Papers by Munindar Singh
Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Existing sentiment analysis models treat aspects and targets separately, causing conflicting sentiments. |
| Approach: | They propose an approach that jointly considers aspects and targets when inferring sentiments. |
| Outcome: | The proposed approach outperforms leading models by 1.6% to 4.3% on benchmark datasets . it uses selective attention mechanisms for selective attention between targets and context words . |
Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery (D19-1)
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| Challenge: | Existing approaches to inference opinionated text do not capture attributes in a one-off manner. |
| Approach: | They propose a probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. |
| Outcome: | The proposed model outperforms state-of-the-art models and yields intuitive topics. |
Pixie: Preference in Implicit and Explicit Comparisons (2022.acl-short)
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| Challenge: | Existing studies on preference classification focus on explicit comparisons, but recent work has focused on indirect comparisons which lack comparative quantifiers and adjectives. |
| Approach: | They propose a manual annotated dataset for preference classification that includes 8,890 app reviews. |
| Outcome: | The proposed model outperforms the state-of-the-art model and achieves a weighted average F1 score of 83.34%. |
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations (D18-1)
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| Challenge: | Existing models for finding aspects and sentiments in opinionated texts ignore sentiments and are not supervised. |
| Approach: | They propose a probabilistic model that finds aspects and sentiments in opinionated texts . they use authors, discourse relations, and word embeddings to capture regularities . |
| Outcome: | The proposed model outperforms state-of-the-art models in topic cohesion and sentiment classification. |
Lin: Unsupervised Extraction of Tasks from Textual Communication (2020.coling-main)
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| Challenge: | Identifying tasks from emails and chats is a hallmark of collaborative communication . state-of-the-art approaches for task identification rely on large annotated datasets . |
| Approach: | They propose an unsupervised approach to identifying tasks that leverages dependency parsing and VerbNet. |
| Outcome: | The proposed approach yields comparable or more accurate results than supervised models on unseen domains. |