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Semantic Parsing is the conversion of natural language into a logical form, a structured representation that captures its meaning and can be processed by machines, critical for question answering systems and chatbots.

Ontologies in NLP are structured frameworks that define the relationships between various concepts and entities, facilitating machine understanding of complex domain-specific knowledge.

Pragmatics is the study of how context influences the interpretation of meaning in language, helping NLP systems to comprehend intentions, implications, and actions beyond literal statements.

Named Entity Recognition (NER) identifies and classifies named entities in text into pre-defined categories like persons, organizations, locations, etc., essential for information extraction and summarization.

Co-reference Resolution is the task of determining when different expressions in a text (such as pronouns or nouns) refer to the same entity, which is important for maintaining consistency in understanding and summarizing documents.

Word Sense Disambiguation is a process in NLP where the system determines which sense of a word is used in a sentence, when the word has multiple meanings, crucial for accurate interpretation.

Semantic Role Labeling assigns roles such as agent, object, and instrument to words in a sentence, determining their relationships to the main verb, thereby allowing better comprehension of events and actions described in text.

Semantics in NLP refers to the study of meaning and how computational systems can understand human language beyond mere words or syntax, involving the interpretation of sentences, phrases, and texts.

Distributional Semantics is the theory that linguistic items with similar distributions have similar meanings, captured computationally through techniques like word embeddings which are fundamental in many NLP applications.

Sentiment Analysis involves detecting and categorizing opinions expressed in text, particularly to determine the writer's attitude as positive, negative, or neutral, which is key for market analysis and customer feedback.

Polysemy refers to the phenomenon of a single word having multiple meanings or senses that are related by extension, and understanding which sense is intended is a challenge that semantic analysis in NLP addresses.