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Devi, Sobha Lalitha
- Neural Machine Translation for English-Malayalam
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1 AU-KBC Research Centre, MIT Campus of Anna University., IN
1 AU-KBC Research Centre, MIT Campus of Anna University., IN
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Research Cell: An International Journal of Engineering Sciences, Vol 35 (2023), Pagination: 01-10Abstract
Neural Machine Translation systems produce state-of-art translation for high resource languages. It is yet a challenge in low-resource and morphologically rich languages. In this paper, we have discussed the existing techniques in handling the morphologically rich and low-resource languages and presented our experiments on developing English-Malayalam NMT system where we have processed the data using different techniques namely word segmentation using morphological analyser and applying Byte pair Encoding (BPE) technique. The results show a significant improvement by implementing the word segmentation using morphological analyser.Keywords
Neural Machine Translation, Morphologically rich languages, Morph segmentation, Byte Pair Encoding.Full Text
References
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- Event Extraction from social media Text in Malayalam using Neural Conditional Random Fields
Abstract Views :72 |
PDF Views:0
Authors
Affiliations
1 AU-KBC Research Centre, MIT Campus of Anna University, Chennai, India., IN
1 AU-KBC Research Centre, MIT Campus of Anna University, Chennai, India., IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35 (2023), Pagination: 01-07Abstract
This paper describes a Neural Conditional Random Fields (NCRF) approach for Event extraction (EE) task which aims to discover different types of events along with the event arguments from the user generated text content (tweets) in Malayalam. The data for this work was obtained from FIRE (Forum for Information Retrieval and Evaluation) 2017 shared task [12] on Event Extraction from Newswires and Social Media Text in Indian Languages. A NCRF is a combination of Recurrent Neural Network (RNN) and Conditional Random Fields (CRF). In addition to event detection, the system also extracts the event arguments which contain the information related to the events such as when (Time), where (Place), Reason, Casualty, Aftereffect etc). Our proposed Event Extraction system achieves F-score of 79.74%. The results are encouraging and comparable with the state-of-art.Keywords
Event Extraction, Social Media Text, Indian Languages, Malayalam, Neural Conditional Random Fields (NCRF).Full Text
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