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This PhD dissertation focuses on improving terminology extraction and alignment for applications in the translation industry. It explores three key use cases where these techniques benefit language professionals: creating client-specific terminology lists from large parallel corpora (i.e. translation memories), building domain-specific terminology resources from comparable corpora, and identifying important domain-specific …

The thesis addresses a novel representation learning framework, combining neural and symbolic text representations, and demonstrates its utility for tackling diverse natural language processing problems. The proposed approach, avoiding the deficiencies of purely symbolic and purely neural methods, can be applied for the generation of efficient text representations. Its usefulness …

This thesis addresses the task of formalizing and implementing the process of semi-automatic ontology construction. We propose a theoretical framework for formalizing the ontology construction process. The process is described as a sequence of operators applied to the ontology. Several types of common operators are identified and each type is …

In language technologies, syntactic parsing represents one of the possible intermediate steps of text analysis in the applications such as machine translation, information extraction, question answering, etc. Syntactic trees are often used to demonstrate the structure of text. In the last decades, the dependency framework became a popular syntactic representation, …