m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement.

Ever since the landmark paper Ramshaw and Marcus (1995), machine learning systems have been used successfully for identifying base phrases (chunks), the bottom constituents of a parse tree. We expand a state-of-the-art chunking algorithm to a bottom-up parser by recursively applying the chunker to its own output. After testing different training configurations we obtain a reasonable parser which is tested against a standard data set. Its performance falls behind that of current state-of-the-art parsers. We give some suggestions for modifications of the parser which may lead to future performance improvements.

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...

This report describes the development of a parsing system for written Swedish and is focused on a grammar, the main component of the system, semiautomatically extracted from corpora. A cascaded, finite-state algorithm is ap- plied to the grammar in which the input contains coarse-grained semantic class information, and the output produced reflects not only the syntactic structure of the input, but grammatical functions as well. The grammar has been tested on a variety of random samples of dif- ferent text genres, achieving precision and recall of 94.62% and 91.92% respectively, and average crossing rate of 0.04, when evaluated against manually disambiguated, annotated texts.

Clauses are important for a variety of NLP tasks such as predicting phrasing in text-tospeech synthesis and inferring text alignment for machine translation (Ejerhed 1988, Leffa 1998, Papageorgiou 1997). The Computational Natural Language Learning 2001 shared task (Sang & Déjean 2001) set the goal of identifying clause boundaries in text using machine learning methods. Systems created for the task predicted a label for each word specifying the number of clauses starting and ending at that position in the sentence without differentiating between clause types. This work extends that of the shared task in several ways: (1) performance bounds are explored, (2) an attempt is made to distinguish ‘main ’ and ‘subordinate’ clauses, and (3) Winnow and maximum entropy, model classes proven effective in similar domains yet not previously employed for the task, are applied to the problem.