Abstract
The unsupervised approach for syntactic analysis tries to discover the structure of the text using only raw text. In this paper we explore this approach using Grammar Inference Algorithms. Despite of still having room for improvement, our approach tries to minimize the effect of the current limitations of some grammar inductors by adding morphological information before the grammar induction process, and a novel system for converting a shallow parse to dependencies, which reconstructs information about inductor’s undiscovered heads by means of a lexical categories precedence system. The performance of our parser, which needs no syntactic tagged resources or rules, trained with a small corpus, is 10% below to that of commercial semi-supervised dependency analyzers for Spanish, and comparable to the state of the art for English.
We thank the support of SNI, SIP-IPN, COFAA-IPN, and PIFI-IPN, CONACYT; and the Japanese Government; the first author is a JSPS fellow. The third author is a Visiting Scholar at Waseda University and specifically acknowledges support of SIP-20100773 grant, CONACYT 50206-H grant, and CONACYT scholarship for Sabbatical stay 2010.
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Calvo, H., Gambino, O.J., Gelbukh, A., Inui, K. (2011). Dependency Syntax Analysis Using Grammar Induction and a Lexical Categories Precedence System. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_9
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