Arc and path consistence revisited
Artificial Intelligence
Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
Disambiguating prepositional phrase attachments by using on-line dictionary definitions
Computational Linguistics - Special issue of the lexicon
Structural disambiguation with constraint propagation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Extraction of semantic information from an ordinary English dictionary and its evaluation
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 2
A best-match algorithm for broad-coverage example-based disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Robust method of pronoun resolution using full-text information
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Portable knowledge sources for machine translation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Shalt2: a symmetric machine translation system with conceptual transfer
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
Shalt2: a symmetric machine translation system with conceptual transfer
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
| Hi-index | 0.00 |
To resolve structural ambiguities in syntactic analysis of natural language, which are caused by prepositional phrase attachment, relative clause attachment, and so on, we developed an experimental system called the Dependency Analyzer. The system uses instances of dependency structures extracted from a terminology dictionary as a knowledge base. Structural (attachment) ambiguity is represented by showing that a word has several words as candidate modifiees. The system resolves such ambiguity as follows First, it searches the knowledge base for modification relationships (dependencies) between the word and each of its possible modifiees, then assigns an order of preference to these relationships, and finally selects the most preferable dependency. The knowledge base can be constructed semi-automatically, since the source of knowledge exists in the form of texts, and these sentences can be analyzed by the parser and transformed into dependency structures by the system. We are realizing knowledge bootstrapping by adding the outputs of the system to its knowledge base.