{"title":"A Customizable Time Warping Method for Motion Alignment","authors":"S. A. Etemad, A. Arya","doi":"10.1109/ICSC.2013.72","DOIUrl":"https://doi.org/10.1109/ICSC.2013.72","url":null,"abstract":"This paper presents Correlation-optimized Time Warping (CoTW) for aligning motion sequences. The proposed method maximizes an objective function based on the correlation of the two sequences. There are several parameters involved in the process, which can be computationally optimized or manually customized. Customization can take place based on the number and/or nature of actions in the sequences. CoTW shows robust performance for aligning simple gait sequences as well as sequences containing several different actions.","PeriodicalId":189682,"journal":{"name":"2013 IEEE Seventh International Conference on Semantic Computing","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114726440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"WordNet Hierarchy Axiomatization and the Mass-Count Distinction","authors":"Jonathan Gordon, Lenhart K. Schubert","doi":"10.1109/ICSC.2013.31","DOIUrl":"https://doi.org/10.1109/ICSC.2013.31","url":null,"abstract":"Word Net provides a semantic hierarchy with broad lexical coverage, which has proved sufficiently precise to boost performance at many tasks involving natural language. However, it has not yet been formalized for use in a general reasoning system. In this paper, we present such a formalization. We use a semi-automatic annotation of Word Net with lexical features - most notably the mass-count distinction - to recognize inferentially different relations between concepts. The result is a collection of 77,263 lexical-semantic axioms, which are being released for general use. We evaluate a sample of the axioms for core concepts, showing their quality to be significantly better than a baseline interpretation of Word Net.","PeriodicalId":189682,"journal":{"name":"2013 IEEE Seventh International Conference on Semantic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130943513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Applying Textual Entailment to the Interpretation of Metaphor","authors":"Michael Mohler, Marc T. Tomlinson, D. Bracewell","doi":"10.1109/ICSC.2013.30","DOIUrl":"https://doi.org/10.1109/ICSC.2013.30","url":null,"abstract":"Metaphor is a pervasive feature of human language that enables us to conceptualize and communicate abstract concepts using more concrete terminology. Unfortunately, computational models of natural language understanding - including systems for question answering, textual entailment, lexical substitution, and word-sense disambiguation - are unable to appropriately grasp the semantic content of metaphor and other forms of figurative language. In particular, we address the problem of understanding metaphoric language in the context of entailment (or paraphrase) detection. We build upon our existing state-of-the-art textual entailment system to specifically address issues of lexical entailment within a metaphoric context and have performed an in-depth experimental analysis to determine which techniques are most effective at interpreting metaphorical text. Our results suggest that a machine learning system trained on metaphor-rich data can achieve an accuracy above 90% for verbal metaphors using a combination of lexical, semantic, and contextual measures of term similarity.","PeriodicalId":189682,"journal":{"name":"2013 IEEE Seventh International Conference on Semantic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131023209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}