(no class - introductory week)

(no class - introductory week)

Introduction

Crash course in Python and NLTK

• NLTK, Install Python 3 and NLTK 3 and play around with it

Generative statistical models; n-grams

• Jurafsky & Martin, Chapters 4.1-4.2
• * Kevin Murphy, Binomial and multinomial distributions
• * Jurafsky & Martin (Smoothing), Chapters 4.5-4.7
• * Manning & Schütze (Smoothing), Chapters 6.2-6.3

More Python

POS tagging with HMMs

• Jurafsky & Martin, Chapters 5.1-5.5, 6.1-6.4

HMM training

• Jurafsky & Martin, Chapters 5.7-5.8, 6.5
• * Jason Eisner, HMM-in-a-spreadsheet

Discussion of Assignment 1

Context-free grammars

• Jurafsky & Martin, Chapters 12.1-12.3, 12.5, 13.1-13.3
• * Stuart Shieber, Yves Schabes, and Fernando Pereira (1993), Principles and implementation of deductive parsing
• * Shieber (1985), Evidence against the context-freeness of natural language

Complexity of algorithms and the CKY parser

• Jurafsky & Martin, Chapter 12.4
• Khan Academy, Asymptotic notation

Probabilistic CFGs

• Jurafsky & Martin, Chapter 14.1-14.5, 14.7

Discussion of Assignment 2

Training PCFGs

• Jurafsky & Martin, Chapter 14.3
• Manning & Schütze, Chapter 11.3-11.4
• Michael Collins, Notes on the inside-outside algorithm
• * Glenn Carroll & Eugene Charniak (1992), Two experiments on learning probabilistic dependency grammars from corpora
• * Fernando Pereira & Yves Schabes (1992), Inside-outside reestimation from partially bracketed corpora

More accurate PCFG parsing

• Mark Johnson (1998), PCFG Models of Linguistic Tree Representations (esp. on parent annotations)
• Michael Collins, Lexicalized PCFGs
• Dan Klein & Chris Manning (2003), Accurate unlexicalized parsing
• * Michael Collins (1997), Three Generative, Lexicalized Models for Statistical Parsing
• * David Magerman (1995), Description of head percolation tables
• * Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein (2006), Learning accurate, compact, and interpretable tree annotation
• William Morgan, Statistical Hypothesis Tests for NLP (explains approximate randomization)
• Berg-Kirkpatrick et al. (2012), An empirical investigation of statistical significance for NLP (explains bootstrap testing)

Advanced PCFG parsing algorithms

• Joshua Goodman (1999), Semiring parsing (not Section 4, 6; ignore Earley parser if you like)
• Charniak et al. (2006), Multilevel coarse-to-fine PCFG parsing
• Dan Klein & Chris Manning (2003), A* Parsing - Fast exact Viterbi parse selection
• * Stuart Shieber, Yves Schabes, and Fernando Pereira (1993), Principles and implementation of deductive parsing
• * Eugene Charniak, Sharon Goldwater, and Mark Johnson (1998), Edge-based best-first chart parsing
• * Liang Huang and David Chiang (2005), Better k-best parsing
• * Michael Collins and Terry Koo (2005), Discriminative reranking for natural language parsing
• * Eugene Charniak and Mark Johnson (2005), Coarse-to-fine n-best parsing and MaxEnt discriminative reranking

Discussion of Assignment 3

Dependency parsing

• Ryan McDonald, Fernando Pereira, Kiril Ribarov, Jan Hajic (2005), Non-projective Dependency Parsing using Spanning Tree Algorithms
• Joakim Nivre (2008), Algorithms for Deterministic Incremental Dependency Parsing
• * Joakim Nivre, Ryan McDonald (2007), Characterizing the Errors of Data-Driven Dependency Parsing Models
• * Jason Eisner (1996), Three New Probabilistic Models for Dependency Parsing -- An Exploration
• * Eliyahu Kiperwasser and Yoav Goldberg (2016), Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
• * Google, SyntaxNet

Statistical machine translation - Alignments

• J & M, Chapter 25.1-25.6
• Lopez, Post, Callison-Burch, JHU 600.468 Machine Translation - Homework 1
• Adam Lopez, Word Alignment and the Expectation-Maximization Algorithm
• * Michael Collins, IBM Models 1 and 2
• * Dyer et al., fast_align
• * Liang et al., Berkeley aligner
• * Gao, MGIZA

Statistical machine translation - Translation

• J & M, rest of Chapter 25
• David Chiang (2007), Hierarchical phrase-based translation

(Christmas break)

(Christmas break)

(Christmas break)

(Christmas break)

Discussion of Assignment 4

More expressive grammar formalisms

• Joshi & Schabes (1997), Tree-adjoining grammars
• Steedman & Baldridge (2011), Combinatory categorial grammar
• * Shieber (1985), Evidence against the context-freeness of natural language
• * Vijay-Shanker and Weir (1994), The equivalence of four extensions of context-free grammar
• * Kuhlmann et al. (2015), Lexicalization and generative power in CCG
• * Lewis et al. (2016), LSTM CCG Parsing

Bayesian methods - LDA

• David Blei (2012), Probabilistic topic models
• Thomas Griffiths & Mark Steyvers (2004), Finding scientific topics
• Mark Steyvers & Thomas Griffiths (2007), Probabilistic topic models (Sections 1-4)
• * William M. Darling (2011), A theoretical and practical implementation tutorial on topic modeling and Gibbs sampling
• * Bob Carpenter (2010), Integrating out multinomial parameters in LDA and Naive Bayes for Collapsed Gibbs Sampling
• * Allison Chaney, Visualizing topic models on Wikipedia

Bayesian methods - Grammar induction

• Kevin Knight (2009), Bayesian Inference with Tears
• Trevor Cohn, Phil Blunsom, and Sharon Goldwater (2010), Inducing tree-substitution grammars (up to Section 4.1; have a quick look at Sections 5-6)
• * Hiroyuki Shindo, Yusuke Miyao, Akinori Fujino, and Masaaki Nagata (2012), Bayesian symbol-refined tree substitution grammars for syntactic parsing

Discussion of Assignment 5

Semantic parsing

• Luke Zettlemoyer and Michael Collins (2005), Learning to Map Sentences to Logical Form. Structured Classification with Probabilistic Categorial Grammars
• Yuk Wah Wong and Raymond J. Mooney (2006), Learning for Semantic Parsing with Statistical Machine Translation
• * Flanigan et al. (2014), A Discriminative Graph-Based Parser for the Abstract Meaning Representation
• * Groschwitz et al. (2018), AMR Dependency Parsing with a Typed Semantic Algebra

(AK away)

Lexical semantics

• Jurafsky & Martin, Chapters 19.1-19.3, 20.6-20.8
• * Mikolov et al. (2013), Efficient estimation of word representations in vector space
• * Jeff Mitchell & Mirella Lapata (2008), Vector-based models of semantic composition
• * Marco Baroni & Roberto Zamparelli (2010), Nouns are vectors, adjectives are matrices
• * Yehoshua Bar-Hillel (1960), A demonstration of the nonfeasibility of fully automatic high quality translation
• * Fellbaum et al., Wordnet website
• * Peters et al. (2018), Deep contextualized word representations

Discussion of Assignment 6

Discussion of final projects