• Publications
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Learning Question Classifiers
  • Xin Li, D. Roth
  • Computer Science
  • 24 August 2002
In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints theExpand
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Design Challenges and Misconceptions in Named Entity Recognition
We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as theExpand
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Local and Global Algorithms for Disambiguation to Wikipedia
Disambiguating concepts and entities in a context sensitive way is a fundamental problem in natural language processing. The comprehensiveness of Wikipedia has made the online encyclopedia anExpand
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Learning to detect objects in images via a sparse, part-based representation
We study the problem of detecting objects in still, gray-scale images. Our primary focus is the development of a learning-based approach to the problem that makes use of a sparse, part-basedExpand
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Learning a Sparse Representation for Object Detection
We present an approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects. A vocabulary of information-rich object parts isExpand
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Knowing What to Believe (when you already know something)
Although much work in NLP has focused on simply determining what a document means, we also must know whether or not to believe it. Fact-finding algorithms attempt to identify the "truth" amongExpand
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On the Hardness of Approximate Reasoning
  • D. Roth
  • Computer Science, Mathematics
  • 28 August 1993
Abstract Many AI problems, when formalized, reduce to evaluating the probability that a propositional expression is true. In this paper we show that this problem is computationally intractable evenExpand
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Emotions from Text: Machine Learning for Text-based Emotion Prediction
In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machineExpand
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The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporatesExpand
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Understanding the Value of Features for Coreference Resolution
In recent years there has been substantial work on the important problem of coreference resolution, most of which has concentrated on the development of new models and algorithmic techniques. TheseExpand
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