Inference

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inference is included as keyword or extra keyword in 0 datasets, 0 tools and 4 publications.

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Publications

Title Author(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Natural language processing neural network considering deep cases Sagara T.
Hagiwara M.
IEEJ Transactions on Electronics, Information and Systems Japanese 2011 In this paper, we propose a novel neural network considering deep cases. It can learn knowledge from natural language documents and can perform recall and inference. Various techniques of natural language processing using Neural Network have been proposed. However, natural language sentences used in these techniques consist of about a few words, and they cannot handle complicated sentences. In order to solve these problems, the proposed network divides natural language sentences into a sentence layer, a knowledge layer, ten kinds of deep case layers and a dictionary layer. It can learn the relations among sentences and among words by dividing sentences. The advantages of the method are as follows: (1) ability to handle complicated sentences; (2) ability to restructure sentences; (3) usage of the conceptual dictionary, Goi-Taikei, as the long term memory in a brain. Two kinds of experiments were carried out by using goo dictionary and Wikipedia as knowledge sources. Superior performance of the proposed neural network has been confirmed. 0 0
Exploiting Wikipedia for directional inferential text similarity Leong C. Wee
S. Hassan
Proceedings - International Conference on Information Technology: New Generations, ITNG 2008 English 2008 In natural languages, variability of semantic expression refers to the situation where the same meaning can be inferred from different words or texts. Given that many natural language processing tasks nowadays (e.g. question answering, information retrieval, document summarization) often model this variability by requiring a specific target meaning to be inferred from different text variants, it is helpful to capture text similarity in a directional manner to serve such inference needs. In this paper, we show how Wikipedia can be used as a semantic resource to build a directional inferential similarity metric between words, and subsequently, texts. Through experiments, we show that our Wikipedia-based metric performs significantly better when applied to a standard evaluation dataset, with a reduction in error rate of 16.1% over the random metric baseline. 0 0
Graphingwiki - A semantic wiki extension for visualising and inferring protocol dependency Juhani Eronen
Roning J.
CEUR Workshop Proceedings English 2006 This paper introduces the Graphingwiki extension toMoinMoin Wiki. Graphingwiki enables the deepened analysis of the Wiki data by augmenting it with semantic data in a simple, practical and easy-to-use manner. Visualisation tools are used to clarify the resulting body of knowledge so that only the data essential for an usage scenario is displayed. Logic inference rules can be applied to the data to perform automated reasoning based on the data. Perceiving dependencies among network protocols presents an example use case of the framework. The use case was applied in practice in mapping effects of software vulnerabilities on critical infrastructures. 0 0
Graphingwiki - a Semantic Wiki extension for visualising and inferring protocol dependency Juhani Eronen
Juha Röning
SemWiki English 2006 This paper introduces the Graphingwiki extension to MoinMoin Wiki. Graphingwiki enables the deepened analysis of the Wiki data by augmenting it with semantic data in a simple, practical and easy-to-use manner. Visualisation tools are used to clarify the resulting body of knowledge so that only the data essential for an usage scenario is displayed. Logic inference rules can be applied to the data to perform automated reasoning based on the data. Perceiving dependencies among network protocols presents an example use case of the framework. The use case was applied in practice in mapping effects of software vulnerabilities on critical infrastructures. 8 0