A Deep Architecture for Semantic Parsing
Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries. This paper presents a novel deep learning architecture which provides a semantic parsing s...
| Main Authors: | , , , |
|---|---|
| 格式: | Journal article |
| 出版: |
2014
|
| _version_ | 1826291692413648896 |
|---|---|
| author | Grefenstette, E Blunsom, P de Freitas, N Hermann, K |
| author_facet | Grefenstette, E Blunsom, P de Freitas, N Hermann, K |
| author_sort | Grefenstette, E |
| collection | OXFORD |
| description | Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries. This paper presents a novel deep learning architecture which provides a semantic parsing system through the union of two neural models of language semantic. It allows for the generation of ontology-specific queries from natural language statements and questions without the need for parsing, which makes it especially suitable to grammatically malformed or syntactically atypical text, such as tweets, as well as permitting the development of semantic parsers for resource-poor languages. |
| first_indexed | 2024-03-07T03:03:14Z |
| format | Journal article |
| id | oxford-uuid:b1a6fc46-c6d8-4f6e-a942-f84d31b10ff1 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T03:03:14Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | oxford-uuid:b1a6fc46-c6d8-4f6e-a942-f84d31b10ff12022-03-27T04:05:41ZA Deep Architecture for Semantic ParsingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b1a6fc46-c6d8-4f6e-a942-f84d31b10ff1Department of Computer Science2014Grefenstette, EBlunsom, Pde Freitas, NHermann, KMany successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries. This paper presents a novel deep learning architecture which provides a semantic parsing system through the union of two neural models of language semantic. It allows for the generation of ontology-specific queries from natural language statements and questions without the need for parsing, which makes it especially suitable to grammatically malformed or syntactically atypical text, such as tweets, as well as permitting the development of semantic parsers for resource-poor languages. |
| spellingShingle | Grefenstette, E Blunsom, P de Freitas, N Hermann, K A Deep Architecture for Semantic Parsing |
| title | A Deep Architecture for Semantic Parsing |
| title_full | A Deep Architecture for Semantic Parsing |
| title_fullStr | A Deep Architecture for Semantic Parsing |
| title_full_unstemmed | A Deep Architecture for Semantic Parsing |
| title_short | A Deep Architecture for Semantic Parsing |
| title_sort | deep architecture for semantic parsing |
| work_keys_str_mv | AT grefenstettee adeeparchitectureforsemanticparsing AT blunsomp adeeparchitectureforsemanticparsing AT defreitasn adeeparchitectureforsemanticparsing AT hermannk adeeparchitectureforsemanticparsing AT grefenstettee deeparchitectureforsemanticparsing AT blunsomp deeparchitectureforsemanticparsing AT defreitasn deeparchitectureforsemanticparsing AT hermannk deeparchitectureforsemanticparsing |