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 -