Papers by Vasudeva Varma
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs (N18-1)
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| Challenge: | Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in Knowledge Graph (KG). |
| Approach: | They propose to densify the Knowledge Graph (KG) with co-occurrence statistics and then use the densified KG to train entity embeddings. |
| Outcome: | The proposed system outperforms state-of-the-art EL systems on benchmark datasets and outperformed state- of-the art systems on sparsely connected entities in the KG. |
Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
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| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
| Outcome: | The proposed system outperforms baselines across standard metrics for the task of depression detection in text. |
Multi-label Categorization of Accounts of Sexism using a Neural Framework (D19-1)
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Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, Vasudeva Varma
| Challenge: | Sexism manifests in blatant as well as subtle ways, authors say . existing work on sexism classification has limitations in terms of categories used . authors: categorization of accounts of sexist behavior can aid in countering sextism . |
| Approach: | They propose a neural solution that can combine sentence representations with distributional and linguistic word embeddings. |
| Outcome: | a new method outperforms deep learning and traditional methods by an appreciable margin . the proposed method outpersforms several deep learning as well as traditional baselines by an approval margin compared to baselines . |
Predicting Clickbait Strength in Online Social Media (2020.coling-main)
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| Challenge: | Clickbaits are sensational, provocative or controversial posts that entice readers to click on them. |
| Approach: | They propose to model clickbait strength prediction using transformers to predict clickbaiting intensity. |
| Outcome: | The proposed model outperforms existing methods on a benchmark dataset with 39K posts on 3K posts. |
Extraction of Message Sequence Charts from Software Use-Case Descriptions (N19-2)
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Girish Palshikar, Nitin Ramrakhiyani, Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Vasudeva Varma, Pushpak Bhattacharyya
| Challenge: | Software Requirement Specification documents provide natural language descriptions of the core functional requirements as a set of use-cases. |
| Approach: | They propose a linguistic knowledge-based approach to extract software requirements from use-cases using a textual representation of the core functional requirements. |
| Outcome: | The proposed method performs better than existing techniques and improves performance. |
All Prompts Are Created Equal? Evaluating Robustness of LLM Judges Against Non-Adversarial Prompt Variations (2026.findings-acl)
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| Challenge: | Our work establishes new standards for assessing LLM judge reliability beyond simple accuracy metrics. |
| Approach: | They propose a diagnostic framework grounded in attribute verifiability that enables principled decisions about evaluation automation. |
| Outcome: | The proposed framework establishes new standards for assessing LLM judge reliability beyond simple accuracy metrics. |
When science journalism meets artificial intelligence : An interactive demonstration (D18-2)
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| Challenge: | Existing tools for automating science journalism do not provide adequate training for AIs to be trained. |
| Approach: | They propose an online tool that generates titles of blog titles by mimicking a human science journalist. |
| Outcome: | The proposed tool generates blog titles by mimicking a human science journalist . it is evaluated using standard metrics to show its viability . |
Identification of Alias Links among Participants in Narratives (P18-2)
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Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Girish Palshikar, Vasudeva Varma, Pushpak Bhattacharyya
| Challenge: | Identifying distinct and independent participants in a narrative is crucial for many NLP applications. |
| Approach: | They propose an approach based on linguistic knowledge for identification of aliases mentioned using proper nouns, pronouns or noun phrases with common noun headword. |
| Outcome: | The proposed approach performs better than the state-of-the-art approach on four diverse history narratives of varying complexity. |
Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification (2020.coling-main)
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| Challenge: | Sexism is a form of oppression based on one's sex and is reported online in numerous ways. |
| Approach: | They propose a multi-task approach for fine-grained multi-label sexism classification that leverages several supporting tasks without incurring manual labeling cost. |
| Outcome: | The proposed method outperforms the state-of-the-art for multi-label sexism classification on a recently released dataset across five standard metrics. |
A Workbench for Rapid Generation of Cross-Lingual Summaries (L18-1)
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| Challenge: | a tool for automating cross-lingual information access is needed in multilingual societies . current state of machine translation is not able to generate publishable articles from English . |
| Approach: | They propose a web-based tool for human editing of cross-lingual summaries . it generates publishable summary in a number of Indian Languages for news articles originally published in english . |
| Outcome: | The proposed tool can generate publishable summaries in multiple languages with minimal human effort and collect detailed logs on the process. |