59 citations · 63 across the 7 of their papers we have counts for
10 papers
APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI
Pratyay Banerjee, Masud Moshtaghi, Shivashankar Subramanian +2
Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying naive retrieval often introduces noise and destabil…
The Semantic Scholar Open Data Platform
Rodney Kinney, Chloe Anastasiades, Russell Authur +45
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data pl…
Fairness-aware Class Imbalanced Learning
Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin +2
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the…
Evaluating Debiasing Techniques for Intersectional Biases
Shivashankar Subramanian, Xudong Han, Timothy Baldwin +2
Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in…
S2AND: A Benchmark and Evaluation System for Author Name Disambiguation
Shivashankar Subramanian, Daniel King, Doug Downey +1
Author Name Disambiguation (AND) is the task of resolving which author mentions in a bibliographic database refer to the same real-world person, and is a critical ingredient of dig…
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran +7
More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe p…
Deep Ordinal Regression for Pledge Specificity Prediction
Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, ther…
Target Based Speech Act Classification in Political Campaign Text
Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance. We propose a new annotation schema incorporating domain-specific s…
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
Shivashankar Subramanian, Timothy Baldwin, Trevor Cohn
Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition --- commonly measured by it…
Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis
Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party's fine-grained position on a particular issue, as we…