Constructing linguistically motivated structures from statistical grammars

Publikation: Bidrag til tidsskriftKonferenceartikelForskningfagfællebedømt

Standard

Constructing linguistically motivated structures from statistical grammars. / Basirat, Ali; Faili, Heshaam.

I: International Conference Recent Advances in Natural Language Processing, RANLP, 2011, s. 63-69.

Publikation: Bidrag til tidsskriftKonferenceartikelForskningfagfællebedømt

Harvard

Basirat, A & Faili, H 2011, 'Constructing linguistically motivated structures from statistical grammars', International Conference Recent Advances in Natural Language Processing, RANLP, s. 63-69.

APA

Basirat, A., & Faili, H. (2011). Constructing linguistically motivated structures from statistical grammars. International Conference Recent Advances in Natural Language Processing, RANLP, 63-69.

Vancouver

Basirat A, Faili H. Constructing linguistically motivated structures from statistical grammars. International Conference Recent Advances in Natural Language Processing, RANLP. 2011;63-69.

Author

Basirat, Ali ; Faili, Heshaam. / Constructing linguistically motivated structures from statistical grammars. I: International Conference Recent Advances in Natural Language Processing, RANLP. 2011 ; s. 63-69.

Bibtex

@inproceedings{91fc491faf704f338f6c4dd87a4e290e,
title = "Constructing linguistically motivated structures from statistical grammars",
abstract = "This paper discusses two Hidden Markov Models (HMM) for linking linguistically motivated XTAG grammar and the automatically extracted LTAG used by MICA parser. The former grammar is a detailed LTAG enriched with feature structures. And the latter one is a huge size LTAG that due to its statistical nature is well suited to be used in statistical approaches. Lack of an efficient parser and sparseness in the supertags set are the main obstacles in using XTAG and MICA grammars respectively. The models were trained by the standard HMM training algorithm, Baum-Welch. To converge the training algorithm to a better local optimum, the initial state of the models also were estimated using two semi-supervised EM-based algorithms. The resulting accuracy of the model (about 91%) shows that the models can provide a satisfactory way for linking these grammars to share their capabilities together.",
author = "Ali Basirat and Heshaam Faili",
year = "2011",
language = "English",
pages = "63--69",
journal = "International Conference Recent Advances in Natural Language Processing, RANLP",
issn = "1313-8502",
publisher = "Association for Computational Linguistics (ACL)",
note = "8th International Conference on Recent Advances in Natural Language Processing, RANLP 2011 ; Conference date: 12-09-2011 Through 14-09-2011",

}

RIS

TY - GEN

T1 - Constructing linguistically motivated structures from statistical grammars

AU - Basirat, Ali

AU - Faili, Heshaam

PY - 2011

Y1 - 2011

N2 - This paper discusses two Hidden Markov Models (HMM) for linking linguistically motivated XTAG grammar and the automatically extracted LTAG used by MICA parser. The former grammar is a detailed LTAG enriched with feature structures. And the latter one is a huge size LTAG that due to its statistical nature is well suited to be used in statistical approaches. Lack of an efficient parser and sparseness in the supertags set are the main obstacles in using XTAG and MICA grammars respectively. The models were trained by the standard HMM training algorithm, Baum-Welch. To converge the training algorithm to a better local optimum, the initial state of the models also were estimated using two semi-supervised EM-based algorithms. The resulting accuracy of the model (about 91%) shows that the models can provide a satisfactory way for linking these grammars to share their capabilities together.

AB - This paper discusses two Hidden Markov Models (HMM) for linking linguistically motivated XTAG grammar and the automatically extracted LTAG used by MICA parser. The former grammar is a detailed LTAG enriched with feature structures. And the latter one is a huge size LTAG that due to its statistical nature is well suited to be used in statistical approaches. Lack of an efficient parser and sparseness in the supertags set are the main obstacles in using XTAG and MICA grammars respectively. The models were trained by the standard HMM training algorithm, Baum-Welch. To converge the training algorithm to a better local optimum, the initial state of the models also were estimated using two semi-supervised EM-based algorithms. The resulting accuracy of the model (about 91%) shows that the models can provide a satisfactory way for linking these grammars to share their capabilities together.

UR - http://www.scopus.com/inward/record.url?scp=84866851139&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84866851139

SP - 63

EP - 69

JO - International Conference Recent Advances in Natural Language Processing, RANLP

JF - International Conference Recent Advances in Natural Language Processing, RANLP

SN - 1313-8502

T2 - 8th International Conference on Recent Advances in Natural Language Processing, RANLP 2011

Y2 - 12 September 2011 through 14 September 2011

ER -

ID: 366047680