Elizabeth Sherly


2020

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A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
Bharathi Raja Chakravarthi | Navya Jose | Shardul Suryawanshi | Elizabeth Sherly | John Philip McCrae
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.

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Malayalam Speech Corpus: Design and Development for Dravidian Language
Lekshmi K R | Jithesh V S | Elizabeth Sherly
Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation

To overpass the disparity between theory and applications in language-related technology in the text as well as speech and several other areas, a well-designed and well-developed corpus is essential. Several problems and issues encountered while developing a corpus, especially for low resource languages. The Malayalam Speech Corpus (MSC) is one of the first open speech corpora for Automatic Speech Recognition (ASR) research to the best of our knowledge. It consists of 250 hours of Agricultural speech data. We are providing a transcription file, lexicon and annotated speech along with the audio segment. It is available in future for public use upon request at “www.iiitmk.ac.in/vrclc/utilities/ml_speechcorpus”. This paper details the development and collection process in the domain of agricultural speech corpora in the Malayalam Language.

2017

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Word Sense Disambiguation for Malayalam in a Conditional Random Field Framework
Junaida M K | Jisha P Jayan | Elizabeth Sherly
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

2015

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Proceedings of the 12th International Conference on Natural Language Processing
Dipti Misra Sharma | Rajeev Sangal | Elizabeth Sherly
Proceedings of the 12th International Conference on Natural Language Processing

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Isolated Word Recognition System for Malayalam using Machine Learning
Maya Moneykumar | Elizabeth Sherly | Win Sam Varghese
Proceedings of the 12th International Conference on Natural Language Processing

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A Study on Divergence in Malayalam and Tamil Language in Machine Translation Perceptive
Jisha P Jayan | Elizabeth Sherly
Proceedings of the 12th International Conference on Natural Language Processing

2013

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A Hybrid Statistical Approach for Named Entity Recognition for Malayalam Language
Jisha P Jayan | Rajeev R R | Elizabeth Sherly
Proceedings of the 11th Workshop on Asian Language Resources