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Language model adaptation using text data downloaded from the WWW is an efficient way to train a topic-specific LM. We are developing an unsupervised LM adaptation method using data in the Web. The one key point of unsupervised Web-based LM adaptation is how to select keywords to compose the search query. In this
In this paper, we consider the estimation of topic specific Language Models (LM) by exploiting documents from the World Wide Web (WWW). We focus on the quality of the generated queries and propose a novel query generation method. In contrast to the n-gram based queries used in past works, our approach relies on
automatic transcription of a spoken document using a speech recognizer. The difficult point of this task is that the automatic transcription contains many recognition errors, therefore we cannot trust keywords extracted from the automatic transcription using conventional method such as tfmiddotidf. To solve this problem, we
Keywords and searching template, the word segmentation algorithm based on the dictionary of keyword, the storage of searching template and the algorithm of template matching. On the foundation, we implement a QA system for Railway domain application, the experimental result show that QA system based on techniques we employed
This paper describes a manzai robot system with scalability that is developed based on the distributed software components. Manzai is a Japanese traditional stand-up comedy that is usually performed by two comedians. The manzai robots generate their manzai scripts based on web news articles related to keywords given
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