Yoji Kiyota

Applying a Burst Model to Detect Bursty Topics in a Topic Model

This paper focuses on two types of modeling of information flow in news stream, namely, burst analysis and topic modeling. First, when one wants to detect a kind of topics that are paid much more attention than usual, it is usually necessary for him/her to carefully watch every article in news stream at every moment. In such a situation, it is well known in the field of time series analysis that Kleinberg’s modeling of bursts is quite effective in detecting burst of keywords. Second, topic models such as LDA (latent Dirichlet allocation) are also quite effective in estimating distribution of topics over a document collection such as articles in news stream. However, Kleinberg’s modeling of bursts is usually applied only to bursts of keywords but not to those of topics. Considering this fact, we propose how to apply Kleinberg’s modeling of bursts to topics estimated by a topic model such as LDA and DTM (dynamic topic model).

Dialog Navigator : A Spoken Dialog Q-A System based on Large Text Knowledge Base

This paper describes a spoken dialog Q-A system as a substitution for call centers. The system is capable of making dialogs for both fixing speech recognition errors and for clarifying vague questions, based on only large text knowledge base. We introduce two measures to make dialogs for fixing recognition errors. An experimental evaluation shows the advantages of these measures.

Exploitation of the wikipedia category system for enhancing the value of LCSH

This paper addresses an approach that integrates two different types of information resources: the Web and libraries. Our method begins from any keywords in Wikipedia, and induces related subject headings of LCSH through the Wikipedia category system.