• Publications
  • Influence
Learning Question Classifiers
  • Xin Li, D. Roth
  • Computer Science
  • COLING
  • 24 August 2002
TLDR
A hierarchical classifier is learned that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. Expand
Design Challenges and Misconceptions in Named Entity Recognition
TLDR
Some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system are analyzed, and several solutions to these challenges are developed. Expand
Local and Global Algorithms for Disambiguation to Wikipedia
TLDR
This work analyzes approaches that utilize information from Wikipedia link structure to arrive at coherent sets of disambiguations for a given document, and compares them to more traditional (local) approaches. Expand
Learning to detect objects in images via a sparse, part-based representation
TLDR
A learning-based approach to the problem of detecting objects in still, gray-scale images that makes use of a sparse, part-based representation is developed and a critical evaluation of the approach under the proposed standards is presented. Expand
Learning a Sparse Representation for Object Detection
TLDR
An approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects, that achieves high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation. Expand
Knowing What to Believe (when you already know something)
TLDR
This work introduces a framework for incorporating prior knowledge into any fact-finding algorithm, expressing both general "common-sense" reasoning and specific facts already known to the user as first-order logic and translating this into a tractable linear program. Expand
Emotions from Text: Machine Learning for Text-based Emotion Prediction
TLDR
This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Expand
On the Hardness of Approximate Reasoning
  • D. Roth
  • Mathematics, Computer Science
  • IJCAI
  • 28 August 1993
TLDR
It is proved that counting satisfying assignments of propositional languages is intractable even for Horn and monotone formulae, and even when the size of clauses and number of occurrences of the variables are extremely limited. Expand
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
TLDR
It is shown that full syntactic parsing information is, by far, most relevant in identifying the argument, especially in the very first stagethe pruning stage, and an effective and simple approach of combining different semantic role labeling systems through joint inference is proposed, which significantly improves its performance. Expand
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
TLDR
The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills, and finds human solvers to achieve an F1-score of 88.1%. Expand
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