Owen Rambow

About

Owen Rambow is the IACS Endowed Chair in the Department of Linguistics at Stony Brook University. His research focuses on natural language processing and computational linguistics, with specific interests including the detailed aspects of language such as morphology and syntax, as well as how language is used in context. Owen received a Ph.D. in Computer and Information Sciences from the University of Pennsylvania. He has professional experience working at AT&T Labs — Research, spent 15 years at Columbia University as a research scientist, and worked for three years at Elemental Cognition LLC, a startup dedicated to developing software for deep language understanding. At Columbia, he was part of the Center for Computational Learning Systems and co-founded CADIM, a research group specializing in Arabic natural language processing, which licenses advanced NLP tools. His group has also released several resources, including a richly annotated version of the Enron email corpus. Owen Rambow has published extensively in top conferences and journals. He has served as the Chair of the American chapter of the Association for Computational Linguistics, as program co-chair of the NAACL HLT 2016 conference, and has held roles as program committee chair or senior program committee member for numerous conferences and workshops.

Research topics

• Sociology
• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Algorithm
• Human–computer interaction
• Psychology
• Epistemology
• Communication

Selected publications

• Active Few-Shot Learning for Text Classification

  2025-01-01 · 4 citations

  articleOpen access

  Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, Seyed Abolghasem Mirroshandel, Owen Rambow, Cornelia Caragea. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

  PublisherOA PDFDOI

• Exploring Limitations of LLM Capabilities with Multi-Problem Evaluation

  2025-01-01 · 3 citations

  articleOpen accessSenior author

  We propose using prompts made up of multiple problems to evaluate LLM capabilities, an approach we call multi-problem evaluation.We examine 7 LLMs on 4 related task types constructed from 6 existing classification benchmarks.We find that while LLMs can generally perform multiple homogeneous classifications at once (Batch Classification) as well as when they do so separately, they perform significantly worse on two selection tasks that are conceptually equivalent to Batch Classification and invol…

  PublisherOA PDFDOI

• Active Few-Shot Learning for Text Classification

  ArXiv.org · 2025-02-26 · 1 citations

  preprintOpen access

  The rise of Large Language Models (LLMs) has boosted the use of Few-Shot Learning (FSL) methods in natural language processing, achieving acceptable performance even when working with limited training data. The goal of FSL is to effectively utilize a small number of annotated samples in the learning process. However, the performance of FSL suffers when unsuitable support samples are chosen. This problem arises due to the heavy reliance on a limited number of support samples, which hampers consis…

  PublisherOA PDFDOI

• LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing

  2025-01-01 · 1 citations

  articleOpen accessSenior author

  The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria.Using a corpus of literature reviews written by L2 graduate students and assessed by human experts against 9 analytic criteria, we prompt several popular LLMs to perform the same task under various conditions.To evaluate the quality of feedback comments, we apply a novel feedback comment quality e…

  PublisherOA PDFDOI

• Improving Opinion Mining Through Automatic Prompt Construction

  Journal of Information Systems and Telecommunication (JIST) · 2024-11-11 · 1 citations

  articleSenior author
  PublisherDOI

Frequent coauthors

• Nizar Habash

  52 shared

• Mona Diab

  Carnegie Mellon University

  26 shared

• Vinodkumar Prabhakaran

  23 shared

• Alexis Nasr

  17 shared

• Jungo Kasai

  Toyota Technological Institute at Chicago

  15 shared

• Robert Frank

  15 shared

• Ramy Eskander

  15 shared

• Srinivas Bangalore

  14 shared

Education

• Ph.D., Computer Science

  University of California, San Diego

  2000

• M.S., Computer Science

  University of California, San Diego

  1997

• B.S., Computer Science

  University of California, San Diego

  1995

Similar researchers at Stony Brook University

• Robert J. Harrisonh-69
• Marat Khairoutdinovh-51
• Benjamin Levineh-40