Search Results for author:
Found 17 papers, 6 papers with code
Identifying Collaborative Conversations using Latent Discourse Behaviors
In this work, we study collaborative online conversations.
Studying the Effects of Collaboration in Interactive Theme Discovery Systems
NLP-assisted solutions have gained considerable traction to support qualitative data analysis.
Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News
Then, for every numerical quantity reported in the article, we learn to identify whether it corresponds to an economic indicator and whether it is being reported in a positive or negative way.
On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software
Due to the ever-increasing complexity of income tax laws in the United States, the number of US taxpayers filing their taxes using tax preparation software (henceforth, tax software) continues to increase.
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections
Experts across diverse disciplines are often interested in making sense of large text collections.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions.
Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents
We show the generalizability of our FSM extraction by using the RFCs for six different protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP.
Identifying Morality Frames in Political Tweets using Relational Learning
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions.
Modeling Human Mental States with an Entity-based Narrative Graph
Understanding narrative text requires capturing characters' motivations, goals, and mental states.
Randomized Deep Structured Prediction for Discourse-Level Processing
In this paper, we explore the use of randomized inference to alleviate this concern and show that we can efficiently leverage deep structured prediction and expressive neural encoders for a set of tasks involving complicated argumentative structures.
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations
Representing, and reasoning over, long narratives requires models that can deal with complex event structures connected through multiple relationship types.
Modeling Content and Context with Deep Relational Learning
Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies.
Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols
Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics.
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
Our proposed system consists of two subsystems and one regression model for predicting STS scores.
