2006 MachineReading
- (Etzioni et al., 2006) ⇒ Oren Etzioni, Michele Banko, and Michael J. Cafarella. (2006). “Machine Reading.” In: Proceedings of the 21st national conference on Artificial intelligence (AAAI 2006).
Subject Headings: Information Extraction, Machine Reading
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Abstract
1. Introduction
The time is ripe for the AI community to set its sights on Machine Reading - the automatic, unsupervised understanding of text. In this paper, we place the notion of “Machine Reading” in context, describe progress towards this goal by the KnowItAll research group at the University of Washington, and highlight several central research questions.
By “understanding text” I mean the formation of a coherent set of beliefs based on a textual corpus and a background theory. Because the text and the background theory may be inconsistent, it is natural to express the resultant beliefs, and the reasoning process in probabilistic terms. A key problem is that many of the beliefs of interest are only implied by the text in combination with a background theory. To recall Roger Schank’s old example, if the text states that a person left a restaurant after a satisfactory meal, it is reasonable to infer that he is likely to have paid the bill and left a tip. Thus, inference is an integral part of text understanding.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2006 MachineReading | Michael J. Cafarella Oren Etzioni Michele Banko | Machine Reading | http://turing.cs.washington.edu/papers/aaai06.pdf |