Automatic labeling of semantic roles
Computational Linguistics
Discovery of inference rules for question-answering
Natural Language Engineering
A knowledge-rich approach to understanding text about aircraft systems
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Can we derive general world knowledge from texts?
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Abstraction summarization for managing the biomedical research literature
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Turing's dream and the knowledge challenge
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Open knowledge extraction through compositional language processing
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Constructing text sense representations
TextMean '04 Proceedings of the 2nd Workshop on Text Meaning and Interpretation
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Our goal is to be able to answer questions about text that go beyond facts explicitly stated in the text, a task which inherently requires extracting a "deep" level of meaning from that text. Our approach treats meaning processing fundamentally as a modeling activity, in which a knowledge base of common-sense expectations guides interpretation of text, and text suggests which parts of the knowledge base might be relevant. In this paper, we describe our ongoing investigations to develop this approach into a usable method for meaning processing.