By Yaakov HaCohen-Kerner, Aharon Tayeb (auth.), Allan Ramsay, Gennady Agre (eds.)

ISBN-10: 3642331858

ISBN-13: 9783642331855

This publication constitutes the refereed lawsuits of the fifteenth foreign convention on synthetic Intelligence: technique, platforms, and purposes, AIMSA 2012, held in Varna, Bulgaria in September 2012.
The 36 revised complete papers provided have been conscientiously reviewed and chosen from quite a few submissions. The papers are geared up in topical sections on normal language processing, social networks, wisdom illustration and reasoning, computer studying, making plans and brokers, seek, and laptop vision.

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Extra resources for Artificial Intelligence: Methodology, Systems, and Applications: 15th International Conference, AIMSA 2012, Varna, Bulgaria, September 12-15, 2012. Proceedings

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We trained logistic regression classifier using only MSR values to predict (m, n)-cousin relationship between words. Classifier was trained to recognize the following classes: 0 ≤ m, n ≤ 3 and negative class which indicates more distant cousin relationship or not at all. g. the clustering method used, so we used a classifier based on the same information instead. In our experiments we applied MSR as an input feature to the logistic regression classifier. MSR was calculated as a cosine similarity between word distributional vectors: each vector element corresponds to the frequency of co-occurrences with other words in the selected dependency relations.

In further works we plan to run more through search of parameter space of LL-AAA. We also plan to test other datasets with LL-AAA. The work on DBC can be extended also with larger datasets and more diverse features. Acknowledgments. Work partially financed by the Polish Ministry of Education and Science, Project N N516 068637. References 1. : Automatic parameter selection by minimizing estimated error. In: Proc. of 12th Twelfth ICML 1995, p. 304 (1995) 2. : Tuning pattern classifier parameters using a ga with an application in mobile robotics.

First we parsed Wikipedia corpus with dependency parser Minipar and then we extracted all patterns between two nouns from dependency graphs. Every pattern that occurred at least five times between an unique word pair defined a feature for logistic regression classifier from LibLINEAR. The second KS is cousin classifier based on distributional similarity in a similar way to [7]. We trained logistic regression classifier using only MSR values to predict (m, n)-cousin relationship between words. Classifier was trained to recognize the following classes: 0 ≤ m, n ≤ 3 and negative class which indicates more distant cousin relationship or not at all.

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Artificial Intelligence: Methodology, Systems, and Applications: 15th International Conference, AIMSA 2012, Varna, Bulgaria, September 12-15, 2012. Proceedings by Yaakov HaCohen-Kerner, Aharon Tayeb (auth.), Allan Ramsay, Gennady Agre (eds.)


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