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Computational Text Analysis
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Table of contents
“Computational Text Analysis” (PGSP11584)
Course Overview
Introduction to R
1
Week 1: Retrieving and analyzing text
2
Week 2: Tokenization and word frequencies
3
Week 2 Demo
4
Week 3: Dictionary-based techniques
5
Week 3 Demo
6
Week 4: Natural language, complexity, and similarity
7
Week 4 Demo
8
Week 5: Scaling techniques
9
Week 5 Demo
10
Week 6: Unsupervised learning (topic models)
11
Week 6 Demo
12
Week 7: Unsupervised learning (word embedding)
13
Week 7 Demo
14
Week 8: Sampling text information
15
Week 9: Supervised learning
16
Week 10: Validation
17
Exercise 1: Word frequency analysis
18
Exercise 2: Dictionary-based methods
19
Exercise 3: Comparison and complexity
20
Exercise 4: Scaling techniques
21
Exercise 5: Unsupervised learning (topic models)
22
Exercise 6: Unsupervised learning (word embedding)
23
Exercise 7: Sampling text information
24
Exercise 9: Validation
25
Assessment data
26
References
View book source
15
Week 9: Supervised learning
Required reading
:
Hopkins and King (
2010
)
King, Pan, et al. (
2017
)
Siegel et al. (
2021
)
Yu et al. (
2008
)
Manning et al. (
2007
, chs. 13,14, and 15)
:
https://nlp.stanford.edu/IR-book/information-retrieval-book.html
]
Further reading
:
Denny and Spirling (
2018
)
King, Lam, et al. (
2017
)
Slides
:
Week 9
Slides
14
Week 8: Sampling text information
16
Week 10: Validation
On this page
15
Week 9: Supervised learning
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