Deep Belief Network Based Part-of-Speech Tagger for Telugu Language

Jagadeesh, Madhu; M, Anand Kumar; Soman, K. P.

doi:10.1007/978-81-322-2526-3_9

Cited by 9 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural taggers generate comparable results to state-of-the-art systems and even surpass them. The works by Ma et al [2014], Wang et al [2015], Zennaki et al [2015], and Jagadeesh et al [2016] are some of the most recent in this field. We use the Bijankhan corpus in our experiments.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Boosting Neural POS Tagger for Farsi Using Morphological Information

Passban

Way

2016

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

is a low-resource language that suffers from the data sparsity problem and a lack of efficient processing tools. Due to their broad application in natural language processing tasks, part-of-speech (POS) taggers are one of those important tools that should be considered in this respect. Despite recent work on Farsi tagging, there is still room for improvement. The best reported accuracy so far is 96%, which in special cases can rise to 96.9%. The main problem with existing taggers is their inefficiency in coping with outof-vocabulary (OOV) words. Addressing both problems of accuracy and OOV words, we developed a neural network-based POS tagger (NPT) that performs efficiently on Farsi. Despite using less data, NPT provides better results in comparison to state-of-the-art systems. Our proposed tagger performs with an accuracy of 97.4%, with performance highly influenced by morphological features. We carry out a shallow morphological analysis and show considerable improvement over the baseline configuration.

show abstract

Section: Related Work and Discussionmentioning

confidence: 99%

Boosting Neural POS Tagger for Farsi Using Morphological Information

Passban

Way

2016

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

show abstract

“…After experimenting 92-95% and 85-87% accuracy achieved with 34 tags and 620 tags respectively. Jagadeesh et al [37] Kovida et al [43] discussed General Approaches (GA) used for language independent 'Sandhi' Splitter and the system has been tested on two languages Telugu and Malayalam. Devadath et al [44] conducted 'Sandhi' splitting experiment on Dravidian languages.…”

Section: Related Workmentioning

confidence: 99%

On State-of-the-art of POS Tagger, ‘Sandhi’ Splitter, ‘Alankaar’ Finder and ‘Samaas’ Finder for Indo-Aryan and Dravidian Languages

Gaikwad¹,

Jatinderkumar²

2021

IJACSA

View full text Add to dashboard Cite

Computational Linguistic refers to the development of the computer systems that deal with human languages. In this paper, different Computational Linguistic Techniques such as Parts of Speech (POS) tagger, 'Sandhi' Splitter, 'Alankaar' Finder and 'Samaas' Finder were considered. After a thorough literature review, it was found that fifteen techniques were used for POS tagging, nine techniques were used for 'Sandhi' splitting, one work is done for 'Alankaar' finder and absolutely no techniques are available for 'Samaas' finder for the Indo-Aryan as well as Dravidian languages. Analysis shows that Rule Based Approach (RBA) and Hidden Markov Model (HMM) are frequently used for POS tagging, RBA is most frequently used for 'Sandhi' Splitter, the general Human Intelligence (HI) is used for 'Alankaar' Finder and no 'Samaas' finder technique is available for any Indian language.

show abstract

“…There have been significant amounts of work related to general POS tagging tasks in Indian languages -Hindi, Oriya, Marathi, Punjabi, Bengali, Kannada, Malayalam, Tamil, Telugu, etc. [1,3,16,19,29,37,38,45,61,62,67]. In Ref.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Part-of-Speech Tagging for Malayalam Twitter Data (Special Issue: Deep Learning Techniques for Natural Language Processing)

Kumar

Soman

2019

Journal of Intelligent Systems

View full text Add to dashboard Cite

The paper addresses the problem of part-of-speech (POS) tagging for Malayalam tweets. The conversational style of posts/tweets/text in social media data poses a challenge in using general POS tagset for tagging the text. For the current work, a tagset was designed that contains 17 coarse tags and 9915 tweets were tagged manually for experiment and evaluation. The tagged data were evaluated using sequential deep learning methods like recurrent neural network (RNN), gated recurrent units (GRU), long short-term memory (LSTM), and bidirectional LSTM (BLSTM). The training of the model was performed on the tagged tweets, at word level and character level. The experiments were evaluated using measures like precision, recall, f1-measure, and accuracy. During the experiment, it was found that the GRU-based deep learning sequential model at word level gave the highest f1-measure of 0.9254; at character-level, the BLSTM-based deep learning sequential model gave the highest f1-measure of 0.8739. To choose the suitable number of hidden states, we varied it as 4, 16, 32, and 64, and performed training for each. It was observed that the increase in hidden states improved the tagger model. This is an initial work to perform Malayalam Twitter data POS tagging using deep learning sequential models.

show abstract

Deep Belief Network Based Part-of-Speech Tagger for Telugu Language

Cited by 9 publications

References 10 publications

Boosting Neural POS Tagger for Farsi Using Morphological Information

Boosting Neural POS Tagger for Farsi Using Morphological Information

On State-of-the-art of POS Tagger, ‘Sandhi’ Splitter, ‘Alankaar’ Finder and ‘Samaas’ Finder for Indo-Aryan and Dravidian Languages

Deep Learning Based Part-of-Speech Tagging for Malayalam Twitter Data (Special Issue: Deep Learning Techniques for Natural Language Processing)

Contact Info

Product

Resources

About