Nate Chambers

Professor, Department of Computer Science

Co-Director, Center for High Performance Computing

(Publishes as Nathanael Chambers)

Recent Developments

New paper on Event Reasoning at ACL-25

Winner of the 2020 Faculty Award for Excellence in Research at USNA

Best Paper Award: WNUT Wkshp

Finalist for ACL19 Test-of-Time Award (10 year)

Teaching

Spring 2026:SI420, Artificial Intelligence
SI448, Capstone Seminar
Fall 2025:SI425, Natural Language Processing
Spring 2025:SD212, Data Science 2
Fall 2024:SI425, Natural Language Processing
Spring 2023:SI425, Natural Language Processing
Fall 2022:SD211, Intro to Data Science
Spring 2022:SI268, Programming for Everyone
Fall 2021:SI425, Natural Language Processing
IT350, Web & Internet Programming
Spring 2021:SI286, Programming for Everyone
Fall 2020:SI425, Natural Language Processing
Spring 2020:SI286, Programming for Everyone
Fall 2019:IC210, Intro to Computing
Spring 2019:IC211, Object-Oriented Programming
(previous semesters 2011-2018)

Research Interests

I research natural language processing (NLP) and learning about the world from text with minimal supervision. I apply modern NLP and machine learning techniques to learn how events and people interact. Most recently this involves researching Large Language Models for event understanding, reasoning, and inference. I am also interested in information extraction to help people better understand large amounts of text.

Student Collaboration

I am always open to advising undergraduate research. Come talk to me if you're interested. Topics ranging from social media like Twitter to intelligent systems that read the news are ripe for study.

Education

Ph.D., Computer Science, Stanford University, 2011

M.S., Computer Science, University of Rochester, 2003

B.S., Computer Science, University of Rochester, 2002

Selected Publications (view all 62 with downloads)

Causal Graph based Event Reasoning using Semantic Relation Experts

ACL 2025

CaT-Bench: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans

EMNLP 2024

Using Commonsense Knowledge to Answer Why-Questions

EMNLP 2022

TellMeWhy: A Dataset for Answering Why-Questions in Narratives

ACL 2021

Character-Based Models for Adversarial Phone Number Extraction: Preventing Human Sex Trafficking

WNUT 2019

Event Representations with Tensor-based Compositions

AAAI 2018

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories

NAACL 2016

Identifying Political Sentiment between Nation States with Social Media

EMNLP 2015

Dense Event Ordering with a Multi-Pass Architecture

TACL 2014

Template-Based Information Extraction without the Templates

ACL 2011

A Multi-Pass Sieve for Coreference Resolution

EMNLP 2010

Unsupervised Learning of Narrative Schemas and their Participants

ACL 2009

view all 60 with downloads

Data

Adversarial Phone Extraction

Twitter DDoS Attacks

Twitter Users/Aliases

Story Cloze Test Corpora

Nation-Nation Sentiment Data and Visualization

CAEVO: Dense Event Ordering

Narrative Cloze Evaluation (Gigaword Corpus NYT labeled)

Narrative Schemas Data

Political Tweets

Document Timestamp Prediction

Pseudo-Word Evaluation Data

TimeBank Event Pairs

Event Duration Lexicon