Notes:

• Schedule is subject to change.
• Slides will be posted throughout the semester.
• Assignments are due by class time (2pm) on their due date.
• DBM refers to Dickinson, Brew and Meurers.
• Readings are given in short format, e.g. DBM 1:1-12 means “DBM Chapter 1, pages 1-12, to be read by class on the date it appears.

Week 1

August 30: Course introduction.

• Slides
• No reading.
  

September 1: Text encoding

• Slides (Markus Dickinson)
• Reading: DBM 1-14
• If you would like to play with the International Phonetic Alphabet, here it is.
• Info on ASCII and Unicode
• Unicode chart with decimal rather than hexadecimal numbers
  
• A tutorial on how to map decimal numbers to binary
  

Week 2

September 4: Labor day.

• Readings: DBM 17-26 (without "under the hood 1" but with "under the hood 2")
• A demo of word n-grams (if you know Python)
  

Week 3

September 11: Forensic linguistics

• Slides (Jason Baldridge)
• There are no book readings, as the book does not cover forensic linguistics. But if you would like to prepare, read the above slides 1-15.
  
• Background readings on forensic linguistics: Not required for class! This is just in case you are curious and want to read more:
  • Jan Svartvik: The Evans statements: a case for forensic linguistics
  • Malcolm Coulthard on Author Identification
  • Language Log: Ben Zimmer on the Unabomber's use of "eat your cake and have it too"
  • Other uses of forensic linguistics:
  • From spam to McDonald's in the trademark wars 
  • Roger Shuy on the DeLorean trial

September 13: Guest lecture: Jessy Li: Forensic linguistics.

• Homework 1 due
• Slides (Jason Baldridge)
• Readings (optional): Slides pages 16-45
  
• Demo of automatic clustering of data points using k-means
  

September 15: Guest lecture: John Beavers: Grammars.

Week 4

September 18: Document classification: What is machine learning? Then we start on sentiment analysis .

• Readings: DBM pp. 127-133
• Slides: machine learning
  
• Slides on sentiment analysis (Jason Baldridge)
• Background reading (optional) on learning:
• Infant language learning is an incredibly tough task
  
• An example of unsupervised learning (learning to detect regularities in data):
  How do infants learn to recognize word boundaries in their native language?
• There has been much additional work on this.
  

September 20: Document classification: More on sentiment analysis. Then: The Naive Bayes classifier.

• Readings: DBM pp. 140-145
• Background reader (optional):
  Lingjia Deng and Janyce Wiebe (2015). Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models. EMNLP 2015. This is the paper that reasoned about different people's interacting opinions.
  

September 22: Document classification: The Naive Bayes classifier.

• Readings: Same as the previous session, DBM pp. 140-145

Week 5

September 25: Document classification: Evaluating a supervised learning system

September 27: Web search: Structured and unstructured data, and how to search through those

• Slides
  
• Readings: DBM pp. 91-107 (minus Under the Hood 6)
• Homework 2 due

September 29: Web search: Page Rank. HTML and XML

• Slides as above
  
• Readings: DBM pp. 103-104: Under the Hood 6

Week 6

October 2: Web search: Regular expressions

• Slides as above
  
• Readings: DBM pp. 107-115 (minus Under the Hood 7)

October 4: Web search: Regular expressions, continued.

    Then we start on Spelling correction.

• Slides as above
  
• We try out regular expressions on regexr

• Homework 3 due
  

October 6: Midterm exam review

Week 7

October 9: Midterm exam

October 11: Spelling correction

• Spelling correction slides (Markus Dickinson)
• Readings: DBM pp. 33-38

October 13: Spelling correction

• Readings DBM pp. 38-49
  

Week 8

October 16: We finish up Spelling correction, then start on cryptography.

• The slides on cryptography that we will use are available on Canvas under "Files".
• Background reading: Slides on cryptography (Jason Baldridge)
• No readings from the book, as that does not cover cryptography

October 18: Cryptography

                    Homework 4 due

October 20: Cryptography

Week 9

October 23: Finishing up Cryptography, then Machine translation

• Readings: DBM ch. 7 pages 181-188
• Slides (Baldridge)
  

October 25: Machine translation: Linguistic phenomena

• Readings: DBM ch. 7 pages 188-200
  

October 27: Machine translation: statistical machine translation

• Slides: Baldridge (pages 4-28, 35-37, 49-50),  Erk (pages 9-12)
  

Week 10

October 30: Machine translation: statistical machine translation continued, then a glimpse at neural machine translation

• Readings: DBM pages 200-203
• Slidesets: We are using Baldridge pp. 52-59, this illustration of word alignment  (pages 28-31)
  

November 1: Machine translation: neural machine translation

• Neural machine translation: We use selected slides from Luong/Cho/Manning, pages 7, 10-14, 23-27 
• Optional background reading: neural networks and deep learning: the chapter with the cheese festival
  

                        Homework 5 due

November 3: Neural machine translation continued

• Optional background reading: a blog post on neural machine translation and whether the model produces an interlingua
• And here is a link to the neural conclusion-drawing model I demonstrated, in case you want to play with it some more: Click on "TE model" in the top row.
  

Week 11

November 6: Grammars

• Readings: DBM chapter 2, pages 49-53
  

November 8: Grammars

• Readings: DBM chapter 2, pages 54-55
  

November 10: Dialogue systems

• Readings: DBM chapter 6, pages 153-159
• Dialogues with award-winning systems: Jabberwacky, and 2017 Loebner prize finalists
• For comparison, here is what Hal could do in the movie
  

Week 12

November 13: Dialogue systems

• Readings: DBM chapter 6, pages 159-177 including "under the hood 10"
• We are using Markus Dickinson's slides
  

November 15: Dialogue systems

• Continuing on Markus Dickinson's slides
• Eliza and Parry examples, and yet another discussion between Eliza and Parry, this time with some actual grammar mistakes by Eliza
  

                            Homework 6 due

November 17: Dialogue systems

• Degrees of understanding: We are using Jason Baldridge's slides, pages 3-7, 10-17, 28-32
  
• Some interesting recent dialogue systems: These are original research papers; we will look mainly at the example output.
  A neural sequence to sequence model, chatbots learning to say "I don't know", and chatbots learning not to mutually tell each other "see you later" for hours
• A restaurant recommendation system
  

Week 13

November 20: Dialogue systems

• What does it mean to understand? We are using Jason Baldridge's slides, pages 37 and following
  

November 22: Thanksgiving break

November 24 Thanksgiving break

Week 14

November 27: Impact of language and technology:

• Overview
• Language technology and jobs
• Dual use
• Readings (after class): 
• Fairness and Accountability in Machine Learning: principles for accountability
  
• Hal Daume on a code of ethics for language technology and machine learning
• DBM chapter 8, pages 215-220
• Dual use: Hovy and Spruit section 4

November 29: Impact of language and technology

• Anthropomorphization
• Readings (before class): Weizenbaum, excerpt from "Computer Power and Human Reason: From Judgment ToCalculation"
• Also refresh your memory of Eliza
• Questions to discuss in class: Language technology and society: ethics of artificial intelligence
  

                        Homework 7 due

December 1: Impact of language and technology

• Privacy and big data
• Readings (before class):
  
• Jennifer Golbeck on "The curly fry conundrum: Why social media "likes" say more than you might think"
• Jason Baldridge on the Facebook Emotional Contagion study

Week 15

December 4: Impact of language and technology

• Finishing up the privacy discussion
• Then: bias and machine learning
• Readings (before class): 
• Hovy and Spruit on exclusion, overgeneralization, and exposure: sections 1-3
• Gender bias amplification (you only need to look at the introduction)
• Jason Baldridge on machine learning and human bias
• Alistair Croll on Big Data as a human rights issue

December 6: Impact of language and technology

• Finishing up the discussion on bias and machine learning
  

December 8: Final exam review

•     Review sheet is on Canvas
  

Week 16

December 11: Finishing up: final exam review

                            Essay due: Social context of language and computers