An unsupervised approach for linking automatically extracted and manually crafted LTAGs
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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
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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
UR - http://www.scopus.com/inward/record.url?scp=79952269393&partnerID=8YFLogxK
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 -
ID: 366047966