Part of Speech Tagging in Odia Using Support Vector Machine

Das, Bishwa Ranjan; Sahoo, Smrutirekha; Panda, C. S.; Patnaik, Srikanta

doi:10.1016/j.procs.2015.04.127

Cited by 19 publications

(4 citation statements)

References 2 publications

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“…POS tagging is useful for a variety of NLP tasks, such as information extraction, entity recognition, and grammatical structure identification. It automatically assigns the parts of speech tags to the tokens considering two main aspects: finding the exact tags for each token and choosing between the possible tags for ambiguous tokens [81][82][83]. Figure 2 shows an example of the output of a POS tagger in regard to two different sentences considering the eight classes of parts of speech tags [77] as Nouns, Verbs, Adjectives, Pronouns, Determiners, Adverbs, Prepositions, and Conjunctions (Table 2).…”

Section: The Taskmentioning

confidence: 99%

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Besharati Moghaddam,

Lopez,

De Vuyst

et al. 2024

Applied Sciences

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Manufacturing industry faces increasing complexity in the performance of assembly tasks due to escalating demand for complex products with a greater number of variations. Operators require robust assistance systems to enhance productivity, efficiency, and safety. However, existing support services often fall short when operators encounter unstructured open questions and incomplete sentences due to primarily relying on procedural digital work instructions. This draws attention to the need for practical application of natural language processing (NLP) techniques. This study addresses these challenges by introducing a domain-specific dataset tailored to assembly tasks, capturing unique language patterns and linguistic characteristics. We explore strategies to process declarative and imperative sentences, including incomplete ones, effectively. Thorough evaluation of three pre-trained NLP libraries—NLTK, SPACY, and Stanford—is performed to assess their effectiveness in handling assembly-related concepts and ability to address the domain’s distinctive challenges. Our findings demonstrate the efficient performance of these open-source NLP libraries in accurately handling assembly-related concepts. By providing valuable insights, our research contributes to developing intelligent operator assistance systems, bridging the gap between NLP techniques and the assembly domain within manufacturing industry.

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Section: The Taskmentioning

confidence: 99%

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Besharati Moghaddam,

Lopez,

De Vuyst

et al. 2024

Applied Sciences

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“… [ [30] , [31] , [32] , [33] ] SVM Support vector machine (SVM) is a binary classifier with advantages in few-shot classification, such as pathological voice detection. [ [34] , [35] , [36] , [37] ] DNN Consists of fully connected layers and is popular in learning a hierarchy of invariant and discriminative features. Features learned by DNNs are more generalized than the traditional hand-crafted features.…”

Section: Overview Of Intelligent Speech Technologiesmentioning

confidence: 99%

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang

Qiu

et al. 2023

Computers in Biology and Medicine

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“…No clear information about the tag set and the evaluation metric is mentioned. An SVM based Odia POS tagger (Das et al, 2015) reported of obtaining an accuracy of 82% with a small tag set of five tags with a training size of 10,000 words. Another Odia SVM POS tagger (Ojha et al, 2015) framed on BIS tag set with a training size of 90k words and testing size of 2k words has been reported.…”

Section: Related Workmentioning

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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Part of Speech Tagging in Odia Using Support Vector Machine

Cited by 19 publications

References 2 publications

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

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

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