Meaning from structure in natural language processing:
Abstract: "The goal of natural language processing is to construct a computer-digestible representation of the meaning of the typed sentence, i.e. a semantic representation. The development of larger scale natural language systems has been hampered by the need to manually create mappings from s...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Pittsburgh, Pa.
School of Computer Science, Carnegie Mellon Univ.
1991
|
| Schriftenreihe: | School of Computer Science <Pittsburgh, Pa.>: CMU-CS
1991,158 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "The goal of natural language processing is to construct a computer-digestible representation of the meaning of the typed sentence, i.e. a semantic representation. The development of larger scale natural language systems has been hampered by the need to manually create mappings from syntactic structures into meaning representations. A new approach to semantic interpretation is described, which uses partial syntactic structures as the main unit of analysis for interpretation rules. The approach can work for a variety of syntactic representations corresponding to directed acyclic graphs and is designed to map into meaning represenations based on frame hierarchies with inheritance Semantic interpretation rules are defined in a compact format which is suitable for automatic rule extension or generalization, when existing hand-coded rules do not cover the current input. Furthermore, automatic discovery of semantic interpretation rules from input/output examples is made possible by this new rule format. The principles of the approach are validated in a comparison to other methods on an independently developed domain In experiments performed on an English language corpus of sentences, the approach allowed semantic interpretation rules to be created manually in about 50 percent less time, with 78 percent coverage of the test corpus, as opposed to the 66.1 percent coverage which had been achieved before with the original rules written for this application by independent sources |
| Beschreibung: | Zugl.: Pittsburgh, Pa., Univ., Diss., 1991 |
| Beschreibung: | III, 169 S. graph. Darst. |
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| 490 | 1 | |a School of Computer Science <Pittsburgh, Pa.>: CMU-CS |v 1991,158 | |
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| 520 | 3 | |a Abstract: "The goal of natural language processing is to construct a computer-digestible representation of the meaning of the typed sentence, i.e. a semantic representation. The development of larger scale natural language systems has been hampered by the need to manually create mappings from syntactic structures into meaning representations. A new approach to semantic interpretation is described, which uses partial syntactic structures as the main unit of analysis for interpretation rules. The approach can work for a variety of syntactic representations corresponding to directed acyclic graphs and is designed to map into meaning represenations based on frame hierarchies with inheritance | |
| 520 | 3 | |a Semantic interpretation rules are defined in a compact format which is suitable for automatic rule extension or generalization, when existing hand-coded rules do not cover the current input. Furthermore, automatic discovery of semantic interpretation rules from input/output examples is made possible by this new rule format. The principles of the approach are validated in a comparison to other methods on an independently developed domain | |
| 520 | 3 | |a In experiments performed on an English language corpus of sentences, the approach allowed semantic interpretation rules to be created manually in about 50 percent less time, with 78 percent coverage of the test corpus, as opposed to the 66.1 percent coverage which had been achieved before with the original rules written for this application by independent sources | |
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Datensatz im Suchindex
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| author | Hauptmann, Alexander G. |
| author_facet | Hauptmann, Alexander G. |
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| author_sort | Hauptmann, Alexander G. |
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| ctrlnum | (OCoLC)25683168 (DE-599)BVBBV009673240 |
| discipline | Informatik |
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| id | DE-604.BV009673240 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:38:59Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006397377 |
| oclc_num | 25683168 |
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| physical | III, 169 S. graph. Darst. |
| publishDate | 1991 |
| publishDateSearch | 1991 |
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| publisher | School of Computer Science, Carnegie Mellon Univ. |
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| series | School of Computer Science <Pittsburgh, Pa.>: CMU-CS |
| series2 | School of Computer Science <Pittsburgh, Pa.>: CMU-CS |
| spelling | Hauptmann, Alexander G. Verfasser aut Meaning from structure in natural language processing Alexander G. Hauptmann CMU CS 91 158 Pittsburgh, Pa. School of Computer Science, Carnegie Mellon Univ. 1991 III, 169 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier School of Computer Science <Pittsburgh, Pa.>: CMU-CS 1991,158 Zugl.: Pittsburgh, Pa., Univ., Diss., 1991 Abstract: "The goal of natural language processing is to construct a computer-digestible representation of the meaning of the typed sentence, i.e. a semantic representation. The development of larger scale natural language systems has been hampered by the need to manually create mappings from syntactic structures into meaning representations. A new approach to semantic interpretation is described, which uses partial syntactic structures as the main unit of analysis for interpretation rules. The approach can work for a variety of syntactic representations corresponding to directed acyclic graphs and is designed to map into meaning represenations based on frame hierarchies with inheritance Semantic interpretation rules are defined in a compact format which is suitable for automatic rule extension or generalization, when existing hand-coded rules do not cover the current input. Furthermore, automatic discovery of semantic interpretation rules from input/output examples is made possible by this new rule format. The principles of the approach are validated in a comparison to other methods on an independently developed domain In experiments performed on an English language corpus of sentences, the approach allowed semantic interpretation rules to be created manually in about 50 percent less time, with 78 percent coverage of the test corpus, as opposed to the 66.1 percent coverage which had been achieved before with the original rules written for this application by independent sources Natural language processing (Computer science) Wissensrepräsentation (DE-588)4049534-6 gnd rswk-swf Maschinelle Übersetzung (DE-588)4003966-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Natürliche Sprache (DE-588)4041354-8 s Wissensrepräsentation (DE-588)4049534-6 s Maschinelle Übersetzung (DE-588)4003966-3 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 School of Computer Science <Pittsburgh, Pa.>: CMU-CS 1991,158 (DE-604)BV006187264 1991,158 |
| spellingShingle | Hauptmann, Alexander G. Meaning from structure in natural language processing School of Computer Science <Pittsburgh, Pa.>: CMU-CS Natural language processing (Computer science) Wissensrepräsentation (DE-588)4049534-6 gnd Maschinelle Übersetzung (DE-588)4003966-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Natürliche Sprache (DE-588)4041354-8 gnd |
| subject_GND | (DE-588)4049534-6 (DE-588)4003966-3 (DE-588)4193754-5 (DE-588)4041354-8 (DE-588)4113937-9 |
| title | Meaning from structure in natural language processing |
| title_alt | CMU CS 91 158 |
| title_auth | Meaning from structure in natural language processing |
| title_exact_search | Meaning from structure in natural language processing |
| title_full | Meaning from structure in natural language processing Alexander G. Hauptmann |
| title_fullStr | Meaning from structure in natural language processing Alexander G. Hauptmann |
| title_full_unstemmed | Meaning from structure in natural language processing Alexander G. Hauptmann |
| title_short | Meaning from structure in natural language processing |
| title_sort | meaning from structure in natural language processing |
| topic | Natural language processing (Computer science) Wissensrepräsentation (DE-588)4049534-6 gnd Maschinelle Übersetzung (DE-588)4003966-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Natürliche Sprache (DE-588)4041354-8 gnd |
| topic_facet | Natural language processing (Computer science) Wissensrepräsentation Maschinelle Übersetzung Maschinelles Lernen Natürliche Sprache Hochschulschrift |
| volume_link | (DE-604)BV006187264 |
| work_keys_str_mv | AT hauptmannalexanderg meaningfromstructureinnaturallanguageprocessing AT hauptmannalexanderg cmucs91158 |