Parts of Speech Tagging for Kannada and Hindi Languages using ML and DL models

Advaith, V; Shivkumar, Anushka; Lakshmi, B. Samrajya

doi:10.1109/conecct55679.2022.9865745

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…Ex: A word may be annotated as a noun in the preceding statement and an adjective or verb in a succeeding statement. Table 2 visualizes a set of POS (21) tags and Figure 4 illustrates an example sentence (22) . 8.…”

Section: Preprocessing and Cleaning Datasetmentioning

confidence: 99%

HiTEK Pre-processing for Speech and Text: NLP

Rudrappa¹,

Reddy²,

Hanumanthappa³

2023

IJST

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Objective: To develop a system that accepts a sentence consisting of two and/or four languages and convert it to a target language text, termed as Cross Language Speech Identification and Text Translation System. Methods: A combinatorial model consisting of Hidden Markov Model, Artificial Neural Networks, Deep Neural Networks and Gaussian Mixture Model are utilized for direct and indirect speech mapping. Trained dataset consisting of thousand phonemes for each of the Hindi, Telugu, English and Kannada languages, initially for bank, hospital domains, later the grammatical phonemes of each language were added and wave files consisting of cross lingual spoken sentence were created which incurred a six months period to build from scratch, as cross lingual vocal data-set is not available. Hindi language dataset Shabdanjali was also referred. The basic parameters considered for creation of structured dataset are loudness, pause, pitch, tone, noise cancellation, sampling frequency, threshold etc. Findings: Comparative analysis of various techniques, target languages and features are tabulated. Research idea emerged from the comparative analysis of Monolingual Systems where there was a gap for cross lingual speech to text translation. The architecture can be enhanced in future for other regional languages of India. Novelty: A new bench mark for Cross Language dataset was created. This work presents CLSITT tool applicable in transforming public speeches spoken in multiple languages to a selected target language and the tool is helpful for a regional news editor, rural and agricultural activities, medical applications, defence and so on.

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Section: Preprocessing and Cleaning Datasetmentioning

confidence: 99%

HiTEK Pre-processing for Speech and Text: NLP

Rudrappa¹,

Reddy²,

Hanumanthappa³

2023

IJST

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“…In question-answering (QA) systems, WSD assumes a significant role in identifying the appropriate types of answers that correspond to a given question type [13,14]. Furthermore, WSD is necessary to accurately assign the appropriate part of speech tagging (POS) to a word, as its POS can vary depending on the contextual usage [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Literature Survey on Word Sense Disambiguation for the Hindi Language

Gujjar,

Mago,

Kumari

et al. 2023

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Word sense disambiguation (WSD) is a process used to determine the most appropriate meaning of a word in a given contextual framework, particularly when the word is ambiguous. While WSD has been extensively studied for English, it remains a challenging problem for resource-scarce languages such as Hindi. Therefore, it is crucial to address ambiguity in Hindi to effectively and efficiently utilize it on the web for various applications such as machine translation, information retrieval, etc. The rich linguistic structure of Hindi, characterized by complex morphological variations and syntactic nuances, presents unique challenges in accurately determining the intended sense of a word within a given context. This review paper presents an overview of different approaches employed to resolve the ambiguity of Hindi words, including supervised, unsupervised, and knowledge-based methods. Additionally, the paper discusses applications, identifies open problems, presents conclusions, and suggests future research directions.

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Statistical and Deep Learning Approach for Kannada Parts of Speech Tagger

Hegde,

Hegde

et al. 2023

2023 IEEE International Carnahan Conference on Security Technology (ICCST)

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Parts of Speech Tagging for Kannada and Hindi Languages using ML and DL models

Cited by 7 publications

References 7 publications

HiTEK Pre-processing for Speech and Text: NLP

HiTEK Pre-processing for Speech and Text: NLP

A Literature Survey on Word Sense Disambiguation for the Hindi Language

Statistical and Deep Learning Approach for Kannada Parts of Speech Tagger

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