Srinath Nair


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TEASER: Towards Efficient Aspect-based SEntiment Analysis and Recognition
Vaibhav Bajaj | Kartikey Pant | Ishan Upadhyay | Srinath Nair | Radhika Mamidi
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task and report the results on the novel Movie20 dataset as well.

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professionals@DravidianLangTech-EACL2021: Malayalam Offensive Language Identification - A Minimalistic Approach
Srinath Nair | Dolton Fernandes
Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages

The submission is being made as a working note as part of the Offensive Language Identification in Dravidian Languages shared task. The proposed model “DrOLIC” uses IndicBERT and a simple 4-layered MLP to do the multiclass classification problem and we achieved an F1 score of 0.85 on the Malayalam dataset.