One useful source of evidence for evaluating a candidate answer to a question is a passage that contains the candidate answer and is relevant to the question. In the DeepQA pipeline, we retrieve passages using a novel technique that we call Supporting Evidence Retrieval, in which we perform separate search queries for each candidate answer, in parallel, and include the candidate answer as part of the query. We then score these passages using an assortment of algorithms that use different aspects and relationships of the terms in the question and passage. We provide evidence that our mechanisms for obtaining and scoring passages have a substantial impact on the ability of our question-answering system to answer questions and judge the confidence of the answers. HypothesesWe evaluated the following two hypotheses.Hypothesis 1VAnalyzing passages using a variety of strategies, at different depths of analysis, is significantly more effective than a single scoring strategy. Specifically,

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Stability of 4-methylthio-3-butenyl isothiocyanate (MTBI) was studied in an aqueous medium. Most of (E)-MTBI was degraded within 3h by shaking or sonicating and within 6h on an agar plate. (Z)-MTBI was more stable. Antimicrobial activity of MTBI on 8 bacteria, 3 yeasts, and 5 fungi was estimated in a gaseous phase. The growth of fungi were strongly inhibited by MTBI in the dose range 2.5-7.5 ƒÊmol/ plate. The fact that the maximum amount of MTBI in head space was only 0.2% of the dose suggested that the antimicrobial activity of MTBI was very high.

In this paper, we describe the extension of an existing monolingual QA system for English-to-Chinese and English-to-Japanese cross-lingual question answering (CLQA). We also attempt to characterize the influence of translation on CLQA performance through experimental evaluation and analysis. The paper also describes some language-specific issues for keyword translation in CLQA.

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