The ATIS spoken language systems pilot corpus
HLT '90 Proceedings of the workshop on Speech and Natural Language
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Automatic grammar generation from two different perspectives
Automatic grammar generation from two different perspectives
Towards efficient statistical parsing using lexicalized grammatical information
Towards efficient statistical parsing using lexicalized grammatical information
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Supertagging: an approach to almost parsing
Computational Linguistics
Integrating compositional semantics into a verb lexicon
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
MICA: a probabilistic dependency parser based on tree insertion grammars application note
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
The hidden TAG model: synchronous grammars for parsing resource-poor languages
TAGRF '06 Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms
Journal of Computer and System Sciences
Parsing Beyond Context-Free Grammars
Parsing Beyond Context-Free Grammars
An unsupervised approach for linking automatically extracted and manually crafted LTAGs
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Efficient computation of the hidden Markov model entropy for a given observation sequence
IEEE Transactions on Information Theory
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LTAG is a rich formalism for performing NLP tasks such as semantic interpretation, parsing, machine translation and information retrieval. Depend on the specific NLP task, different kinds of LTAGs for a language may be developed. Each of these LTAGs is enriched with some specific features such as semantic representation and statistical information that make them suitable to be used in that task. The distribution of these capabilities among the LTAGs makes it difficult to get the benefit from all of them in NLP applications. This paper discusses a statistical model to bridge between two kinds LTAGs for a natural language in order to benefit from the capabilities of both kinds. To do so, an HMM was trained that links an elementary tree sequence of a source LTAG onto an elementary tree sequence of a target LTAG. Training was performed by using the standard HMM training algorithm called Baum-Welch. To lead the training algorithm to a better solution, the initial state of the HMM was also trained by a novel EM-based semi-supervised bootstrapping algorithm. The model was tested on two English LTAGs, XTAG (XTAG-Group, 2001) and MICA's grammar (Bangalore et al., 2009) as the target and source LTAGs, respectively. The empirical results confirm that the model can provide a satisfactory way for linking these LTAGs to share their capabilities together.