
Optimizing Linguistic Analysis: How MATIC Structures Facilitate FrameNet and Natural Language Fusion
Dilara Ataturk , Associate Professor at The Department of Computer Programming of Trakya University, TurkeyAbstract
In the field of computational linguistics, enhancing the accuracy and depth of linguistic analysis remains a central challenge. This paper explores the role of MATIC (Multilingual and Automatic Textual Information Classifier) structures in facilitating the integration of FrameNet, a lexical database that captures frame semantics, with natural language processing (NLP) systems. By leveraging the structural capabilities of MATIC, we propose a method that optimizes the analysis of linguistic data, bridging the gap between conceptual representations in FrameNet and the syntactic and semantic features inherent in natural language. We demonstrate how MATIC structures enable more effective mapping of linguistic data, enhancing tasks such as word sense disambiguation, syntactic parsing, and semantic role labeling. The integration of these frameworks not only improves the efficiency of NLP systems but also enriches the contextual understanding of language. The findings suggest that MATIC structures offer a promising approach to advancing both linguistic theory and practical NLP applications, providing a robust foundation for future research in semantic processing.
Keywords
MATIC Structures, FrameNet, Natural Language Processing
References
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