Inductive learning of a knowledge dictionary for a text mining system

Sakurai, S.; Ichimura, Yumi; Suyama, Akihiro; Orihara, Ryohei

doi:10.1007/3-540-45517-5_28

Cited by 14 publications

(8 citation statements)

References 2 publications

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“…Sakurai [2] proposed a tool for composing a knowledge dictionary for text mining. This tool supports knowledge engineers who do not have language-specialized knowledge.…”

Section: Knowledge Dictionarymentioning

confidence: 99%

A knowledge system for promotion of selecting, sharing, and circulation of multilingual technical knowledge

Minowa

Hishiyama

2012

Proceedings of the 30th ACM International Conference on Design of Communication

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A "knowledge system" is a system to allow users to select technical knowledge on a lexical basis from technical papers written in English and share that knowledge. It supports users to read technical paper written in English, understand technical knowledge in English, and complement knowledge with other users' knowledge. We experimented for accumulating English special knowledge, translating it to Japanese, and adding information how much people know the knowledge. And we evaluated the complementation and comprehensiveness of the technical knowledge between two different systems that we proposed and among the users. The experiment(s) showed that the number of Japanese-translated words tends to increase with increasing number of users and the environment facilitates acquiring technical knowledge in their mother language. Using this system, users can collect knowledge comprehensively. In addition, the system provides an effective way to create a high-quality environment for helping users to read English-written technical papers.

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“…Sakurai [2] proposed a tool for composing a knowledge dictionary for text mining. This tool supports knowledge engineers who do not have language-specialized knowledge.…”

Section: Knowledge Dictionarymentioning

confidence: 99%

A knowledge system for promotion of selecting, sharing, and circulation of multilingual technical knowledge

Minowa

Hishiyama

2012

Proceedings of the 30th ACM International Conference on Design of Communication

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“…However, this is not verystraightforward because of the temporal relationship of video frames and their inherent structure. A video isnot only asequence of pictures, it represents the actions and events in a chronological order to convey a story and represent moving visual information [1,2]. For that the following algorithm has been proposed for indexing video clips in a database to be suitable and efficient for retrieval and mining.…”

Section: The Proposed Systemmentioning

confidence: 99%

Approach for Retrieving and Mining Video Clips

AL-Hamami¹,

Hassan²,

AL-Hakeem³

2012

ETJ

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Multimedia (include video, images, audio and text media) is characterized by its high dimensionality, which makes information retrieval and data mining even more challenging. This research proposesa method to build an indexes database for huge collection of video clips, to make the video retrieval and mining much more efficient and perfectthat by considering similarity in both text of sound and features of frames. The proposed method has the following steps: First, isolates video motion from sound in the video clips. Second, converts the sound to text and index the result with database. Third converts video motion to shots, then select the master frame for each one and extracts the feature vector for them such as color, texture, shape and others and finally index the result with database. Fourth,combines the two resulted indexed database (Second and Third steps)into one database and make it the final and standard for both retrieval and mining.

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“…A fuzzy decision tree that uses a fuzzy inductive learning to acquire relations from examples is presented in [32]. The authors generate a concept relation dictionary and a classiÿcation tree from a random set of daily business reports database of text classes concerning retailing.…”

Section: Text Miningmentioning

confidence: 99%