Professional Certificate in Language Processing Strategies: Information Extraction
-- ViewingNowThe Professional Certificate in Language Processing Strategies: Information Extraction is a crucial course designed to empower learners with essential skills in language processing. This program focuses on teaching effective strategies for extracting valuable information from unstructured text data, a high-in-demand skill in today's data-driven world.
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Here are the essential units for a Professional Certificate in Language Processing Strategies: Information Extraction:
• Fundamentals of Language Processing: In this unit, students will learn about the basic concepts, techniques, and tools used in natural language processing, including tokenization, part-of-speech tagging, parsing, and semantic role labeling. They will also gain an understanding of the challenges and opportunities of working with human language data at scale.
• Introduction to Information Extraction: This unit will cover the basics of information extraction, including the definition, history, and applications of the field. Students will learn about the different types of information extraction tasks, such as named entity recognition, entity linking, and relation extraction, and how they can be used to extract structured data from unstructured text.
• Named Entity Recognition: This unit will focus on named entity recognition (NER), a common information extraction task that involves identifying and classifying named entities, such as people, organizations, and locations, in text. Students will learn about the different approaches to NER, including rule-based, machine learning, and deep learning methods, and how to evaluate and improve the performance of NER systems.
• Entity Linking: In this unit, students will learn about entity linking, a task that involves linking mentions of entities in text to their corresponding entries in a knowledge base. They will learn about the different types of entity linking, such as within-document and cross-document linking, and how to evaluate and improve the performance of entity linking systems.
• Relation Extraction: This unit will cover relation extraction, a task that involves identifying and classifying the semantic relationships between entities in text. Students will learn about the different approaches to relation extraction, such as pattern-based, machine learning, and deep learning methods, and how to evaluate and improve the performance of relation extraction systems.
• Information Extraction Evaluation: In this
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