Papers by Sabyasachi Kamila
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. |
| Approach: | They propose a weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. |
| Outcome: | The proposed framework outperforms weakly supervised baselines on four benchmark datasets and is able to generate multiple aspect category-sentiment pairs per review sentence. |
Sentence Level Temporality Detection using an Implicit Time-sensed Resource (L18-1)
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| Challenge: | Temporal sense detection of any word is an important aspect for detecting temporality at the sentence level. |
| Approach: | They build a temporal resource based on a semi-supervised learning approach . they use past, present, future, neutral and atemporal senses to tag sentences . |
| Outcome: | The proposed resource is based on a semi-supervised learning approach . it is used to tag sentences with past, present and future temporal senses . |
Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation (2025.findings-emnlp)
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| Challenge: | Sign Language Translation evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. |
| Approach: | We conduct signer-fold cross-validation on three leading SLT models . they find that under signer independent evaluation performance drops sharply . |
| Outcome: | a signer-dependent evaluation can substantially overestimate SLT capability, the authors say . they recommend adopting signer independent protocols to ensure generalisation to unseen signers . |
Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates (N18-1)
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| Challenge: | Temporal orientation refers to an individual’s tendency to connect to the psychological concepts of past, present or future and affects personality, motivation, emotion, decision making and stress coping processes. |
| Approach: | They propose to use a minimally supervised method to classify tweets in one of three temporal categories, past, present, and future, and a deep bi-directional long-term memory (BLSTM) to measure correlation between sentiment view of temporal orientation and different psycho-demographic factors. |
| Outcome: | The proposed method achieves 78.27% accuracy on a manually created test set. |