School of Computing and Information Systems The University of Melbourne COMP90042 NATURAL LANGUAGE PROCESSING (Semester 1, 2020) Workshop exercises: Week 4 Discussion 1. What is a POS tag? (a) What are some common approaches to POS tagging? What aspects of the data might allow us to predict POS tags systematically? (b) POS tag (by hand) […]
Part of speech tagging COMP90042 Natural Language Processing Lecture 5 COPYRIGHT 2020, THE UNIVERSITY OF MELBOURNE 1 COMP90042 L5 • • • 2 assignments (down from 3) 20% of subject (no change) 1st assignment will be released in week 4 Assignments 2 COMP90042 L5 • • Online workshops available till week 12 Workshop slides by
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N j=1 Sequence Tag åπ =1 j Hidden Markov Models COMP90042 The Markov Chain ging: åa=1; 1≤i≤N ij N j=1 The Markov chain described above is also called the observab cause the output of the process is the set of states at each time instan corresponds to an observable event Xi . In other
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Computational Linguistics CSC 485 Summer 2020 7 7. Statistical parsing Gerald Penn Department of Computer Science, University of Toronto Reading: Jurafsky & Martin: 5.2–5.5.2, 5.6, 12.4, 14.0–1, 14.3–4, 14.6–7. Bird et al: 8.6. Copyright © 2017 Suzanne Stevenson, Graeme Hirst and Gerald Penn. All rights reserved. Statistical parsing 1 • General idea: • Assign probabilities
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A Primer on Neural Network Models for Natural Language Processing Yoav Goldberg Draft as of October 5, 2015. The most up-to-date version of this manuscript is available at http://www.cs.biu. ac.il/ ̃yogo/nnlp.pdf. Major updates will be published on arxiv periodically. I welcome any comments you may have regarding the content and presentation. If you spot a
Natural Language Processing Jacob Eisenstein October 15, 2018 Contents Contents 1 Preface i Background ………………………………. i Howtousethisbook………………………….. ii 1 Introduction 1 1.1 Naturallanguageprocessinganditsneighbors . . . . . . . . . . . . . . . . . 1 1.2 Threethemesinnaturallanguageprocessing ……………… 6 1.2.1 1.2.2 1.2.3 I Learning Learningandknowledge ……………………. 6 Searchandlearning ……………………….
Formal Language Theory & Finite State Automata COMP90042 Natural Language Processing Lecture 13 COPYRIGHT 2020, THE UNIVERSITY OF MELBOURNE 1 COMP90042 L13 What is a Language? • Methodstoprocesssequenceofsymbols: ‣ Language Model ‣ Hidden Markov Model ‣ Recurrent Neural Networks • Nothingisfundamentallylinguisticaboutthese models 2 COMP90042 L13 Formal Language Theory • Alanguage=setofstrings • Astring=sequenceofelementsfromafinite alphabet 3
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Subject Review COMP90042 Natural Language Processing Lecture 22 COPYRIGHT 2020, THE UNIVERSITY OF MELBOURNE 1 COMP90042 L22 • • • Sentence segmentation Tokenisation ‣ Subword tokenisation Word normalisation ‣ Derivational vs. inflectional morphology ‣ Lemmatisation vs. stemming Stop words • Preprocessing 2 COMP90042 L22 N-gram Language Models • Derivation • Smoothingtechniques ‣ Add-k ‣ Absolute
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School of Computing and Information Systems The University of Melbourne COMP90042 NATURAL LANGUAGE PROCESSING (Semester 1, 2020) Workshop exercises: Week 9 Discussion 1. What differentiates probabilistic CYK parsing from CYK parsing? Why is this important? How does this affect the algorithms used for parsing? 2. What is a probabilistic context-free grammar and what problem does
Course Overview & Introduction COMP90042 Natural Language Processing Lecture 1 COPYRIGHT 2020, THE UNIVERSITY OF MELBOURNE 1 COMP90042 L1 Prerequisites • COMP90049“IntroductiontoMachineLearning”or COMP30027 “Machine Learning” ‣ Modules → Welcome → Machine Learning Readings • Pythonprogrammingexperience • Noknowledgeoflinguisticsoradvancedmathematicsis assumed • Caveats–Not“vanilla”computerscience ‣ Involves some basic linguistics, e.g., syntax and morphology ‣ Requires maths, e.g., algebra, optimisation,