The nature of the concepts regarding images in many domains are imprecise, and the interpretation of finding similar images is also ambiguous and diverse on the level of human perception. Considering these features, in this paper, images' semantic classes and the tolerance degree between them are defined systematically, and the approach of modeling tolerance relations between the semantic classes is proposed. On the basis of it, a general mechanism of representing images' semantics by associative values with predefined classes regarding a corresponding dimension is depicted. Moreover, as demonstration, the methods of generating associative values with defined classes regarding the nature vs. man-made dimension and human vs. non-human dimension are described, and experimental results of images' retrieval show the effectiveness of our proposed mechanism of representing images' semantics in improving the precision-recall performance.
We propose a method for affect analysis of textual input in Japanese supported with Web mining. The method is based on a pragmatic reasoning that emotional states of a speaker are conveyed by emotional expressions used in emotive utterances. It means that if an emotive expression is used in a sentence in a context described as emotive, the emotion conveyed in the text is revealed by the used emotive expression. The system ML-Ask (Emotive Elements / Expressions Analysis System) is constructed on the basis of this idea. An evaluation of the system is performed in which two evaluation methods are compared. To choose the most objective evaluation method we compare the most popular method in the field and a method proposed by us. The proposed evaluation method was shown to be more objective and revealed the strong and weak points of the system in detail. In the evaluation experiment ML-Ask reached human level in recognizing the general emotiveness of an utterance (0.83 balanced F-score) and 63% of human level in recognizing the specific types of emotions. We support the system with a Web mining technique to improve the performance of emotional state types extraction. In the Web mining technique emotive associations are extracted from the Web using co-occurrences of emotive expressions with morphemes of causality. The Web mining technique improved the performance of the emotional states types extraction to 85% of human performance.