Computational Linguistics CSC 485 Summer 2020 10A 10A. Log-Likelihood Dependency Parsing Gerald Penn Department of Computer Science, University of Toronto Based on slides by Yuji Matsumoto, Dragomir Radev, David Smith, Sam Thomson and Jason Eisner Copyright © 2020 Gerald Penn. All rights reserved. How about structured outputs? Log-linear models great for n-way classification […]
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COMP9414: Artificial Intelligence Tutorial Week 8: Language Processing 1. Trace the bottom up chart parsing algorithm using the following grammar and lexicon to parse the sentence ¡°0 This 1 is 2 the 3 house 4 that 5 Jack 6 built 7 ¡±. Number the rules and lexical entries for convenience. 1. S¡úNPVP 2. NP ¡ú
Sections 8.1-8.2 Week 5 Ali Mousavidehshikh Department of Mathematics University of Toronto Ali Mousavidehshikh Week 5 Outline 1 Sections 8.1-8.2 Sections 8.1-8.2 Week 5 Ali Mousavidehshikh Sections 8.1-8.2 Two vectors u and v in Rn are said to be orthogonal if and only if u · v = 0. Week 5 Ali Mousavidehshikh Sections 8.1-8.2
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Instructions Fundamentals of Computer Vision Project 3 Tracking Objects in Videos 1. Integrity and collaboration: Students are encouraged to work in groups but each student must submit their own work. If you work as a group, include the names of your collaborators in your write-up. Code should NOT be shared or copied. Please DO NOT
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Computational Linguistics CSC 485 Summer 2020 5B 5b. Resolution of ambiguity Gerald Penn Department of Computer Science, University of Toronto Copyright © 2017 Suzanne Stevenson, Graeme Hirst and Gerald Penn. All rights reserved. Ambiguity resolution • Problem of chart parsing: Structural ambiguity: Time flies like an arrow. … paint the office in the building near
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Algorithms Tutorial Problems 3 Greedy Strategy Solutions 1. There are N robbers who have stolen N items. You would like to distribute the items among the robbers (one item per robber). You know the precise value of each item. Each robber has a particular range of values they would like their item to be worth
COMP3121/9101/3821/9801 Lecture Notes More on Dynamic Programming (DP) LiC: Aleks Ignjatovic THE UNIVERSITY OF NEW SOUTH WALES School of Computer Science and Engineering The University of New South Wales Sydney 2052, Australia 1 Turtle Tower You are given n turtles, and for each turtle you are given its weight and its strength. The strength of
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Static Assurance Phantom Types GADTs Type Families 1 Software System Design and Implementation Static Assurance with Types Liam O’Connor University of Edinburgh LFCS (and UNSW) Term 2 2020 Static Assurance Phantom Types GADTs Type Families Methods of Assurance Static Analysers Proofs Static Types Model Checkers Monitors, watchdogs Gradual Types Hybrid Contracts assert() Dynamic Testing 2
Tree Learning COMP9417 Machine Learning and Data Mining Term 2, 2020 COMP9417 ML & DM Tree Learning Term 2, 2020 1 / 100 Acknowledgements Material derived from slides for the book “Machine Learning” by T. Mitchell McGraw-Hill (1997) http://www-2.cs.cmu.edu/~tom/mlbook.html Material derived from slides by Andrew W. Moore http:www.cs.cmu.edu/~awm/tutorials Material derived from slides by Eibe Frank
Text Classification in scikit-learn¶ First, let’s get the corpus we will be using, which is included in NLTK. You will need NLTK and Scikit-learn (as well as their dependencies, in particular scipy and numpy) to run this code. In [1]: import nltk nltk.download(“reuters”) # if necessary from nltk.corpus import reuters [nltk_data] Downloading package reuters to /Users/jason/nltk_data…
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