Abstract
Recently, there is an increasing interest in integrating rule based methods with statistical techniques for developing robust, wide coverage, high performance parsing systems. In this paper, we describe an architecture, called UCSG shallow parser architecture, which combines linguistic constraints expressed in the form of finite state grammars with statistical rating using HMMs built from a POS-tagged corpus and an A* search for global optimization for determining the best shallow parse for a given sentence. The primary aim of the design of the UCSG parsing architecture is developing a judicious combination of linguistic and statistical methods to develop wide coverage robust shallow parsing systems, without the need for large scale manually parsed training corpora. The UCSG architecture uses a grammar to specify all valid structures and a statistical component to rate and rank the possible alternatives, so as to produce the best parse first without compromising on the ability to produce all possible parses. The architecture supports bootstrapping with an aim to reduce the need for parsed training corpora. The complete system has been implemented in Perl under Linux. In this paper we first describe the UCSG shallow parsing architecture and then focus on the evaluation of the UCSG finite state grammar for the chunking task for English. Recall of 91.16% and 93.73% have been obtained on the Susanne parsed corpus and CoNLL 2000 chunking task test data set respectively. Extensive experimentation is under way to evaluate the other modules.
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Kumar, G.B., Murthy, K.N. (2006). UCSG Shallow Parser. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_18
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DOI: https://doi.org/10.1007/11671299_18
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