An unsupervised approach for linking automatically extracted and manually crafted LTAGs

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

An unsupervised approach for linking automatically extracted and manually crafted LTAGs. / Faili, Heshaam; Basirat, Ali.

Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings. PART 1. ed. 2011. p. 68-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); No. PART 1, Vol. 6608 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Faili, H & Basirat, A 2011, An unsupervised approach for linking automatically extracted and manually crafted LTAGs. in Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6608 LNCS, pp. 68-81, 12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011, Tokyo, Japan, 20/02/2011. https://doi.org/10.1007/978-3-642-19400-9_6

APA

Faili, H., & Basirat, A. (2011). An unsupervised approach for linking automatically extracted and manually crafted LTAGs. In Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings (PART 1 ed., pp. 68-81). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 6608 LNCS No. PART 1 https://doi.org/10.1007/978-3-642-19400-9_6

Vancouver

Faili H, Basirat A. An unsupervised approach for linking automatically extracted and manually crafted LTAGs. In Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings. PART 1 ed. 2011. p. 68-81. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); No. PART 1, Vol. 6608 LNCS). https://doi.org/10.1007/978-3-642-19400-9_6

Author

Faili, Heshaam ; Basirat, Ali. / An unsupervised approach for linking automatically extracted and manually crafted LTAGs. Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings. PART 1. ed. 2011. pp. 68-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); No. PART 1, Vol. 6608 LNCS).

Bibtex

@inproceedings{dab0c973972d4c3c955f558bc90b945a,
title = "An unsupervised approach for linking automatically extracted and manually crafted LTAGs",
abstract = "Though the lack of semantic representation of automatically extracted LTAGs is an obstacle in using these formalism, due to the advent of some powerful statistical parsers that were trained on them, these grammars have been taken into consideration more than before. Against of this grammatical class, there are some widely usage manually crafted LTAGs that are enriched with semantic representation but suffer from the lack of efficient parsers. The available representation of latter grammars beside the statistical capabilities of former encouraged us in constructing a link between them. Here, by focusing on the automatically extracted LTAG used by MICA [4] and the manually crafted English LTAG namely XTAG grammar [32], a statistical approach based on HMM is proposed that maps each sequence of former elementary trees onto a sequence of later elementary trees. To avoid of converging the HMM training algorithm in a local optimum state, an EM-based learning process for initializing the HMM parameters were proposed too. Experimental results show that the mapping method can provide a satisfactory way to cover the deficiencies arises in one grammar by the available capabilities of the other.",
keywords = "Automatically Extracted Tree Adjoining Grammar, Grammar Mapping, HMM Initialization, MICA, Semantic Representation, Supertagging, XTAG Derivation Tree",
author = "Heshaam Faili and Ali Basirat",
year = "2011",
doi = "10.1007/978-3-642-19400-9_6",
language = "English",
isbn = "9783642193996",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "68--81",
booktitle = "Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings",
edition = "PART 1",
note = "12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011 ; Conference date: 20-02-2011 Through 26-02-2011",

}

RIS

TY - GEN

T1 - An unsupervised approach for linking automatically extracted and manually crafted LTAGs

AU - Faili, Heshaam

AU - Basirat, Ali

PY - 2011

Y1 - 2011

N2 - Though the lack of semantic representation of automatically extracted LTAGs is an obstacle in using these formalism, due to the advent of some powerful statistical parsers that were trained on them, these grammars have been taken into consideration more than before. Against of this grammatical class, there are some widely usage manually crafted LTAGs that are enriched with semantic representation but suffer from the lack of efficient parsers. The available representation of latter grammars beside the statistical capabilities of former encouraged us in constructing a link between them. Here, by focusing on the automatically extracted LTAG used by MICA [4] and the manually crafted English LTAG namely XTAG grammar [32], a statistical approach based on HMM is proposed that maps each sequence of former elementary trees onto a sequence of later elementary trees. To avoid of converging the HMM training algorithm in a local optimum state, an EM-based learning process for initializing the HMM parameters were proposed too. Experimental results show that the mapping method can provide a satisfactory way to cover the deficiencies arises in one grammar by the available capabilities of the other.

AB - Though the lack of semantic representation of automatically extracted LTAGs is an obstacle in using these formalism, due to the advent of some powerful statistical parsers that were trained on them, these grammars have been taken into consideration more than before. Against of this grammatical class, there are some widely usage manually crafted LTAGs that are enriched with semantic representation but suffer from the lack of efficient parsers. The available representation of latter grammars beside the statistical capabilities of former encouraged us in constructing a link between them. Here, by focusing on the automatically extracted LTAG used by MICA [4] and the manually crafted English LTAG namely XTAG grammar [32], a statistical approach based on HMM is proposed that maps each sequence of former elementary trees onto a sequence of later elementary trees. To avoid of converging the HMM training algorithm in a local optimum state, an EM-based learning process for initializing the HMM parameters were proposed too. Experimental results show that the mapping method can provide a satisfactory way to cover the deficiencies arises in one grammar by the available capabilities of the other.

KW - Automatically Extracted Tree Adjoining Grammar

KW - Grammar Mapping

KW - HMM Initialization

KW - MICA

KW - Semantic Representation

KW - Supertagging

KW - XTAG Derivation Tree

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U2 - 10.1007/978-3-642-19400-9_6

DO - 10.1007/978-3-642-19400-9_6

M3 - Article in proceedings

AN - SCOPUS:79952269393

SN - 9783642193996

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 68

EP - 81

BT - Computational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings

T2 - 12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011

Y2 - 20 February 2011 through 26 February 2011

ER -

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