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[ http://web.cs.dal.ca/~vlado/csci6509/coursecalendar.html ]
Fall 2025 (Sep23-Dec9) Faculty of Computer Science Dalhousie University |
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# | Date | Title | |
---|---|---|---|
Part I: Introduction | |||
1 | Tu Sep 23 | Course Introduction
Course introduction: logistics, administrivia, references, evaluation, policies, schedule; Introduction to NLP (reading Ch.1 [JM]): natural language and other languages, NLP applications, NLP as a research area, NLP Research Links and NLP Anthology http://aclweb.org/anthology/. Short history of NLP. NLP methodology overview. Levels of NLP. Files: slides, lecture notes. Reading: [JM] Ch.1 | |
Part II: Stream-based Text Processing | |||
2 | Th Sep 25 | Sources of Complexity in NLP, Course Project, Finite Automata Review (start)
Why is NLP generally hard. Ambiguities at different levels of NLP. About Course Project: topics and teams, deliverables, P0, P1, P, R. Part II: Stream-based Text Processing: Deterministic and Non-deterministic Automata. (Reading: Chapter 2 [JM]) Review of Deterministic Finite Automata (DFA) (start). Files: slides, lecture notes, Syllabus (PDF). | |
L1 | Mo Sep 29 | Lab 1: FCS Computing Environment, Perl Tutorial 1
Logging in using CSID, timberlea environment; Introduction to Perl programming language: basic syntax, variables, string literals, subroutines. Files: lab notes, slides. | |
Tu Sep 30 | National Day for Truth and Reconciliation, University closed | ||
3 | Th Oct 2 | Finite Automata Review
Review of Non-deterministic Finite Automata (NFA), and their use in NLP. NFA-to-DFA conversion. Review of regular expressions. Files: slides, lecture notes. Reading: [JM] Ch.2 | |
L2 | Mo Oct 6 | Lab 2: Perl Tutorial 2
Regular expressions in Perl, Perl: basic I/O. Files: lab notes, slides. | |
4 | Tu Oct 7 | Basic NLP with Perl
Introduction to Perl, main Perl features, syntactic elements, program examples. Files: slides, lecture notes. Reading: On timberlea server `man perlretut' and `man perlre', or perlretut and perlre | |
5 | Th Oct 9 | N-grams and Morphology
Regular expressions in Perl and basic text processing; Text processing examples: tokenization, counting letters. Elements of Morphology: reading: Section 3.1 [JM]; morphemes, stems, affixes, tokenization, stemming, lematization, morphological processes. Characters, Words, and N-grams: counting words, Zipf's law. Perl examples with n-gram collection. Files: slides, lecture notes. Reading: Section 3.1 [JM] | A0 out |
Fr Oct 10 | P0 Project Topic Proposal due | P0 due | |
Mo Oct 13 | Thanksgiving Day, University closed | ||
6 | Tu Oct 14 | Text Similarity and Applications
N-gram collection (finished). Elements of Information Retrieval: Vector Space Model. Some interesting links: Lucene, IR book by Manning, Raghavan, and Schutze. Files: slides, lecture notes. Reading: [JM] 23.1 (Information Retrieval), [MS] Ch.15 (Topics in Information Retrieval) | |
7 | Th Oct 16 | Text Classification
IR Evaluation: precision, recall, F-measure, precision-recall curve. Interpolated Precision-Recall curve. Text mining. Text Classification: classifier evaluation precision, recall, and F-measure in classification. Files: slides, lecture notes. | A0 due |
L3 | Fr Oct 17 | Lab 3: Perl Tutorial 3
Note: Lab 3 (Perl Tutorial 3) is provided for reference only. It is not required to be completed. Files: lab notes, slides. | |
L4 | Mo Oct 20 | Lab 4: Git and GitLab Tutorial
Introduction to GitLab and Git; adding and modifying files, setting up SSH key, add, commit, and push commands, checkout; creating branches and working collaboratively, pull, merge, resolving conflicts. Files: lab notes, slides. | |
8 | Tu Oct 21 | Similarity-based Classification Files: slides, lecture notes. | |
Part III: Probabilistic and Machine Learning Approach to NLP | |||
Labs: Python, NLTK, PyTorch | |||
P0 Topics Discussion; Introduction to Probabilistic Modeling | |||
Basic Probabilistic Models | |||
Naive Bayes Model | |||
N-gram Model | |||
N-gram Model Smoothing | |||
POS Tagging and Hidden Markov Model | |||
Inference with HMMs | |||
Efficient Inference for Bayesian Networks and HMMs | |||
Fr Nov 7 | P1 Project Statement due | P1 due | |
Neural Networks and NLP | |||
Deep Learning and NLP | |||
Part IV: Syntactic Processing | |||
Labs: To Be Decided (possibly Prolog) | |||
DCG and PCFG | |||
DCG and PCFG Grammars | |||
Syntax of Natural Languages; CKY Algorithm | |||
CKY Algorithm and PCFGs | |||
Part V: Student Presentations | |||
Student Presentations | |||
We Dec 10 | Classes end, Report due | Report due | |
Final Exam | |||
?? Dec ? | Final Exam (TBA)
Final exam, 3 hours; date, time, and location to be announced. Exam period: Dec 11 to Dec 21 (3 hour final exam); Exams schedule URL: http://www.dal.ca/academics/exam_schedule/halifax_campus_exam_schedule.html | F.Exam |