An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Knowledge entry as the graphical assembly of components
Proceedings of the 1st international conference on Knowledge capture
Automatic labeling of semantic roles
Computational Linguistics
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Developing and empirically evaluating robust explanation generators: the KNIGHT experiments
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Extracting knowledge from evaluative text
Proceedings of the 3rd international conference on Knowledge capture
Automatic cinematography and multilingual NLG for generating video documentaries
Artificial Intelligence
Domain kernels for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
A unified knowledge based approach for sense disambiguationm and semantic role labeling
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning by reading: a prototype system, performance baseline and lessons learned
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Acquiring correct knowledge for natural language generation
Journal of Artificial Intelligence Research
Introduction to the shared task on comparing semantic representations
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Introduction to the shared task on comparing semantic representations
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
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The lack of large amounts of readily available, explicitly represented knowledge has long been recognized as a barrier to applications requiring semantic knowledge such as machine translation and question answering. This problem is analogous to that facing machine translation decades ago, where one proposed solution was to use human translators to post-edit automatically produced, low quality translations rather than expect a computer to independently create high-quality translations. This paper describes an attempt at implementing a semantic parser that takes unrestricted English text, uses publically available computational linguistics tools and lexical resources and as output produces semantic triples which can be used in a variety of tasks such as generating knowledge bases, providing raw material for question answering systems, or creating RDF structures. We describe the TextCap system, detail the semantic triple representation it produces, illustrate step by step how TextCap processes a short text, and use its results on unseen texts to discuss the amount of post-editing that might be realistically required.