Advait Deshmukh

Advait Deshmukh

Graduate Student

Publications

A Structured Clustering Approach for Inducing Media Narratives

Recommended to ACL 2026, ACL ARR, 2025

Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures that communication theory emphasizes as central to how meaning is constructed. Existing approaches either miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. To bridge this gap, we present a framework for inducing rich narrative themes by jointly modeling events and characters via structured clustering. Our approach produces explainable narrative themes that align with established framing theory while scaling to large corpora without exhaustive manual annotation.

Recommended citation: Rohan Das, Advait Deshmukh, Alexandria Leto, Zohar Naaman, I-Ta Lee and Maria Leonor Pacheco https://drive.google.com/file/d/1P23DLGdzBhJU3jPNfMmJmPvkmfYn7p59/

All Entities are Not Created Equal: Examining the Long Tail for Fine-Grained Entity Typing

Published in Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025, to appear), 2025

Pre-trained language models (PLMs) are trained on large amounts of data, which helps capture world knowledge alongside linguistic competence. Due to this, they are extensively used for ultra-fine entity typing tasks, where they provide the entity knowledge held in its parameter space. Given that PLMs learn from co-occurrence patterns, they likely contain more knowledge or less knowledge about entities depending on their how frequent they are in the pre-training data. In this work, we probe PLMs to elicit encoded entity probabilities and demonstrate that they highly correlate with their frequency in large-scale internet data. Then, we demonstrate that entity-typing approaches that rely on PLMs struggle with entities at the long tail on the distribution. Our findings suggests that we need to go beyond PLMs to produce solutions that perform well for rare, new or infrequent entities.

Recommended citation: Advait Deshmukh, Ashwin Umadi, Dananjay Srinivas, Maria Leonor Pacheco https://arxiv.org/abs/2410.17355