Natural Language Processing Prof. Jason Eisner Course # 601.465/665 — Fall 2018 Announcements | Course Information | Schedule

Announcements

• 11/19/18 HW7 is available at 25 pages. Long homework! It walks you through a series of exercises, holding your hand along the way. You may work in pairs. It is due on Friday, December 7, at 11:59pm (as late as we could make it without cutting into reading period).
• 10/29/18 HW6 is available, with a separate "reading handout" appended to it. It is due on Thursday, November 15 at 9pm. This isn't conceptually hard if you understood the HMM lectures. But there are some finicky details ... it's a programming assignment with a long pair of handouts, so start as soon as possible!
• 10/13/18 HW5 is available, with a short "reading handout" appended to it. It deals with attaching semantic λ-expressions to grammar rules. It is due on Friday, 11/2, at 2pm.
• 10/12/18 Midterm will be held on Wed 10/17, in our usual classroom, from 3-4:30.
• 10/5/18 HW4 is available, with a separate "reading handout" appended to it. The reading might also help you study parsing for the midterm. This is the conceptually hardest assignment in the course, with two major challenges: probabilistic Earley parsing, and making parsing efficient. It is due on Wednesday, October 24 at 2pm. You may work with a partner on this one.
• 9/25/18 The log-linear quiz associated with HW2 has been rescheduled to Monday 10/1 (in class).
• 9/24/18 HW3 is now available, with a separate "reading handout" appended to it. The due date is Friday, 10/5, at 2pm. Start early: This is a long and detailed assignment that requires you to write some annoying programs for smoothing and experiment with their parameters and design to see what happens. I strongly suggest that you start going through the reading handout now, and then spread the work out. You may work in pairs.
• 9/12/18 HW2 (9 pages + extra credit) is available. It's due in 2 weeks, Wednesday 9/26 at 2pm. This assignment is mostly a problem set about manipulating probabilities. But it is a long assignment! Most significantly, question 6 asks you to read a separate handout and to work through a series of online lessons, preferably with a partner or two. Question 8 asks you to write a small program. It is okay to work on questions 6 and 8 out of order.
• 8/31/18 HW1 (11 pages) is available. It is due on Friday, 9/17 at 2pm: please get this one in on time so we can discuss it in class an hour later.
• 8/30/18 Please bookmark this page (http://cs.jhu.edu/~jason/465).
  • Start of class is Thursday 8/30, 3pm.
  • As explained below, please keep MWF 3-4:30 open to accommodate a variable class schedule as well as office hours after class.
  • The main classroom is Olin 305. Olin Hall is northwest of Hodson, across San Martin Drive (map).
  • We will have our weekly problem discussion sessions Tuesdays 6-7:30pm in Shaffer 101.

Vital Statistics ^[1]

Course catalog entry: This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers?

The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as tagging and morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition, and machine translation. There are a number of structured but challenging programming assignments. Prerequisite: 601.226 or equivalent. [Applications, 4 credits]

Course objectives: Welcome! This course is designed to introduce you to some of the problems and solutions of NLP, and their relation to linguistics and statistics. You need to know how to program (e.g., 601.120) and use common data structures (601.226). It might also be nice—though it's not required—to have some previous familiarity with automata (600.271) and probabilities (601.475/675, 553.420/620, or 553.310/311). At the end you should agree (I hope!) that language is subtle and interesting, feel some ownership over some of NLP's formal and statistical techniques, and be able to understand research papers in the field.

Lectures:	MWF 3-4 or 3-4:15, Olin 305.
Recitations:	Tue 6-7:30, Shaffer 101.
Prof:	Jason Eisner - ()
TAs:	Arya McCarthy, Sebastian Mielke -
CAs:	Lisa Li, Shijia Liu, Molly Pan -
Office hrs:	Prof: After class until 4:30; or by appt in Hackerman 324C TAs/CAs: Schedule
Discussion site:	https://piazza.com/class/jlgolfygqqg1rs ... public questions, discussion, announcements
Web page:	http://cs.jhu.edu/~jason/465
Textbook:	Jurafsky & Martin, 2nd ed. (semi-required - P98.J87 2009 in Science Ref section on C-Level) Roark & Sproat (recommended - P98.R63 2007 in same section) Manning & Schütze (recommended - free online PDF version here!)
Policies:	Grading: homework 50%, participation 5%, midterm 15%, final 30% Submission: TBA Lateness: floating late days policy Honesty: CS integrity code, JHU undergraduate policies, JHU graduate policies Intellectual engagement: much encouraged Disabilities: If you need accommodations for a disability, obtain a letter from Student Disability Services, 385 Garland, (410) 516-4720. Announcements: Read mailing list and this page!

[1] Or are these statistics? What are "statistics" anyway? How are they different from "information"?

Schedule

This class is in the "flexible time slot" MWF 3-4:30. Please keep the entire slot open. Class will usually run 3-4, followed by office hours in the classroom from 4-4:30 (stick around to get your money's worth). However, class will sometimes run till 4:15 in order to keep up with the syllabus. I'll try to give advance notice of these "long classes," which among other things make up for no-class days when I'm out of town.

We also run a once-per-week recitation led by the prof or the TA. This session will focus on solving problems together. That's meant as an efficient and cooperative way to study for an hour: it reinforces the past week's class material without adding to your homework load. Also, if you come to discussion session as recommended, you won't be startled by the exam style — the discussion problems are taken from past exams and are generally interesting.

Warning: The schedule below may change. Links to future lectures and assignments may also change (they currently point to last year's versions).

Warning: Use the PPT slides if possible. The PDF export versions don't have animations and they may be out of date relative to the PPT files (although I do try to update them before exams).

Old Materials

• Optimal paths in graphs: A Dyna perspective
• Tree-adjoining grammars (by Darcey Riley)
• Grouping words (ppt): Semantic clustering
  • Clustering spreadsheet
• Splitting words (ppt): Word sense disambiguation and the Yarowsky algorithm
• Words vs. terms in IR (ppt): Collocation discovery and Latent Semantic Indexing
• Text categorization (ppt)
• Knowledge extraction and dialogue systems (ppt)
• Machine translation:
  • Competitive linking (ppt): A simple bitext alignment algorithm
  • IBM Model 1 (guest lecture)
  • Phrase-based models and decoding (guest lecture)
• Applied NLP tasks:
  • named entity recognition (ppt) (guest lecture)
  • entity disambiguation and linking, question answering, sentiment analysis, dependency parsing, semantic role labeling (ppt) (guest lecture)

• The XFST finite-state toolkit; Porter stemming

ABET Outcomes

Course Outcomes

• Show sensitivity to linguistic phenomena and an ability to model them with formal grammars. [program outcomes (a),(c*),(i),(j)]
• Understand and carry out proper experimental methodology for training and evaluating empirical NLP systems. [program outcomes (b),(c),(e*)]
• Be able to manipulate probabilities, construct statistical models over strings and trees, and estimate parameters using supervised and unsupervised training methods. [program outcomes (a),(i),(j*)]
• Be able to design, implement, and analyze NLP algorithms. [program outcomes (a),(c),(d),(i*),(j)]

Program Outcomes

Last Modification $Date: 2018/10/29 13:45:59 $ (GMT)