There is a growing consensus that significant, rapid progress can be made in both text understanding and spoken language understanding by investigating those phenomena that occur most centrally in naturally occurring unconstrained materials and by attempting to automatically extract information about language from very large corpora. Such corpora are beginning to serve as important research tools for investigators in natural language processing, speech recognition, and integrated spoken language systems, as well as in theoretical linguistics. Annotated corpora promise to be valuable for enterprises as diverse as the automatic construction of statistical models for the grammar of the written and the colloquial spoken language, the development of explicit formal theories of the differing grammars of writing and speech, the investigation of prosodic phenomena in speech, and the evaluation and comparison of the adequacy of parsing models. In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus 1 consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. These materials are available to members of the Linguistic Data Consortium; for details, see Section 5.1.

The Penn Treebank has recently implemented a new syntactic annotation scheme, designed to highlight aspects of predicate-argument structure. This paper discusses the implementation of crucial aspects of this new annotation scheme. It incorporates a more consistent treatment of a wide range of grammatical phenomena, provides a set of coindexed null elements in what can be thought of as "underlying " position for phenomena such as wh-movement, passive, and the subjects of infinitival constructions, provides some non-context free annotational mechanism to allow the structure of discontinuous constituents to be easily recovered, and allows for a clear, concise tagging system for some semantic roles. 1. INTRODUCTION During the first phase of the The Penn Treebank project [10], ending in December 1992, 4.5 million words of text were tagged for part-of-speech, with about two-thirds of this material also annotated with a skeletal syntactic bracketing. All of this material has been hand corre...

The Penn Treebank, in its eight years of operation (1989-1996), produced approximately 7 million words of part-of-speech tagged text, 3 million words of skeletally parsed text, over 2 million words of text parsed for predicateargument structure, and 1.6 million words of transcribed spoken text annotated for speech disfluencies. This paper describes the design of the three annotation schemes used by the Treebank: POS tagging, syntactic bracketing, and disfluency annotation and the methodology employed in production. All available http://www.ldc.upenn.edu.

Most of the world’s natural languages have complex morphology. But the expense of building morphological analyzers by hand has prevented the development of morphological analysis systems for the large majority of languages. Unsupervised induction techniques, that learn from unannotated text data, can facilitate the development of computational morphology systems for new languages. Such unsupervised morphological analysis systems have been shown to help natural language processing tasks including speech recognition (Creutz, 2006) and information retrieval (Kurimo and Turunen, 2008). This thesis describes ParaMor, an unsupervised induction algorithm for learning morphological paradigms from large collections of words in any natural language. Paradigms are sets of mutually substitutable morphological operations that organize the inflectional morphology of natural languages. ParaMor focuses on the most common morphological process, suffixation. ParaMor learns paradigms in a three-step algorithm. First, a recall-centric search scours a space of candidate partial paradigms for those which possibly model suffixes of true paradigms. Second, ParaMor merges selected candidates that appear to model portions

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In this paper a research on the importance of human familiarity with texts to be sense-tagged in the field of Word Sense Disambiguation is presented. This research investigates whether people who are more familiar with English texts about physics, better disambiguate words in such a text, than people who are not that familiar with such a text. For the population considered in this study became evident this was not the case. A possible explanation for this outcome could be that the degree of linguistic skills had a bigger impact on the scores than the familiarity with the texts. Another explanation can be found in the fact that the participants were nonnative English speakers.

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The unsupervised Data Oriented Parsing (uDOP) approach has been repeatedly reported to achieve state of the art performance in experiments on parsing of different corpora. At the same time the approach is demanding both in computation time and memory. This paper describes an approach which decreases these demands. First the problem is translated into the generation of probabilistic bottom up tree automata (pBTA). Then it is explained how solving two standard problems for these automata results in a reduction in the size of the grammar. The reduction of the grammar size by using efficient algorithms for pBTAs is the main contribution of this paper. Experiments suggest that this leads to a reduction in grammar size by a factor of 2. This paper also suggests some extensions of the original uDOP algorithm that are made possible or aided by the use of tree automata. 1