Abstract. Joost is a question answering system for Dutch which makes extensive use of dependency relations. It answers questions either by table look-up, or by searching for answers in paragraphs returned by IR. Syntactic similarity is used to identify and rank potential answers. Tables were constructed by mining the CLEF corpus, which has been syntactically analyzed in full.
Abstract. We describe the system of the University of Groningen for the monolingual Dutch and multilingual English to Dutch QA tasks. First, we give a brief outline of the architecture of our QA-system, which makes heavy use of syntactic information. Next, we describe the modules that were improved or developed especially for the CLEF tasks, among others incorporation of syntactic knowledge in IR, incorporation of lexical equivalences and coreference resolution, and a baseline multilingual (English to Dutch) QA system, which uses a combination of Systran and Wikipedia (for term recognition and translation) for question translation. For non-list questions, 31% (20%) of the highest ranked answers returned by the monolingual (multilingual) system were correct.
We investigate the impact of the precision/recall trade-off of information extraction on the performance of an offline corpus-based question answering (QA) system. One of our findings is that, because of the robust final answer selection mechanism of the QA system, recall is more important. We show that the recall of the extraction component can be improved using syntactic parsing instead of more common surface text patterns, substantially increasing the number of factoid questions answered by the QA system.
Lexico-semantic knowledge is becoming increasingly important within the area of natural language processing, especially for applications, such as Word Sense Disambiguation, Information Extraction and Question Answering (QA). Although the coverage of handmade resources, such as WordNet (Fellbaum, 1998), in general is impressive, coverage problems still exist for those applications involving specific domains or languages other than English.
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