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See Gradescope for the most up to date due dates.
Course topics outline
- Words and Classification Models (9/7-9/21)
- Datasets and Classification Applications (9/23-10/7)
- Linguistic Structure (10/12-10/21, approx.)
- Embeddings, Neural Networks (10/26-11/2, approx.)
- BERT and Pre-trained LMs (11/16-11/18, approx.)
- NLP Applications (11/23-12/2, approx.)
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Readings should be done before the indicated class.
The main textbooks are available free online:
- JM = Jurafsky and Martin, Speech and Language Processing, 3rd edition. (We'll use this one the most.)
- INLP = Eisenstein, Intro to NLP, online text (or book version)
Tentative schedule follows.
Tue 9/14: Naive Bayes Text Classification
Thu 9/16: Naive Bayes Text Classification, continued
Tue 9/21: Logistic Regression Classification
Thu 9/23: Annotation and Labeling
Tue 10/12: Tagging, part 1: Parts of Speech and Named Entities
- No separate readings for this day. Topics include: lexical ambiguity, word sense disambiguation, NER, BIO encoding, and sequence models.
Tue 10/26: Word Embeddings (I)
Thu 10/28: Word Embeddings (II)
Tue 11/2: Neural Networks
Thu 11/4: Midterm Review and Misc. Topics
Tue 11/9: In-class midterm
No class Thu 11/11 (holiday)
11/16-11/18: Pretrained Neural Language Models (BERT)
11/23-12/2: NLP Applications
Tue 12/7: Final presentations
Rest of semester
Tentative future dates. Subject to revision.
- ~10/29: HW3 due
- Early to mid-November: Midterm (in-class)
- Mid to late-November: Project progress report due
- ~12/3: HW4 due
Tue 12/7: Posters/presentations
Last day of class! The poster or presentation session will take place during and around the standard lecture time.
Thu 12/16: Final project due (last day of finals)
Final project reports are due by the last day of the final exam period. Late days cannot be used.