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BACKGROUND Cyanobacteria possess several cytochrome P450s, but very little is known about their catalytic functions. CYP110 genes unique to cyanaobacteria are widely distributed in heterocyst-forming cyanobacteria including nitrogen-fixing genera Nostoc and Anabaena. We screened the biocatalytic functions of all P450s from three cyanobacterial strains of(More)
We propose a new model for the guided text summarization task. In this task, it is required that a generated summary covers all the <i>aspects</i>, which are predefined for the topic of the given document cluster; for example, aspects for the topic "Accidents and Natural Disasters" include WHAT, WHEN, WHERE, WHY, WHO AFFECTED, DAMAGES and COUNTERMEASURES.(More)
We describe our two query-oriented summarization systems implemented for the NTCIR-9 1CLICK task. We regard a Question Answering problem as a summarization process. Both of the systems are based on the integer linear programming technique, and consist of an abstractive summarization model and a model ensuring to cover diversified aspects for answering(More)
This paper describes the textual entailment system of FLL for RITE-2 task in NTCIR-10. Our system is based on a set of local alignments conducted on different linguistic units, such as word, Japanese base phrase, numerical expression, Named Entity, and sentence. Our system uses features obtained from local alignments' results. We applied our system to(More)
This paper describes our system for answering Center Exam subtask of QALab with three solvers. The first solver is based on search results obtained with different search engines as clues for answering questions. The second solver is trained with text books and virtual examples, generated automatically from text books by randomly replacing words in the text(More)
In Targeted Entity Disambiguation setting, we take (i) a set of entity names which belong to the same domain (target entities), (ii) candidate mentions of the given entities which are texts that contain the target entities as input, and then determine which ones are true mentions of " target entity ". For example, given the names of IT companies, including(More)
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