Online chat systems, e.g.., Twitter and Slack, have been used in academic conferences or study meetings as a means of instant discussion and sharing related information alongside a real presentation. We propose a system for activating online discussion by providing a bot that suggests webpages related to current timeline of the discussion. Our system generates keyword vectors according to discussion timeline, searches best related webpages from several web sites, and timely provides these pages to the discussion timeline. This paper describes deployments of our system in two types of meetings: lightning talk format meetings and group meetings; and daily exchanges using online chat system. As a result, we could not find good enough reactions to the bot's postings from meeting participants at the lightning talk format meetings, but we could observe more reactions and progress of discussion caused by the bot's postings at the relaxed meetings and daily exchanges among group members.

Comparable Corpora are valuable resources for many NLP applications, and extensive research has been done on information mining based on comparable corpora in recent years. While there are not enough large-scale available public comparable corpora at present, this paper presents a bi-directional CLIR-based method for creating comparable corpora from two independent news collections in different languages. The original Chinese document collections and English documents collections are crawled from XinHuaNet respectively and formatted in a consistent manner. For each document from the two collections, the best query keywords are extracted to represent the essential content of the document, and then the keywords are translated into the language of the other collection. The translated queries are run against the collection in the same language to pick up the candidate documents in the other language and candidates are aligned based on their publication dates and the similarity scores. Results show that our approach significantly outperforms previous approaches to the construction of Chinese-English comparable corpora.

Recently, a flexible image database retrieval system where image keywords can be captured automatically is strongly required, in order to manage a practical number of image data successfully. However, image recognition/understanding technology level is not generally sufficient enough to achieve this requirement. In order to overcome this problem, a new type of image database framework is proposed in this paper. In the proposed system, image keywords are extracted in fully-automated fashion by the flexible and generalized image recognition system. Image keywords employed in this system are a collection of recognized objects in the image, where achieved recognition levels are allowed to be intermediate or imperfect. The concept of recognition thesaurus" has been introduced to manage these various abstraction level of kerwords successfully. As an embodiment of this concept, an experimental image database with various types of sports scenes has been implemented and various retrieval evaluations have been performed. Experimental results reveal the effectiveness of the proposed method.