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. 1

Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems. The algorithm finds a combination of values for a set of variables such that satisfies -- to the maximum possible degree -- a set of given constraints. This pat)er scribes some experiments performed applying it to POS tagging, and the results obtained. it also ponders the possibility of applying it, to Word Sense Disambiguation.

. We have applied inductive learning of statistical decision trees to the Natural Language Processing (NLP) task of morphosyntactic disambiguation (Part Of Speech Tagging). Previous work showed that the acquired language models are independent enough to be easily incorporated, as a statistical core of rules, in any flexible tagger. They are also complete enough to be directly used as sets of POS disambiguation rules. We have implemented a quite simple and fast tagger that has been tested and evaluated on the Wall Street Journal (WSJ) corpus with a remarkable accuracy. In this paper we basically address the problem of tagging when only small training material is available, which is crucial in any process of constructing, from scratch, an annotated corpus. We show that quite high accuracy can be achieved with our system in this situation. In addition we also face the problem of dealing with unknown words under the same conditions of lacking training examples. In this case some comparati...

The current paradigm in parsing has been developed primarily using English, a language that relies on word order to express grammatical function. However, most languages in the world rely much more on NP-marking to express the same functions. We propose therefore a shallow NP parsing technique which makes much more use of NP-marking, and evaluate the technique on Korean, an agglutinating language. 1 Introduction In this paper, we take a shallow parsing approach to identifying Noun Phrases and their grammatical relationship to the verb. Unlike more conventional parsers, our techniques rely on local analysis and the use of decision trees which are trained on syntactically annotated corpora. 1.1 Coding Grammatical Function One of the most important reasons for performing parsing is to determine the functional relationships that exist between constituents in a sentence. Determining these relationships is necessary for many, if not most, applications of parsing, including Machine Tr...

An implementation of a Spanish POS tagger is described in this paper. This implementation combines three basic approaches: a single word tagger based on decision trees, a POS tagger based on variable memory Markov models, and a feature structures set of tags. Using decision trees for single word tagging allows the tagger to work without a lexicon that lists only possible tags. Moreover, it decreases the error rate because there are no unknown words. The feature structure set of tags is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages like Spanish. Finally, variable memory Markov models training is more efficient than traditional full-order Markov models and achieves better accuracy. In this implementation, 98.58Y0 of tokens are correctly classified.

We describe the use of energy function optimisation in very shallow syntactic pars- ing. 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.

Relaxation labelling is an optimization technique used in many fields to solve constraint satisfaction problems (CSP). The algorithm finds a combination of values for a set of variables such that satisfies-to the maximum possible degree- a set of given constraints. This paper describes some experiments performed applying it to POS tagging and the constraints used.