Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Implementing a semantic interpreter using conceptual graphs
IBM Journal of Research and Development
Conceptual graphs for the analysis and generation of sentences
IBM Journal of Research and Development
Automatic labeling of semantic roles
Computational Linguistics
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A fully statistical approach to natural language interfaces
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Semantic classification of verbs in CROVALLEX
CEA'10 Proceedings of the 4th WSEAS international conference on Computer engineering and applications
CROVALLEX lexicon improvements: subcategorization and semantic constraints
WSEAS Transactions on Computers
Information retrieval with a simplified conceptual graph-like representation
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
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With the huge number of documents becoming available in electronic form, finding relevant information in a large corpus is becoming an increasingly important, but difficult, task. We believe that semantic processing is required in order to achieve more accurate information retrieval. This paper describes a framework for the creation of semantic markup and its insertion into XML documents. We describe the semi-automatic construction of conceptual graph representations of texts using a combination of existing linguistic resources, such as VerbNet and WordNet. The system we have developed uses a two-step approach, firstly identifying the semantic rôles in a sentence, and then using these rôles, together with semi-automatically compiled domain-specific knowledge, to construct the conceptual graph representation.