of Odia a computationally under-developed language. The proposed hybrid method suggests a robust POS tagger for Odia. Observing the rich morphology of the language and unavailability of sufficient annotated text corpus a combination of machine learning and linguistic rules is adopted in the building of the tagger. The tagger is trained on tagged text corpus from the domain of tourism and is capable of obtaining a perceptible improvement in the result. Also an appreciable performance is observed for news articles texts of varied domains. The performance of proposed algorithm experimenting on Odia language shows its manifestation in dominating over existing methods like rule based, hidden Markov model (HMM), maximum entropy (ME) and conditional random field (CRF).

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Section: Transition Probabilitymentioning

confidence: 99%