We describe a stochastic approach to partial parsing, i.e., the recognition of syntactic structures of limited depth. The technique utilises Markov Models, but goes beyond usual bracketing approaches, since it is capable of recognising not only the boundaries, but also the internal structure and syntactic category of simple as well as complex NP's, PP's, AP's and adverbials. We compare tagging accuracy for different applications and encoding schemes.
@cite_0: A language-independent method of finite-state surface syntactic parsing and word-disambiguation is discussed. Input sentences are represented as finite-state networks already containing all possible roles and interpretations of its units. Also syntactic constraint rules are represented as finite-state machines where each constraint excludes certain types of ungrammatical readings. The whole grammar is an intersection of its constraint rules and excludes all ungrammatical possibilities leaving the correct interpretation(s) of the sentence. The method is being tested for Finnish, Swedish and English.
@cite_2: We describe the use of energy function optimization in very shallow syntactic parsing. The approach can use linguistic rules and corpus-based statistics, so the strengths of both linguistic and statistical approaches to NLP can be combined in a single framework. The rules are contextual constraints for resolving syntactic ambiguities expressed as alternative tags, and the statistical language model consists of corpus-based n-grams of syntactic tags. The success of the hybrid syntactic disambiguator is evaluated against a held-out benchmark corpus. Also the contributions of the linguistic and statistical language models to the hybrid model are estimated.
Symbolic NP chunkers usually rely on finite automata and or pattern matching, cf. @cite_0 , @cite_2 . presents a partial parsing technique based on cascaded finite automata. describe a POS tagger and shallow parser combining symbolic and stochastic processing via relaxation labelling .