Lecture 9: syntactic parsing (2/2); intro to computational semantics (1/2)

The Earley algorithm. Probabilistic CFGs (PCFGs). PCFGs for disambiguation: the probabilistic CKY algorithm. PCFGs for language modeling. Introduction to computational semantics. Syntax-driven semantic analysis. Semantic attachments. First-Order Logic. Lambda notation and lambda calculus for semantic representation. Lexicon, lemmas and word forms. Word senses: monosemy vs. polysemy. Special kinds of polysemy. Computational sense representations: enumeration vs. generation. Graded word sense assignment.

Lecture 8: syntactic parsing

Introduction to syntax. Context-free grammars and languages. Treebanks. Normal forms. Dependency grammars. Syntactic parsing: top-down and bottom-up. Structural ambiguity. Backtracking vs. dynamic programming for parsing. The CKY algorithm. Neural transition-based dependency parsing.

Lecture 7: part-of-speech tagging

Introduction to part-of-speech (POS) tagging. POS tagsets: the Penn Treebank tagset and the Google Universal Tagset. Rule-based POS tagging. Stochastic part-of-speech tagging. Hidden markov models. Deleted interpolation. Linear and logistic regression: Maximum Entropy models. Transformation-based POS tagging. Handling out-of-vocabulary words. The Stanford POS tagger. Neural POS tagging with bidirection LSTMs. Presentation of homework 2.