Achim G. Hoffmann

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This paper introduces a new method for the rapid development of complex rule bases involving cue phrases for the purpose of classifying text segments. The method is based on Ripple-Down Rules, a knowledge acquisition method that proved very successful in practice for building medical expert systems and does not require a knowledge engineer. We implemented(More)
The development of highly eeective heuristics for search problems is a diicult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have introspective access to that knowledge , their explanations of actual search considerations seems very valuable in(More)
We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients(More)
The development of highly e!ective heuristics for search problems is a di$cult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have complete introspective access to that knowledge, their explanations of actual search considerations seem very valuable in(More)
The contribution of this paper is threefold: It substantially extends Ripple Down Rules, a proven eeective method for building large knowledge bases without a knowledge engineer. Furthermore, we propose to develop highly eeective heuristics searchers for combinatorial problems by a knowledge acquisition approach to acquire human search knowledge. Finally,(More)
Summarization, like other natural language processing tasks, is tackled with a range of different techniques particularly machine learning approaches, where human intuition goes into attribute selection and the choice and tuning of the learning algorithm. Such techniques tend to apply differently in different contexts, so in this paper we describe a hybrid(More)
The Semantic Web can be conceived as an extension of the current Web where information is given welldefined meaning. In this scenario ontologies are crucial since they provide meaning and facilitate the search for contents and information. Ontology population is a knowledge acquisition activity used to transform data sources into instance data. The(More)
Our research aims at interactive document viewers that can select and highlight relevant text passages on demand. Another related objective is the generation of topic-specific summaries of texts as opposed to general purpose summaries. This paper introduces our notions of <i>discourse structure tree</i> and <i>level-of-detail tree</i>. Both structures are(More)
The aim of our work is to provide support for reading (or skimming) scientific papers. In this paper we report on the task to identify concepts or terms with positive attributions in scientific papers. This task is challenging as it requires the analysis of the relationship between a concept or term and its sentiment expression. Furthermore, the context of(More)