Wc use seven machine learning algorithms one t;sk: idenl, it)ing base nom phrases. The results have been processed by (lifin'ent system confi)ination methods and all of these outpertbrmed the best individual result. Wc lmw applied the sewm learners wil, h tim best, combinatot, a majo1'it,y vote of the 1,o t) five sysl,elnS, to a sta.l(lard (bt; sol, and mmaged lt) ilnl)rove the t)cst published resull; tbr this (lata set.

Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 B.2 Portions of the Interlingua Representation . . . . . . . . . . . . . . . . . . . 236 B.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 viii List of Tables 4.1 The Three Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 ix List of Figures 1.1 Parse Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2 Combination Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Repair Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1 Combination Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1 Sample Partial Parse . . . . . . . . . . . . . . . . . . . . . . . . . . .

In this paper, we present achunk based partial parsing system for spontaneous, conversational speech in unrestricted domains. We show that the chunk parses produced by this parsing system can be usefully applied to the task of reranking Nbest lists from a speech recognizer, using a combination of chunk-based n-gram model scores and chunk coverage scores. The input for the system is Nbest lists generated from speech recognizer lattices. The hypotheses from the Nbest lists are tagged for part of speech, \cleaned up " by a preprocessing pipe, parsed by a part of speech based chunk parser, and rescored using a backpropagation neural net trained on the chunk based scores. Finally, the reranked Nbest lists are generated. The results of a system evaluation are promising in that a chunk accuracy of 87.4 % is achieved and the best performance on a randomly selected test set is a decrease in word error rate of 0.3 percent (absolute), measured on the new rst hypotheses in the reranked Nbest lists. 1

We present the system description language that offers a declarative way of specifying new complex NLP systems from already existing modules with the help of three operators: sequence, parallelism, and unrestricted iteration. Given a system description and modules that implement a minimal interface, the compiler returns a running Java program which realizes exactly the desired behavior of the original specication. The execution semantics is complemented by a precise formal semantics, dened in terms of concepts of function theory. The compiler is part of the SProUT shallow language platform, a system for the development and processing of multilingual resources.

Efficient partial parsing systems (chunkers) are urgently required by various natural language application areas as these parsers always produce partially parsed text even when the text does not fully fit existing lexica and grammars. Availability of partially parsed corpora is absolutely necessary for extracting various kinds of information that may then be fed into those systems, increasing their processing power. In this paper, we propose an efficient partial parsing scheme based on chart parsing that is flexible enough to support both normal parsing tasks and diagnosis in previously obtained partial parses of possible causes (kinds of faults) that led to those partial parses instead of complete parses. Through the use of the built-in tabulation capabilites of the DyALog system, we implemented a partial parser that runs as fast as the best non-deterministic parsers. In this paper we ellaborate on the implementation of two different grammar formalisms: Definite Clause Grammars (DCG) extended with head declarations and