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CSE111: Great Ideas in Computer Science

CSE111: Great Ideas in Computer Science. Dr. Carl Alphonce 219 Bell Hall Office hours: M-F 11:00-11:50 645-4739 alphonce@buffalo.edu. cell phones off (please). Announcements. HW5 Part 1 – work on this week Part 2 – work on next week due April 16 4/5-4/9: Artificial Intelligence

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CSE111: Great Ideas in Computer Science

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  1. CSE111: Great Ideas in Computer Science Dr. Carl Alphonce 219 Bell Hall Office hours: M-F 11:00-11:50 645-4739 alphonce@buffalo.edu

  2. cell phones off (please)

  3. Announcements • HW5 • Part 1 – work on this week • Part 2 – work on next week • due April 16 • 4/5-4/9: Artificial Intelligence • 4/12-4/16: Theory • 4/19-4/23: Exam week

  4. Agenda • Today • Computational linguistics • Knowledge representation and reasoning • Next time: • Game playing

  5. Recall: Computational Linguistics is… • Those “computational techniques that process spoken and written language, as language” [Jurafsky & Martin,Speech and Language Processing, pg. 2]

  6. Some applications – extant and envisioned • spelling checkers • grammar checkers • natural language interfaces • information extraction • text summarization • conversational agents • machine translation

  7. Levels of processing • phonetics/phonology – sounds • morphology – word structure • syntax – sentence structure • semantics – meaning • pragmatics – goals of language use • discourse – utterances in context

  8. Basic models • state machines • e.g. finite state automata and transducers • formal rule systems • e.g. regular and context-free grammars • Chomsky hierarchy • logic • e.g. first-order logic, semantic networks • probabilistic/statistical models

  9. Ambiguity – a pervasive problem • An expression is ambiguous if it has two or more different possible interpretations. • Some ambiguity is syntactic… • E.g. Mary saw the man on the hill with a telescope • How many alternate interpretations can you find? • …but ambiguity exists at every level of linguistic representation • E.g. I made her duck (pg. 4 of Jurafsky/Martin book) • I cooked waterfowl for her. • I cooked waterfowl belonging to her. • I created the (fake) duck she owns. • I caused her to quickly lower her head or body. • I waved my magic wand and turned her into undifferentiated waterfowl.

  10. Reasoning • Making implicit knowledge explicit • Traditional example: • All men are mortal. • Socrates is a man. • Socrates is mortal. Explicit knowledge Rule of inference Implicit knowledge

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