N-gram Parsing for Jointly Training a Discriminative Constituency Parser
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Instituto Politécnico Nacional
2013
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| _version_ | 1876488992892911616 |
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| author | Arda Çelebi |
| author_facet | Arda Çelebi |
| contents | N-gram Parsing for Jointly Training a Discriminative Constituency Parser Arda Çelebi Arzucan Özgür Computación gram parsing Constituency parsing discriminative learning hierarchical joint learning Syntactic parsers are designed to detect the complete syntactic structure of grammatically correct sentences. In this paper, we introduce the concept of n-gram parsing, which corresponds to generating the constituency parse tree of n consecutive words in a sentence. We create a stand-alone n-gram parser derived from a baseline full discriminative constituency parser and analyze the characteristics of the generated n-gram trees for various values of n. Since the produced n-gram trees are in general smaller and less complex compared to full parse trees, it is likely that n-gram parsers are more robust compared to full parsers. Therefore, we use n-gram parsing to boost the accuracy of a full discriminative constituency parser in a hierarchical joint learning setup. Our results show that the full parser jointly trained with an n-gram parser performs statistically significantly better than our baseline full parser on the English Penn Treebank test corpus. 2013 artículo científico 1870-9044 https://www.redalyc.org/articulo.oa?id=402640461002 en http://www.redalyc.org/revista.oa?id=4026 Polibits application/pdf Instituto Politécnico Nacional Polibits (México) Vol.47 |
| format | Artículo científico |
| id | redalyc_402640461002 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | N-gram Parsing for Jointly Training a Discriminative Constituency Parser Arda Çelebi Computación gram parsing Constituency parsing discriminative learning hierarchical joint learning N-gram Parsing for Jointly Training a Discriminative Constituency Parser Arda Çelebi Arzucan Özgür Computación gram parsing Constituency parsing discriminative learning hierarchical joint learning Syntactic parsers are designed to detect the complete syntactic structure of grammatically correct sentences. In this paper, we introduce the concept of n-gram parsing, which corresponds to generating the constituency parse tree of n consecutive words in a sentence. We create a stand-alone n-gram parser derived from a baseline full discriminative constituency parser and analyze the characteristics of the generated n-gram trees for various values of n. Since the produced n-gram trees are in general smaller and less complex compared to full parse trees, it is likely that n-gram parsers are more robust compared to full parsers. Therefore, we use n-gram parsing to boost the accuracy of a full discriminative constituency parser in a hierarchical joint learning setup. Our results show that the full parser jointly trained with an n-gram parser performs statistically significantly better than our baseline full parser on the English Penn Treebank test corpus. 2013 artículo científico 1870-9044 https://www.redalyc.org/articulo.oa?id=402640461002 en http://www.redalyc.org/revista.oa?id=4026 Polibits application/pdf Instituto Politécnico Nacional Polibits (México) Vol.47 |
| title | N-gram Parsing for Jointly Training a Discriminative Constituency Parser |
| topic | Computación gram parsing Constituency parsing discriminative learning hierarchical joint learning |
| url | https://www.redalyc.org/articulo.oa?id=402640461002 |