Different Strategies for Combining Diagnostic Problem Solver Methods

. For Diagnostic Problem Solving Methods, appropriate methods and knowledge bases require considerable time for development. Graphical knowledge interfaces and reuse of Problem Solving Methods (PSM) can increase user performance of building knowledge bases. Furthermore classification accuracy is also imporant which can be increased by combinations of different PSM. We combined three algorithms of the Shell--Kit D3 in different ways, to improve evaluation results of PSM applied previously while reusing the same knowledge base. Evaluation results demonstrated limited gains which might be increased by further methological refinement. 1 Introduction The cost of knowledge acquisition, modeling and maintaining for knowledge bases increases exponentially with the degree of completeness. Hence neither completeness nor correctness assumptions can be made for knowledge bases [F. van Harmelen et al 98]. There are several ways to build a knowledge base: by knowledge engineers supported by domain ..

Improving SMT quality with morpho-syntactic analysis

In the framework of statistical machine translation (SMT), correspondences between the words in the source and the target language are learned from bilingual corpora on the basis of so-called alignment models. Many of the statistical systems use little or no linguistic knowledge to structure the underlying models. In this paper we argue that training data is typically not large enough to suciently represent the range of di erent phenomena in natural languages and that SMT can take advantage of the explicit introduction of some knowledge about the languages under consideration. The improvement of the translation results is demonstrated on two di erent German-English corpora. 1 Introduction In this paper, we address the question of how morphological and syntactic analysis can help statistical machine translation (SMT). In our approach, we introduce several transformations to the source string (in our experiments the source language is German) to demonstrate how linguistic knowledge can..

Maximum Entropy Models for Named Entity Recognition

In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional nonannotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy