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Monday and Wednesday 3:30–5:20 pm, KAP 166
Required: Natural Language Processing - Eisenstein -- or free version
Required: Selected papers from NLP literature, see (evolving) schedule
Optional: Introduction to Deep Learning - Charniak -- first three chapters here
Optional: Speech and Language Processing 3rd edition - Jurafsky, Martin
10% - In class participation
10% - Posted questions before each in-class selected paper presentation
10% - In-class selected paper presentation
30% - Three Homeworks (10% each)
40% - Project, comprising proposal (5%), first version of report (5%), in-class presentation (10%), and final report (20%). Done in small groups.
Final report is due December 13, 2021, 4:00 PM PST
On Piazza, Slack, or in class/office hours. Please do not email (unless notified otherwise).
(subject to change per instructor/class whim) (will not be presented in this order):
: Linguistic Stack (graphemes/phones - words - syntax - semantics - pragmatics - discourse) : Tools: : Corpora, Corpus statistics, Data cleaning and munging : Annotation and crowdwork : Evaluation : Models/approaches: rule-based, automata/grammars, perceptron, logistic regression, neural network models : Effective written and oral communication : Components/Tasks/Subtasks: : Language Models : Syntax: POS tags, constituency tree, dependency tree, parsing : Semantics: lexical, formal, inference tasks : Information Extraction: Named Entities, Relations, Events : Generation: Machine Translation, Summarization, Dialogue, Creative Generation
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