Tudor Muresan

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High accuracy is essential to any data mining process. A large part of the factors which influence the success of a data mining problem reside in the quality of the data used. Feature selection represents one of the tools which can refine a dataset before presenting it to a learning scheme. This paper analyzes a wrapper approach for feature selection, with(More)
The applicability of learning methods to raw data coming from different areas of human activity is one of the main concerns in data mining research today. This paper emphasizes the need for a sound preprocessing method to improve the quality of the learning process through data imputation. Three classification methods we have previously developed are(More)
We present a relational learning framework for grammar induction that is able to learn meaning as well as syntax. We introduce a type of constraint-based grammar, lexicalized well-founded grammar (lwfg), and we prove that it can always be learned from a small set of semantically annotated examples, given a set of assumptions. The semantic representation(More)
This paper presents the theoretical foundation of a new type of constraint-based grammars, Lexicalized Well-Founded Grammars, which are adequate for modeling human language and are learnable. These features make the grammars suitable for developing robust and scalable natural language understanding systems. Our grammars capture both syntax and semantics and(More)
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