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CS 447 - Natural Language Processing

Spring 2021

Official Description

Part-of-speech tagging, parsing, semantic analysis and machine translation. Relevant linguistics concepts from morphology (word formation) and lexical semantics (the meaning of words) to syntax (sentence structure) and compositional semantics (the meaning of sentences). Course Information: 3 undergraduate hours. 3 or 4 graduate hours. Credit is not given for both CS 447 and LING 406. Prerequisite: CS 374.

Related Faculty

Learning Goals

1. Be able to describe key concepts, models and challenges in Natural Language Processing
(this includes linguistic concepts such as POS tags, morphemes, phrase structure trees, dependency trees, various grammar formalisms, computational models such as recurrent and convolutional neural nets, HMMs, PCFGs, IBM models for machine translation; challenges include Zipf's law; lexical, syntactic, semantic, referential ambiguity) (1), (3)

2. Be able to describe, implement, and apply a variety of fundamental algorithms in Natural Language Processing (1), (2), (3)
(e.g. HMMs, CKY parsers, IBM alignment models)

3. Be able to describe and evaluate more complex software systems for various Natural Language Processing tasks (1), (2), (3), (6)

4. Be able to describe current approaches, datasets and systems for various Natural Language Processing tasks (1), (2), (3), (6)

Topic List

Morphological Analysis

POS tagging

Sequence labeling

Syntactic Parsing

Semantic Parsing

Machine Translation




CS446 (Machine Learning) and CS440 (Artificial Intelligence)

Required, Elective, or Selected Elective


Natural Language ProcessingNL373128OLC3 -    Julia Constanze Hockenmaier
Natural Language ProcessingNL473129OLC4 -    Julia Constanze Hockenmaier