Computational Psycholinguistics

Over the last two and a half decades, computational linguistics has been revolutionized as a result of three closely related developments: increases in computing power, the advent of large linguistic datasets, and a paradigm shift toward probabilistic modeling. At the same time, similar theoretical developments in cognitive science have led to a view major aspects of human cognition as instances of rational statistical inference. These developments have set the stage for renewed interest in computational approaches to human language use. Correspondingly, this course covers some of the most exciting developments in computational psycholinguistics over the past decade. The course spans human language comprehension, production, and acquisition, and covers key phenomena from both phonetics and syntax. Students will learn key technical tools including probabilistic models, formal grammars, and decision theory, and how theory, computational modeling, and data can be combined to advance our fundamental understanding of human language use.

We'll meet twice a week; the course format will be a combination of lecture, discussion, and in-class exercises as class size, structure, and interests permit.

Undergraduates in Brain & Cognitive Sciences, Linguistics, Electrical Engineering & Computer Science, and any of a number of related disciplines. Graduate students are also welcome to take the course (contact the instructor if you are interested in taking this course as part of the BCS PhD program requirements; it's possible). Postdocs and faculty are also welcome to participate!

The course prerequisites are one semester of Python programming (fulfilled by 6.00), plus either one semester of probability/statistics/machine learning (fulfilled by, for example, 6.041B or 9.40) or one semester of introductory linguistics (fulfilled by 24.900). If you think you have the requisite background but have not taken the specific courses just mentioned, please talk to the instructor to work out whether you should take this course or do other prerequisites first.

We will be doing some Python programming in this course, and also using programs that must be run from the Unix/Linux/OS X command line.

Readings will frequently be drawn from the following textbooks:

1. Daniel Jurafsky and James H. Martin. Speech and Language Processing. Third edition (draft). Draft chapters can be found here. (I refer to this book as "SLP" in the syllabus.)

   This textbook is the single most comprehensive and up-to-date introduction available to the field of computational linguistics.

2. Bird, Steven, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python. O'Reilly Media. (I refer to this book as "NLTK" in the syllabus.)

   This is the book for the Natural Language Toolkit (or NLTK), which we will be using extensively to do programming We will also be doing some of our programming in the Python programming language, and will make quite a bit of use of for Python. You can buy this book, or you can freely access it on the Web at http://www.nltk.org/book.

3. Christopher D. Manning and Hinrich Schütze. (1999). Foundations of statistical natural language processing. Cambridge: MIT press. Book chapter PDFs can be obtained through the MIT library website. (I refer to this book as "M&S" in the syllabus.)

   This is an older but still very useful book on NLP.

4. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press. Book chapter PDFs can be obtained through the MIT library website. (I refer to this book as "MRS" in the syllabus.)

We'll also occasionally draw upon other sources for readings, and sometimes read original papers.

You'll be graded on:

• A number of homework assignments throughout the semester (50% of grade)
• A midterm and a final (50% of grade)
• In the case of grad students, a final project

Active participation in the class is also encouraged and taken into account in borderline grade cases!

There will be a mailing list for this course, which you can access at https://mailman.mit.edu:444/mailman/listinfo/9.19-2017-spring. Please make sure you're signed up for it! This list is both for discussion of ideas in the class and for communications about organizational issues.