Aman Sinha


2023

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What shall we read : the article or the citations? - A case study on scientific language understanding
Aman Sinha | Sam Bigeard | Marianne Clausel | Mathieu Constant
Actes de CORIA-TALN 2023. Actes de l'atelier "Analyse et Recherche de Textes Scientifiques" (ARTS)@TALN 2023

The number of scientific articles is increasing tremendously across all domains to such an extent that it has become hard for researchers to remain up-to-date. Evidently, scientific language understanding systems and Information Extraction (IE) systems, with the advancement of Natural Language Processing (NLP) techniques, are benefiting the needs of users. Although the majority of the practices for building such systems are data-driven, advocating the idea of “The more, the better”. In this work, we revisit the paradigm - questioning what type of data : text (title, abstract) or citations, can have more impact on the performance of scientific language understanding systems.

2022

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IAI @ SocialDisNER : Catch me if you can! Capturing complex disease mentions in tweets
Aman Sinha | Cristina Garcia Holgado | Marianne Clausel | Matthieu Constant
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

Biomedical NER is an active research area today. Despite the availability of state-of-the-art models for standard NER tasks, their performance degrades on biomedical data due to OOV entities and the challenges encountered in specialized domains. We use Flair-NER framework to investigate the effectiveness of various contextual and static embeddings for NER on Spanish tweets, in particular, to capture complex disease mentions.

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Word Sense Disambiguation of French Lexicographical Examples Using Lexical Networks
Aman Sinha | Sandrine Ollinger | Mathieu Constant
Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing

This paper focuses on the task of word sense disambiguation (WSD) on lexicographic examples relying on the French Lexical Network (fr-LN). For this purpose, we exploit the lexical and relational properties of the network, that we integrated in a feedforward neural WSD model on top of pretrained French BERT embeddings. We provide a comparative study with various models and further show the impact of our approach regarding polysemic units.

2020

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C-Net: Contextual Network for Sarcasm Detection
Amit Kumar Jena | Aman Sinha | Rohit Agarwal
Proceedings of the Second Workshop on Figurative Language Processing

Automatic Sarcasm Detection in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on its context. This paper presents our proposed model, C-Net, which takes contextual information of a sentence in a sequential manner to classify it as sarcastic or non-sarcastic. Our model showcases competitive performance in the Sarcasm Detection shared task organised on CodaLab and achieved 75.0% F1-score on the Twitter dataset and 66.3% F1-score on Reddit dataset.

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DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction
Aadarsh Singh | Priyanshu Kumar | Aman Sinha
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We explore the performance of Bidirectional Encoder Representations from Transformers (BERT) at definition extraction. We further propose a joint model of BERT and Text Level Graph Convolutional Network so as to incorporate dependencies into the model. Our proposed model produces better results than BERT and achieves comparable results to BERT with fine tuned language model in DeftEval (Task 6 of SemEval 2020), a shared task of classifying whether a sentence contains a definition or not (Subtask 1).

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DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis
Pradyumna Gupta | Himanshu Gupta | Aman Sinha
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Memes have become an ubiquitous social media entity and the processing and analysis of such multimodal data is currently an active area of research. This paper presents our work on the Memotion Analysis shared task of SemEval 2020, which involves the sentiment and humor analysis of memes. We propose a system which uses different bimodal fusion techniques to leverage the inter-modal dependency for sentiment and humor classification tasks. Out of all our experiments, the best system improved the baseline with macro F1 scores of 0.357 on Sentiment Classification (Task A), 0.510 on Humor Classification (Task B) and 0.312 on Scales of Semantic Classes (Task C).

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DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa
Sirigireddy Dhana Laxmi | Rohit Agarwal | Aman Sinha
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.