Current events (2008)
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| Dates | Topic | Readings | Lecture slides | Reminders |
| M 8-25 | Philosophy: the empirical way of thinking about language | Pereira, 2000; Abney, 1996; blog post on the science/engineering question - comments are especially interesting | ||
| W 8-27 | Evaluation, experimentation, and hypothesis testing | |||
| M 9-1 | Labor day (no class) | |||
| W 9-3 | Numerical optimization | Dan Klein's tutorial on Lagrangean multipliers (was this helpful? let me know!) | ||
| M 9-8 | Weighted dynamic programming | Goodman, 1999; McAllester, 2002; Eisner, Goldlust, and Smith, 2005; if you're in love, Shieber, Schabes, and Pereira, 1995 (warning: these papers focus to some extent on parsing algorithms, which won't be covered in this lecture much) | assignment 1 out | |
| W 9-10 | Due: short summary of proposed literature review topic and hyperlinks to 6-8 papers you think are appropriate. Submit via your User page on the wiki, and link to that page from Noah's user page. | |||
| M 9-15 | Stochastic models of sequences: Markov models, hidden Markov models, and related algorithms | Manning and Schütze, 1999 (ch. 9); Smith, 2004 | ||
| W 9-17 | some notes related to forward/backward probabilities | assignment 1 due | ||
| M 9-22 | Log-linear/exponential/maximum entropy models | Log-linear model article draft sent to you by email (sections 1, 2, and 3). | assignment 2 out | |
| W 9-24 | Training log-linear models, maximum entropy | Three tutorials: Adam Berger's tutorial; Smith, 2004, and Ratnaparkhi, 1997. Chen and Rosenfeld 1999; Rosenfeld, Chen, and Zhu, 2000; Della Pietra, Della Pietra, and Lafferty, 1995 | ||
| M 9-29 | Conditional random fields (structured log-linear models) | Lafferty, McCallum, and Pereira, 2001 | assignment 2 due Tuesday 9pm | |
| W 10-1 | Review session with Kevin Gimpel (TA); brief intro to parsing | |||
| M 10-6 | Stochastic and weighted context-free grammars, statistical parsing with CFGs | Johnson, 1998 | This week: literature review progress meetings with instructor. assignment 3 out | |
| W 10-8 | ||||
| M 10-13 | ||||
| W 10-15 | Other discriminative methods for structured data: perceptron, boosting, maximizing the margin, online methods | assignment 3 due | ||
| M 10-20 | ||||
| W 10-22 | Dependency parsing (guest lecture - Dipanjan Das and André Martins) | Due: literature review draft. | ||
| M 10-27 | EM, word clustering, and word alignment (guest lecture - Kevin Gimpel) | assignment 4 out | ||
| W 10-29 | Nonparametric Bayesian methods (guest lecture - Shay Cohen) | |||
| M 11-3 | EM for NL models, contrastive estimation | |||
| W 11-5 | assignment 4 due | |||
| M 11-10 | Variational Bayesian inference and learning for NL models | |||
| W 11-12 | ||||
| M 11-17 | TBD | |||
| W 11-19 | Combining labeled and unlabeled data: Yarowsky algorithms, self-training, co-training, generalized expectation | |||
| M 11-24 | Partially labeled data: weighted finite-state machines and transducers, learning in machine translation | Due: literature review. assignment 5 out | ||
| W 11-26 | Thanksgiving break (no class) | |||
| M 12-1 | Oral presentations | |||
| W 12-3 | Oral presentations | assignment 5 due | ||
| TBD | Final exam |
