This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.


David Bamman (, OH: Wed 10am-11am; Thurs 10am-11am (Zoom; Queue).

TAs (
  • Shefali Bhatia (
  • Gautham Koorma (
  • Manav Rathod (
  • Aayushi Sanghi (
  • Tim Schott (
  • Jerry Shan (
  • Texts

    • [SLP3] Dan Jurafsky and James Martin, Speech and Language Processing (3nd ed. draft) [Available here]
    • [G] Yoav Goldberg, Neural Network Methods for Natural Language Processing (2017) [Available for free on campus/VPN here]
    • [PS] James Pustejovsky and Amber Stubbs, Natural Language Annotation for Machine Learning (2012) [Online access available for free through the UC library here]


    (Subject to change.)

    WeekDateTopicReadings Assignments
    11/18Introduction [slides]SLP2 ch 1
    1/20Lexical semantics/static word embeddings [slides]SLP3 ch 6HW1 out (due 1/26)
    21/25Text classification 1: Logistic regression [slides]SLP3 ch 5
    1/27Text classification 2: MLP and CNN [slides]SLP3 ch 7; G ch. 13HW2 out (due 2/2)
    32/1Text classification 3: Attention and transformers [slides]SLP3 ch 9
    2/3Annotation [slides]PS ch. 6HW3 out (due 2/9)
    42/8Language modeling 1 [slides]SLP3 ch 3
    2/10Language modeling 2 [slides]SLP3 ch 7HW4 out (due 2/16); AP0 (due 2/16)
    52/15Language modeling 3: Contextual embeddings [slides]Smith 2020; SLP3 ch 11
    2/17Language modeling 4: Few-shot learning and prompting methods [slides]Liu et al. 2021AP1 out (due 2/23)
    62/22Sequence labeling: POS tagging; HMM [slides]SLP3 ch 8
    2/24Neural sequence labeling [slides]SLP3 ch 9HW5 out (due 3/2)
    73/1Context-free syntax [slides]SLP3 ch 12
    3/3Context-free parsing algorithms [slides]SLP3 ch 13AP2 out (due 3/30)
    83/8Review [slides]
    93/15Dependency parsing 1 [slides]SLP3 ch 14
    3/17Dependency parsing 2 [slides]SLP3 ch 14
    103/22Spring Break
    3/24Spring Break
    113/29Semantic role labeling [slides]SLP3 ch 19
    3/31Wordnet, supersenses and WSD [slides]SLP3 ch 18HW6 out (due 4/6)
    124/5Coreference resolution [slides]SLP3 ch 21
    4/7Social NLP (Lucy Li)Nguyen et al. 2020AP3 out (due 4/13)
    134/12Question answering [slides]SLP3 ch 23
    4/14Text generation [slides]SLP3 ch 24AP4 out (due 4/21)
    144/19Machine translation [slides]SLP3 ch 10
    4/21Information extraction [slides]SLP3 ch 17
    154/26Multimodal NLP [slides]
    4/28Final project presentations


    • — Algorithms: Computer Science 61B
    • — Probability/Statistics: Computer Science 70, Math 55, Statistics 134, Statistics 140 or Data 100
    • — Strong programming skills


    Info 159

    25%Homeworks (HW)
    25%Annotation project (AP)
    10%Weekly quizzes
    20%Midterm exam
    20%NLP subfield survey

    Info 259

    20%Homeworks (HW)
    20%Annotation project (AP)
    10%Weekly quizzes
    20%Midterm exam
         5% Proposal/literature review
         5% Midterm report
         15% Final report
         5% Presentation

    All lectures will be recorded and made available through bCourses; attendance at lectures is not required. Weekly quizzes will test your knowledge of that week's lectures and readings, so be sure to watch the lecture to stay on track.

    Annotation project

    The most exciting applications of NLP haven't been invented yet. While much of this course will give you exposure to the common methods in NLP, you will also carry out an annotation project where you will get exposure to the entire NLP design process for building a classifer for a brand new task. You will decide on a new NLP task — either document classification (one label per document) or sequence labeling (one label per contiguous span) — annotate data to support it (including creating annotation guidelines), measure your inter-annotator agreement rate, and build a classifier to predict those labels using the methods we discuss in class.

    You may use existing NLP tasks (except sentiment analysis), but try to think outside of the box: projects will be rewarded for their creativity and originality in coming up with a task that few people have considered before, and to being able to create a comprehensive set of guidelines that lead to consistent third-party annotations (i.e., not by your team). To give you a sample of similarly creative new NLP tasks, consider the following work: how dogmatic is a forum post?; how respectful are police officers in their interactions at traffic stops?; how suspenseful is a passage from a story?; how much time is passing in it?


  • AP0 (due 2/16; note add/drop deadline is 2/9). Form a project group of exactly 3 people and let us know who's in the group. Either select your group yourself or let us pair you randomly with other teammates.
  • AP1 (due 2/23). Decide on an annotation task, either document classification or sequence labeling. Any natural language is OK. No sentiment analysis! Collect data and tokenize it. All data must be shareable with the public, so no private information, nothing within copyright. Keep privacy and ethics in mind as you are considering potential sources of data.
  • AP2 (due 3/30). Annotate the data, creating at least 1000 labeled examples and a robust set of annotation guidelines that govern the decisions you make. All of the data must be manually annotated by each member of your group. Report your inter-annotator agreement rates. In a separate assignment (AP3, due 4/13), a different group will annotate your same data only using your own annotation guidelines (and calculating their IAA), so make the guidelines comprehensive!
  • AP4 (due 4/21). Build a classifier to automatically predict the labels using the data you've annotated.


    The full details for this project can be found here.

  • NLP subfield survey (Info 159)

    Understanding how to read and synthesize articles in NLP is an important part of carrying out research in this space. To cultivate this skill, your final report will be a 2000-word survey for a specific NLP subfield of your choice (e.g., coreference resolution, question answering, interpretability, narrative generation, etc.), synthesizing at least 25 papers published at ACL, EMNLP, NAACL, EACL, AACL, Transactions of the ACL or Computational Linguistics. This survey should be able to provide a newcomer (such as yourself at the start of the semester) a sense of the current state of the art in that subfield in 2022, the major historical papers that have defined that area, and the different schools of thought within it. The survey should use the ACL 2021 style files for formatting, which are available as an Overleaf template and source templates for LaTeX and Microsoft Word.

    Project (Info 259)

    Info 259 will be capped by a semester-long project (involving one to three students), involving natural language processing -- either focusing on core NLP methods or using NLP in support of an empirical research question. For examples of the former, see papers published at ACL, NAACL and EMNLP; for examples of the latter, see workshops for NLP and Computational Social Science, Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Natural Language Processing Techniques for Educational Applications, Noisy User-Generated Text, and many more.

    The project will be comprised of four components:

    • — Project proposal and literature review. Students will propose the research question to be examined, motivate its rationale as an interesting question worth asking, and assess its potential to contribute new knowledge by situating it within related literature in the scientific community. (2 pages; 5 sources)
    • — Midterm report. By the middle of the course, students should present initial experimental results and establish a validation strategy to be performed at the end of experimentation. (4 pages; 10 sources)
    • — Final report. The final report will include a complete description of work undertaken for the project, including data collection, development of methods, experimental details (complete enough for replication), comparison with past work, and a thorough analysis. Projects will be evaluated according to standards for conference publication—including clarity, originality, soundness, substance, evaluation, meaningful comparison, and impact (of ideas, software, and/or datasets). (6 pages, not including references)
    • — Presentation. At the end of the semester, teams will present their work in a poster session.
    All reports should use the ACL 2021 style files, which are available as an Overleaf template and source templates for LaTeX and Microsoft Word.


    Academic Integrity

    All students will follow the UC Berkeley code of conduct. You may discuss homeworks at a high level with your classmates (if you do, include their names on the submission), but each homework deliverable must be completed independently -- all writing and code must be your own. All quizzes and exams must be completed on your own. If you mention the work of others, you must be clear in citing the appropriate source (For additional information on plagiarism, see here.) This holds for source code as well: if you use others' code (e.g., from StackOverflow), you must cite its source. All homeworks and project deliverables are due at the time and date of the deadline. We have zero tolerance policy for cheating and plagiarism; violations will be referred to the Center for Student Conduct and will likely result in failing the class.


    We'll use Piazza as a platform for asking and answering questions about the course material, including homeworks. Students are encouraged to actively participate on this forum and help others by answering questions that arise (helpful students can see a grade bump across a threshold (e.g., B+ to A-) for this participation. When helping with homework questions, keep the discussion to the high-level concepts; don't post answers to homeworks or quiz/exam questions.

    TA office hours

    TA office hours will be held over Zoom, which enables students to self-organize into breakout rooms to discuss any questions they have in common among themselves, with the TA moving between rooms to answer questions for everyone there. While in the breakout room, keep academic integrity in mind: you may discuss homework questions at a high level with others present, but don't discuss specific answers or share screens with code solutions. Neither the TA office hours or Piazza should be used for pre-grading (asking if a specific answer to a homework or quiz question is correct before the assignment is due).

    Students with Disabilities

    Our goal is to make class a learning environment accessible to all students. If you need disability-related accommodations and have a Letter of Accommodation from the DSP, have emergency medical information you wish to share with me, or need special arrangements in case the building must be evacuated, please inform me immediately. I'm happy to discuss privately after class or at my office.

    Late assignments

    Student have will have a total of three late days to use when turning in homework assignments and quizzes (not group annotation project deliverables or 259 project deliverables); each late day extends the deadline by 24 hours. If all late days have been used up, homeworks/quizzes can be turned in up to 48 hours late for 50% credit; anything submitted after 48 hours late = 0 credit. Each homework and quiz will be due at 11:59pm, and will have a 2-hour grace period for any last-minute submission issues. Late days and incompletes will be assessed immediately following the grace period (at 2:00am sharp). The grace period applies to late days as well (if a homework is due at 11:59pm 1/21, and you use a late day to extend it to 11:59pm 1/22, you may turn it in up to 2:00am 1/23 and still be assessed 1 late day.) Late days are assessed immediately once homeworks or quizzes are submitted late and can't be retroactively changed (if you submit 2 homeworks and 2 quizzes late, for example, you can't decide after the fact which ones to apply your 3 slip days to -- they apply to whichever homeworks or quizzes use them up first).


    Grades for this course will not be curved. Minimum thresholds for letter grades are the following: 93 A, 90 A-, 87 B+, 83 B, 80 B-, 77 C+ 73 C, 70 C-, 67 D+, 63 D, 60 D-, 0 F. Students taking the course P/NP must complete all deliverables and will receive a P if their grade is greater or equal to 70 (C-); Students taking S/U must complete all deliverables and will receive an S if their grade is greater or equal to 80 (B-).


    This course has a midterm exam scheduled for 3/10 (completed during class time, but not in person) and no final exam. We will not be offering alternative midterm exam dates, so if you anticipate a conflict, you should not register for this course.