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Learning Question Classifiers
A hierarchical classifier is learned that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes.
Design Challenges and Misconceptions in Named Entity Recognition
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.
Local and Global Algorithms for Disambiguation to Wikipedia
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.
Learning to detect objects in images via a sparse, part-based representation
- S. Agarwal, A. Awan, D. Roth
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 November 2004
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.
Learning a Sparse Representation for Object Detection
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.
Knowing What to Believe (when you already know something)
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.
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
- Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, D. Roth
- Computer ScienceNAACL
- 1 June 2018
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%.
Emotions from Text: Machine Learning for Text-based Emotion Prediction
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.
On the Hardness of Approximate Reasoning
- D. Roth
- Computer ScienceIJCAI
- 28 August 1993
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
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.