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CONNECTIVE POSITION, ARGUMENT ORDER AND INFORMATION STRUCTURE OF DISCOURSE CONNECTIVES IN WRITTEN TURKISH TEXTS THESIS SUBMITTED TO THE GRADUATE SCHOOL OF INFORMATICS OF THE MIDDLE EAST TECHNICAL
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How to fill out languages full-textprocessing focus in

01
Identify the language or languages you want to focus on for full-text processing.
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Gather resources and tools specific to each language, such as language models, tokenizers, and libraries.
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Create a strategy for pre-processing text data in each language, including steps like tokenization, stemming, and stop-word removal.
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Implement language-specific techniques for tasks like named entity recognition, part-of-speech tagging, and sentiment analysis.
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Test and evaluate the performance of your full-text processing pipeline for each language to ensure accurate results.

Who needs languages full-textprocessing focus in?

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Natural language processing researchers and practitioners who work with text data in multiple languages.
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Companies with multilingual applications and services that require accurate language processing capabilities.
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Organizations dealing with global communications, customer support, or content management across different languages.
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