Augmenting the automated extracted tree adjoining grammars by semantic representation
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Augmenting the automated extracted tree adjoining grammars by semantic representation. / Faili, Heshaam; Basirat, Ali.
Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE, 2010. 2010. 5587766 (Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2010).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Augmenting the automated extracted tree adjoining grammars by semantic representation
AU - Faili, Heshaam
AU - Basirat, Ali
PY - 2010
Y1 - 2010
N2 - MICA [1] is a fast and accurate dependency parser for English that uses an automatically LTAG derived from Penn Treebank (PTB) using the Chen's approach [7]. However, there is no semantic representation related to its grammar. On the other hand, XTAG [20] grammar is a hand crafted LTAG that its elementary trees were enriched with the semantic representation by experts. The linguistic knowledge embedded in the XTAG grammar caused it to being used in wide variety of natural language applications. However, the current XTAG parser is not as fast and accurate as well as the MICA parser. Generating an XTAG derivation tree from a MICA dependency structure could make a bridge between these two notions and gets the benefits of both models. Also, by having this conversion, the applications that use the XTAG parser, may get the helps from MICA parser too. In addition, it can enrich the MICA's grammar by semantic representation of XTAG grammar. In this paper, an unsupervised sequence tagger that maps any sequence of MICA elementary trees onto an XTAG elementary trees sequence is presented. The proposed sequence tagger is based on a Hidden Markov Model (HMM) proceeded by an EM-based algorithm for setting its initial parameters values. The trained model is tested on a part of PTB and about 82% accuracy for the detected sequences is achieved.
AB - MICA [1] is a fast and accurate dependency parser for English that uses an automatically LTAG derived from Penn Treebank (PTB) using the Chen's approach [7]. However, there is no semantic representation related to its grammar. On the other hand, XTAG [20] grammar is a hand crafted LTAG that its elementary trees were enriched with the semantic representation by experts. The linguistic knowledge embedded in the XTAG grammar caused it to being used in wide variety of natural language applications. However, the current XTAG parser is not as fast and accurate as well as the MICA parser. Generating an XTAG derivation tree from a MICA dependency structure could make a bridge between these two notions and gets the benefits of both models. Also, by having this conversion, the applications that use the XTAG parser, may get the helps from MICA parser too. In addition, it can enrich the MICA's grammar by semantic representation of XTAG grammar. In this paper, an unsupervised sequence tagger that maps any sequence of MICA elementary trees onto an XTAG elementary trees sequence is presented. The proposed sequence tagger is based on a Hidden Markov Model (HMM) proceeded by an EM-based algorithm for setting its initial parameters values. The trained model is tested on a part of PTB and about 82% accuracy for the detected sequences is achieved.
KW - Automated extracted tree adjoining grammar (TAG)
KW - Grammar mapping
KW - HMM initializing
KW - Semantic representation
KW - XTAG derivation tree
UR - http://www.scopus.com/inward/record.url?scp=78649267371&partnerID=8YFLogxK
U2 - 10.1109/NLPKE.2010.5587766
DO - 10.1109/NLPKE.2010.5587766
M3 - Article in proceedings
AN - SCOPUS:78649267371
SN - 9781424468966
T3 - Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2010
BT - Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE, 2010
T2 - 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2010
Y2 - 21 August 2010 through 23 August 2010
ER -
ID: 366048038