Jaya Saraswati


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Synset Ranking of Hindi WordNet
Sudha Bhingardive | Rajita Shukla | Jaya Saraswati | Laxmi Kashyap | Dhirendra Singh | Pushpak Bhattacharyya
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Word Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context. It has been observed that such approaches often find it difficult to beat the WordNet First Sense (WFS) baseline which assigns the sense irrespective of context. In this paper, we present our work on creating the WFS baseline for Hindi language by manually ranking the synsets of Hindi WordNet. A ranking tool is developed where human experts can see the frequency of the word senses in the sense-tagged corpora and have been asked to rank the senses of a word by using this information and also his/her intuition. The accuracy of WFS baseline is tested on several standard datasets. F-score is found to be 60%, 65% and 55% on Health, Tourism and News datasets respectively. The created rankings can also be used in other NLP applications viz., Machine Translation, Information Retrieval, Text Summarization, etc.

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How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature
Aditya Joshi | Vaibhav Tripathi | Pushpak Bhattacharyya | Mark Carman | Meghna Singh | Jaya Saraswati | Rajita Shukla
Proceedings of the Australasian Language Technology Association Workshop 2016

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How Do Cultural Differences Impact the Quality of Sarcasm Annotation?: A Case Study of Indian Annotators and American Text
Aditya Joshi | Pushpak Bhattacharyya | Mark Carman | Jaya Saraswati | Rajita Shukla
Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities