| Jan 25 |
Introduction |
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NLP (circa 2001) |
| Jan 30 |
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PDF |
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Text classification |
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M&S 7.4,16.2-16.3, Collins: Naive Bayes (Sec 1-4), Collins: Log Linear (Sec 2), MaxEnt, Baselines, CNN Classification Naive Bayes prior derivation |
| Feb 1 |
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PDF |
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| Feb 6 |
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PDF |
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| Feb 8 |
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PDF |
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| Feb 13 |
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Neural networks |
PDF |
Primer, Back-prop, Deep Averaging Networks, Gradient Checks (briefly), Gradient Checks (in details) |
| Feb 15 |
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PDF |
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Computation graphs |
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Intro to Computation Graphs |
| Feb 20 |
No class – Feburary break |
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| Feb 22 |
Lexical semantics and embeddings |
PDF |
w2v explained, word2vec, word2vec phrases, Hill2016, Turney2010 |
| Feb 27 |
DyNet and error analysis |
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DyNet tutorials |
| Mar 1 |
Lexical semantics (contd.) |
PDF |
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| Mar 6 |
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PDF |
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Language modeling |
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J&M 4, M&S 6, Collins: LM, Smoothing, Char RNN |
| Mar 8 |
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PDF |
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| Mar 13 |
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PDF |
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| Mar 15 |
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Sequence modeling |
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J&M 5.1-5.3, 6, M&S 3.1, 9, 10.1-10.3, Collins: HMM, Collins: MEMMs (Sec 3), Collins: CRF (sec 4), Collins: Forward-backward, SOTA Taggers, TnT Tagger, Stanford Tagger |
| Mar 20 |
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PDF |
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| Mar 22 |
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PDF |
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| Mar 27 |
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PDF |
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| Mar 29 |
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PDF |
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Recurrent neural networks |
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BPTT, RNN Tutorial, Effectiveness, Luong2015 |
| Apr 3 |
No class – spring break |
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| Apr 5 |
No class – spring break |
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| Apr 10 |
Recurrent neural networks |
PDF |
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| Apr 12 |
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PDF |
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| Apr 17 |
Dependency parsing |
PDF |
J&M 12.7, Nivre2003, Chen2014 |
| Apr 19 |
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PDF |
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| Apr 24 |
Constituency parsing |
PDF |
J&M 12.1-12.6, 13.1-13.4, 14.1-14.4, M&S 11, 12.1, Collins: PCFGs, Eisner: Inside-outside, Collins: Inside-outside |
| Apr 26 |
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PDF |
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| May 1 |
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IBM translation models |
PDF |
J&M 25.5, M&S 13.1-13.2, Collins: IBM Models, IBM Models, Collins: EM (Sec 5-6), HMM alignments, IBM Model 2 EM Notebook |
| May 3 |
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PDF |
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Meta NLP |
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| May 8 |
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