We have investigated the potential use of question answering systems and participated in the Question Answering Challenge (QAC) of National institute of informatics Test Collection for Information Retrieval systems (NTCIR). In this paper, we describe our question answering system, the preliminary results of our experiments to the contest and some possible improvement to question answering systems.

The input of this component is the question sentence and the output is the list of keywords, NE types of the answer, and several kinds of minor information. The sentence is first analyzed by morphological analyzer, JUMAN [JUMAN homepage], and our NE tagger. Words are concatenated if it is a sequence of noun-prefix, nouns and noun-suffix or a NE expression, while the individual words remains (wh...

This paper describes our system and additional experimental results in NTCIR-4 QAC Task 1. The main components of our system are question classification, passage retrieval, and named entity extraction. Passage retrieval was performed by a density-based ranking method based on importance of query terms occurred in the passage. Question classification and Named entity extraction were designed by ...

This paper investigates our experimental results for NTCIR-4 QAC2, the second attempt to evaluate the technology of Japanese question answering (QA). Our basic approach is a combination of information retrieval and named entity (NE) extraction based on pattern matching. The results show that the accuracy of NE extraction crucially affects the overall performance of our system. Additional experi...

OSC-Qasm is a cross-platform, Python-based, OSC interface for executing Qasm code. It serves as simple way to connect creative programming environments like Max (with The QAC Toolkit) and Pure Data with real quantum hardware, using the Open Sound Control protocol. In this paper, authors introduce context meaning of developing tool this, what it can offer artists.