Grammatical Relations as the Basis for Natural Language Parsing and Text Understanding

Bayer, Samuel; Joseph, Leonard; Kalish, Candace E.

Grammatical Relations as the Basis for Natural Language Parsing and Text Understanding

Samuel Bayer, Leonard Joseph, Candace E. Kalish

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Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Natural Language. Pages 788-789.

Abstract

The KING KONG parser described by this paper attempts to apply the principles of relational grammar to the parsing of English in order to overcome the problems encount~red by syntactic and semantic parsers. Specif~cally, this Earser uses relational categories such as subJect~ direct 9bject, and instrume~t to map _ sytact~c const~tuents onto semant~c roles w~th~n CD-like structures. Thus, the parser makes use of both syntactic and semant~c information to guide its parse.

Samuel Bayer, Leonard Joseph, and Candace E. Kalish. 1985. Grammatical Relations as the Basis for Natural Language Parsing and Text Understanding. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85. IJCAI, 788–789. https://ijcai.org/proceedings/1985/155

@inproceedings{ijcai1985p155,
  title     = {Grammatical Relations as the Basis for Natural Language Parsing and Text Understanding},
  author    = {Bayer, Samuel and Joseph, Leonard and Kalish, Candace E.},
  booktitle = {Proceedings of the Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-85}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Aravind Joshi},
  pages     = {788--789},
  year      = {1985},
  note      = {Natural Language},
  url       = {https://ijcai.org/proceedings/1985/155},
}

TY  - CPAPER
TI  - Grammatical Relations as the Basis for Natural Language Parsing and Text Understanding
AU  - Bayer, Samuel
AU  - Joseph, Leonard
AU  - Kalish, Candace E.
T2  - Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1985
SP  - 788
EP  - 789
UR  - https://ijcai.org/proceedings/1985/155
ER  -