Discourse Analysis: Structures and Theoretical Models in Natural Language Processing
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Description
This exam evaluates comprehension of discourse analysis in Natural Language Processing. It emphasizes the significance of discourse structures for improving human-computer interactions.
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Exam Details
Duration: 50 minutes
Prerequisites: Introduction to Linguistics, Natural Language Understanding, Cognitive Linguistics
Key Topics
- Coherence
- Cohesion
- Discourse Markers
- Dialogue Systems
- Automated Summarization
Learning Outcomes
- Analyze Discourse Structures
- Articulate Theoretical Models
- Discuss Relevance for NLP Applications
Full Description
This exam investigates the theoretical structures of discourse analysis within Natural Language Processing. It covers coherence, cohesion, and the models employed to understand larger units of language.
Discourse analysis is central to enhancing machine comprehension of context and interactions, vital for applications like dialogue systems and automated summarization, thus improving human-computer communication.
Students will demonstrate their understanding of discourse structures and articulate how these frameworks assist in interpreting human language more effectively during the examination.
Preparation should include engaging with various theoretical models of discourse analysis and their consequences for Natural Language Processing systems.
Sample Questions
- What are the components of coherence in discourse analysis?
- How do discourse structures improve the performance of dialogue systems?
Field: Engineering and Technology
Subfield: Software Engineering
Specialization: Natural Language Processing (NLP)
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