Abstract
Case Based Reasoning(CBR), an artificial intelligence technique, solves new problem by reusing solutions of previously solved similar cases. In conventional CBR, cases are represented in terms of structured attribute-value pairs. Acquisition of cases, either from domain experts or through manually crafting attribute-value pairs from incident reports, constitutes the main reason why CBR systems have not been more common in industries. Manual case generation is a laborious, costlier and time consuming task. Textual CBR (TCBR) is an emerging line that aims to apply CBR techniques on cases represented as textual descriptions. Similarity of cases is based on the similarity between their constituting features. Conventional CBR benefits from employing domain specific knowledge for similarity assessment. Correspondingly, TCBR needs to involve higher-order relationships between features, hence domain specific knowledge. In addition, the term order has also been contended to influence the similarity assessment. This paper presents an account where features and cases are represented using a distributed representation paradigm that captures higher-order relations among features as well as term order information.
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Öztürk, P., Prasath, R.R., Moen, H. (2010). Distributed Representations to Detect Higher Order Term Correlations in Textual Content. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_78
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DOI: https://doi.org/10.1007/978-3-642-13529-3_78
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