Loitongbam Gyanendro Singh


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Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models
Loitongbam Gyanendro Singh | Junyu Mao | Rudra Mutalik | Stuart E. Middleton
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)

This paper explores the use of Large Language Models (LLMs) in analyzing social media content for mental health monitoring, specifically focusing on detecting and summarizing evidence of suicidal ideation. We utilized LLMs Mixtral7bx8 and Tulu-2-DPO-70B, applying diverse prompting strategies for effective content extraction and summarization. Our methodology included detailed analysis through Few-shot and Zero-shot learning, evaluating the ability of Chain-of-Thought and Direct prompting strategies. The study achieved notable success in the CLPsych 2024 shared task (ranked top for the evidence extraction task and second for the summarization task), demonstrating the potential of LLMs in mental health interventions and setting a precedent for future research in digital mental health monitoring.


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Detecting Moments of Change and Suicidal Risks in Longitudinal User Texts Using Multi-task Learning
Tayyaba Azim | Loitongbam Gyanendro Singh | Stuart E. Middleton
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

This work describes the classification system proposed for the Computational Linguistics and Clinical Psychology (CLPsych) Shared Task 2022. We propose the use of multitask learning approach with bidirectional long-short term memory (Bi-LSTM) model for predicting changes in user’s mood and their suicidal risk level. The two classification tasks have been solved independently or in an augmented way previously, where the output of one task is leveraged for learning another task, however this work proposes an ‘all-in-one’ framework that jointly learns the related mental health tasks. The experimental results suggest that the proposed multi-task framework outperforms the remaining single-task frameworks submitted to the challenge and evaluated via timeline based and coverage based performance metrics shared by the organisers. We also assess the potential of using various types of feature embedding schemes that could prove useful in initialising the Bi-LSTM model for better multitask learning in the mental health domain.


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Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding
Loitongbam Gyanendro Singh | Anasua Mitra | Sanasam Ranbir Singh
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Sentiment classification on tweets often needs to deal with the problems of under-specificity, noise, and multilingual content. This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple representations of a tweet and address the above issues. The generated representations are further ensembled and classified using a neural-based early fusion approach. Further, we propose a centrality aware random-walk for node embedding and tweet representations suitable for the multi-layer network. From various experimental analysis, it is evident that the proposed method can address the problem of under-specificity, noisy text, and multilingual content present in a tweet and provides better classification performance than the text-based counterparts. Further, the proposed centrality aware based random walk provides better representations than unbiased and other biased counterparts.


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Automatic Syllabification for Manipuri language
Loitongbam Gyanendro Singh | Lenin Laitonjam | Sanasam Ranbir Singh
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Development of hand crafted rule for syllabifying words of a language is an expensive task. This paper proposes several data-driven methods for automatic syllabification of words written in Manipuri language. Manipuri is one of the scheduled Indian languages. First, we propose a language-independent rule-based approach formulated using entropy based phonotactic segmentation. Second, we project the syllabification problem as a sequence labeling problem and investigate its effect using various sequence labeling approaches. Third, we combine the effect of sequence labeling and rule-based method and investigate the performance of the hybrid approach. From various experimental observations, it is evident that the proposed methods outperform the baseline rule-based method. The entropy based phonotactic segmentation provides a word accuracy of 96%, CRF (sequence labeling approach) provides 97% and hybrid approach provides 98% word accuracy.