6.864: Natural Language Processing

Gilbert Street
[main] | [about] | [homeworks] | [links]



Instructors: Regina Barzilay, Michael Collins,
Time & Location: Tues & Thurs 1-2.30, 32-155
Office Hours: By appointment

Announcements:
  • Date Topic References
    9/7 Introduction and Overview
    9/12 Parsing and Syntax I
    9/14 Smoothed Estimation, and Language Modeling
    • Note: the two "background reading" papers are provided because we think they may be useful and/or interesting to you. We don't expect you to necessarily understand the material in the background readings -- the lecture slides contain all material that is required for the class. The first reading (Chen and Goodman) gives a very comprehensive overview of different smoothing techniques. The second reading (McAllester and Schapire) gives proofs regarding the Good-Turing estimators.
    9/19 Parsing and Syntax II
    9/21 Parsing and Syntax III
    • Background Reading: Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency. Dan Klein and Chris Manning. 2004. In Proceedings of the 42nd Annual Meeting of the ACL, 2004. Some people in class expressed an interest in unsupervised learning of parsing models (i.e., learning without a treebank). This paper covers that topic. Again, as background reading you're not expected to know this material, it's posted in case you find it of interest/relevance.
    9/26 Log-Linear Models
    9/28 Tagging
    10/3 Distributional Similarity and Clustering
    • Scott Miller, Jethran Guinness and Alex Zamanian Name Tagging with Word Clusters and Discriminative Training, In proceedings of NAACL/HLT 2004.
    10/5 Unsupervised Vocabulary Induction
    10/12 Word Sense Disambiguation
    10/17 History-Based Models, and Global Linear Models
    10/19 History-Based Models, and Global Linear Models
    • Continued the slides from last lecture
    10/24 Global Linear Models Part II
    10/26 Text Segmentation
    10/31 Graph-Based Approaches
    11/7 Summarization and Generation: Content Selection
    11/9 Summarization and Generation: Aggregation and Ordering
    11/14 The EM Algorithm (Part I)
    11/16 The EM Algorithm (Part II)
    11/21 Dialogue Systems
    11/23 Machine Translation (Part I)
    11/28 Machine Translation (Part II)