Proceedings of the. Eighth Conference on Computational Natural Language Learning

Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling

Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling Xavier Carreras and Lluís Màrquez TALP Research Center Technical University of Catalonia Boston, May 7th, 2004 Outline Outline of the

Semantic Role Labeling via Tree Kernel Joint Inference

Semantic Role Labeling via Tree Kernel Joint Inference Alessandro Moschitti, Daniele Pighin and Roberto Basili Department of Computer Science University of Rome Tor Vergata 00133 Rome, Italy {moschitti,basili}@info.uniroma2.it

Fast Computing Grammar-driven Convolution Tree Kernel for Semantic Role Labeling

Fast Computing Grammar-driven Convolution Tree Kernel for Semantic Role Labeling Wanxiang Che 1, Min Zhang 2, Ai Ti Aw 2, Chew Lim Tan 3, Ting Liu 1, Sheng Li 1 1 School of Computer Science and Technology

Driving Semantic Parsing from the World s Response

Driving Semantic Parsing from the World s Response James Clarke, Dan Goldwasser, Ming-Wei Chang, Dan Roth Cognitive Computation Group University of Illinois at Urbana-Champaign CoNLL 2010 Clarke, Goldwasser,

Chunking with Support Vector Machines

NAACL2001 Chunking with Support Vector Machines Graduate School of Information Science, Nara Institute of Science and Technology, JAPAN Taku Kudo, Yuji Matsumoto {taku-ku,matsu}@is.aist-nara.ac.jp Chunking

NAACL HLT Spatial Language Understanding (SpLU-2018) Proceedings of the First International Workshop

NAACL HLT 2018 Spatial Language Understanding (SpLU-2018) Proceedings of the First International Workshop June 6, 2018 New Orleans, Louisiana c 2018 The Association for Computational Linguistics Order

The Research on Syntactic Features in Semantic Role Labeling

23 6 2009 11 J OU RNAL OF CH IN ESE IN FORMA TION PROCESSIN G Vol. 23, No. 6 Nov., 2009 : 100320077 (2009) 0620011208,,,, (, 215006) :,,,( NULL ),,,; CoNLL22005 Shared Task WSJ 77. 54 %78. 75 %F1, : ;;;;

Learning and Inference over Constrained Output

Learning and Inference over Constrained Output Vasin Punyakanok Dan Roth Wen-tau Yih Dav Zimak Department of Computer Science University of Illinois at Urbana-Champaign {punyakan, danr, yih, davzimak}@uiuc.edu

arxiv: v2 [cs.cl] 20 Apr 2017

Syntax Aware LSM Model for Chinese Semantic Role Labeling Feng Qian 2, Lei Sha 1, Baobao Chang 1, Lu-chen Liu 2, Ming Zhang 2 1 Key Laboratory of Computational Linguistics, Ministry of Education, Peking

Tuning as Linear Regression

Tuning as Linear Regression Marzieh Bazrafshan, Tagyoung Chung and Daniel Gildea Department of Computer Science University of Rochester Rochester, NY 14627 Abstract We propose a tuning method for statistical

Machine Learning for natural language processing

Machine Learning for natural language processing Classification: Maximum Entropy Models Laura Kallmeyer Heinrich-Heine-Universität Düsseldorf Summer 2016 1 / 24 Introduction Classification = supervised

Margin-based Decomposed Amortized Inference

Margin-based Decomposed Amortized Inference Gourab Kundu and Vivek Srikumar and Dan Roth University of Illinois, Urbana-Champaign Urbana, IL. 61801 {kundu2, vsrikum2, danr}@illinois.edu Abstract Given

TALP at GeoQuery 2007: Linguistic and Geographical Analysis for Query Parsing

TALP at GeoQuery 2007: Linguistic and Geographical Analysis for Query Parsing Daniel Ferrés and Horacio Rodríguez TALP Research Center Software Department Universitat Politècnica de Catalunya {dferres,horacio}@lsi.upc.edu

arxiv: v2 [cs.cl] 20 Aug 2016

Solving General Arithmetic Word Problems Subhro Roy and Dan Roth University of Illinois, Urbana Champaign {sroy9, danr}@illinois.edu arxiv:1608.01413v2 [cs.cl] 20 Aug 2016 Abstract This paper presents

ALGORITHMS AND ANALYSIS FOR MULTI-CATEGORY CLASSIFICATION

i ALGORITHMS AND ANALYSIS FOR MULTI-CATEGORY CLASSIFICATION Dav Arthur Zimak, Ph.D. Department of Computer Science University of Illinois at Urbana-Champaign, 2006 Dan Roth, Adviser Classification problems

Redoing the Foundations of Decision Theory

Redoing the Foundations of Decision Theory Joe Halpern Cornell University Joint work with Larry Blume and David Easley Economics Cornell Redoing the Foundations of Decision Theory p. 1/21 Decision Making:

Distributional Semantics and Word Embeddings. Chase Geigle

Distributional Semantics and Word Embeddings Chase Geigle 2016-10-14 1 What is a word? dog 2 What is a word? dog canine 2 What is a word? dog canine 3 2 What is a word? dog 3 canine 399,999 2 What is a

Capturing Argument Relationships for Chinese Semantic Role Labeling

Capturing Argument Relationships for Chinese Semantic Role abeling ei Sha, Tingsong Jiang, Sujian i, Baobao Chang, Zhifang Sui Key aboratory of Computational inguistics, Ministry of Education School of

Information Extraction from Text

Information Extraction from Text Jing Jiang Chapter 2 from Mining Text Data (2012) Presented by Andrew Landgraf, September 13, 2013 1 What is Information Extraction? Goal is to discover structured information

Utilizing Portion of Patent Families with No Parallel Sentences Extracted in Estimating Translation of Technical Terms

1 1 1 2 2 30% 70% 70% NTCIR-7 13% 90% 1,000 Utilizing Portion of Patent Families with No Parallel Sentences Extracted in Estimating Translation of Technical Terms Itsuki Toyota 1 Yusuke Takahashi 1 Kensaku

ACS Introduction to NLP Lecture 2: Part of Speech (POS) Tagging

ACS Introduction to NLP Lecture 2: Part of Speech (POS) Tagging Stephen Clark Natural Language and Information Processing (NLIP) Group sc609@cam.ac.uk The POS Tagging Problem 2 England NNP s POS fencers

General Neoclassical Closure Theory: Diagonalizing the Drift Kinetic Operator

General Neoclassical Closure Theory: Diagonalizing the Drift Kinetic Operator E. D. Held eheld@cc.usu.edu Utah State University General Neoclassical Closure Theory:Diagonalizing the Drift Kinetic Operator

Deep Learning for NLP

Deep Learning for NLP CS224N Christopher Manning (Many slides borrowed from ACL 2012/NAACL 2013 Tutorials by me, Richard Socher and Yoshua Bengio) Machine Learning and NLP NER WordNet Usually machine learning

A Syntax-based Statistical Machine Translation Model. Alexander Friedl, Georg Teichtmeister

A Syntax-based Statistical Machine Translation Model Alexander Friedl, Georg Teichtmeister 4.12.2006 Introduction The model Experiment Conclusion Statistical Translation Model (STM): - mathematical model

Cross-Lingual Language Modeling for Automatic Speech Recogntion

GBO Presentation Cross-Lingual Language Modeling for Automatic Speech Recogntion November 14, 2003 Woosung Kim woosung@cs.jhu.edu Center for Language and Speech Processing Dept. of Computer Science The

Encoding Tree Pair-based Graphs in Learning Algorithms: the Textual Entailment Recognition Case

Encoding Tree Pair-based Graphs in Learning Algorithms: the Textual Entailment Recognition Case Alessandro Moschitti DII University of Trento Via ommarive 14 38100 POVO (T) - Italy moschitti@dit.unitn.it

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 REDCLIFF MUNICIPAL PLANNING COMMISSION FOR COMMENT/DISCUSSION DATE: TOPIC: April 27 th, 2018 Bylaw 1860/2018, proposed amendments to the Land Use Bylaw regarding cannabis

11/3/15. Deep Learning for NLP. Deep Learning and its Architectures. What is Deep Learning? Advantages of Deep Learning (Part 1)

11/3/15 Machine Learning and NLP Deep Learning for NLP Usually machine learning works well because of human-designed representations and input features CS224N WordNet SRL Parser Machine learning becomes

Bringing machine learning & compositional semantics together: central concepts

Bringing machine learning & compositional semantics together: central concepts https://githubcom/cgpotts/annualreview-complearning Chris Potts Stanford Linguistics CS 244U: Natural language understanding

Learning Features from Co-occurrences: A Theoretical Analysis

Learning Features from Co-occurrences: A Theoretical Analysis Yanpeng Li IBM T. J. Watson Research Center Yorktown Heights, New York 10598 liyanpeng.lyp@gmail.com Abstract Representing a word by its co-occurrences

Statistical Methods for NLP

Statistical Methods for NLP Sequence Models Joakim Nivre Uppsala University Department of Linguistics and Philology joakim.nivre@lingfil.uu.se Statistical Methods for NLP 1(21) Introduction Structured

Klour Q» m i o r L l V I* , tr a d itim i rvpf tr.j UiC lin» tv'ilit* m in 's *** O.hi nf Iiir i * ii, B.lly Q t " '

/ # g < ) / h h #

Classification, Linear Models, Naïve Bayes

Classification, Linear Models, Naïve Bayes CMSC 470 Marine Carpuat Slides credit: Dan Jurafsky & James Martin, Jacob Eisenstein Today Text classification problems and their evaluation Linear classifiers

Statistical Machine Learning Theory. From Multi-class Classification to Structured Output Prediction. Hisashi Kashima.

http://goo.gl/jv7vj9 Course website KYOTO UNIVERSITY Statistical Machine Learning Theory From Multi-class Classification to Structured Output Prediction Hisashi Kashima kashima@i.kyoto-u.ac.jp DEPARTMENT

Statistical Machine Learning Theory. From Multi-class Classification to Structured Output Prediction. Hisashi Kashima.

http://goo.gl/xilnmn Course website KYOTO UNIVERSITY Statistical Machine Learning Theory From Multi-class Classification to Structured Output Prediction Hisashi Kashima kashima@i.kyoto-u.ac.jp DEPARTMENT

The Noisy Channel Model and Markov Models

1/24 The Noisy Channel Model and Markov Models Mark Johnson September 3, 2014 2/24 The big ideas The story so far: machine learning classifiers learn a function that maps a data item X to a label Y handle

Natural Language Processing CS Lecture 06. Razvan C. Bunescu School of Electrical Engineering and Computer Science

Natural Language Processing CS 6840 Lecture 06 Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu Statistical Parsing Define a probabilistic model of syntax P(T S):

Annotation tasks and solutions in CLARIN-PL

Annotation tasks and solutions in CLARIN-PL Marcin Oleksy, Ewa Rudnicka Wrocław University of Technology marcin.oleksy@pwr.edu.pl ewa.rudnicka@pwr.edu.pl CLARIN ERIC Common Language Resources and Technology

Natural Language Processing

SFU NatLangLab Natural Language Processing Anoop Sarkar anoopsarkar.github.io/nlp-class Simon Fraser University October 9, 2018 0 Natural Language Processing Anoop Sarkar anoopsarkar.github.io/nlp-class

Modern Information Retrieval

Text Classification, Modern Information Retrieval, Addison Wesley, 2009 p. 1/141 Modern Information Retrieval Chapter 5 Text Classification Introduction A Characterization of Text Classification Algorithms

Joint Inference for Event Timeline Construction

Joint Inference for Event Timeline Construction Quang Xuan Do Wei Lu Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801, USA {quangdo2,luwei,danr}@illinois.edu

A Discriminative Model for Semantics-to-String Translation

A Discriminative Model for Semantics-to-String Translation Aleš Tamchyna 1 and Chris Quirk 2 and Michel Galley 2 1 Charles University in Prague 2 Microsoft Research July 30, 2015 Tamchyna, Quirk, Galley

Structure and Complexity of Grammar-Based Machine Translation

Structure and of Grammar-Based Machine Translation University of Padua, Italy New York, June 9th, 2006 1 2 Synchronous context-free grammars Definitions Computational problems 3 problem SCFG projection

Proposition Knowledge Graphs. Gabriel Stanovsky Omer Levy Ido Dagan Bar-Ilan University Israel

Proposition Knowledge Graphs Gabriel Stanovsky Omer Levy Ido Dagan Bar-Ilan University Israel 1 Problem End User 2 Case Study: Curiosity (Mars Rover) Curiosity is a fully equipped lab. Curiosity is a rover.

High-Order Low-Rank Tensors for Semantic Role Labeling

High-Order Low-Rank Tensors for Semantic Role Labeling Tao Lei 1, Yuan Zhang 1, Lluís Màrquez 2, Alessandro Moschitti 2, and Regina Barzilay 1 1 Computer Science and Artificial Intelligence Laboratory,

2003 National Name Exchange Annual Report

2003 National Name Exchange Annual Report Executive Summary 28 th annual meeting Hilton, University of Florida Conference Center April 16, 2004 Hosted by the University of Florida http://www.grad.washington.edu/nameexch/national/

a) b) (Natural Language Processing; NLP) (Deep Learning) Bag of words White House RGB [1] IBM

c 1. (Natural Language Processing; NLP) (Deep Learning) RGB IBM 135 8511 5 6 52 yutat@jp.ibm.com a) b) 2. 1 0 2 1 Bag of words White House 2 [1] 2015 4 Copyright c by ORSJ. Unauthorized reproduction of

Categorial Grammar. Larry Moss NASSLLI. Indiana University

1/37 Categorial Grammar Larry Moss Indiana University NASSLLI 2/37 Categorial Grammar (CG) CG is the tradition in grammar that is closest to the work that we ll do in this course. Reason: In CG, syntax

Learning to translate with neural networks. Michael Auli

Learning to translate with neural networks Michael Auli 1 Neural networks for text processing Similar words near each other France Spain dog cat Neural networks for text processing Similar words near each

Spatial Role Labeling CS365 Course Project

Spatial Role Labeling CS365 Course Project Amit Kumar, akkumar@iitk.ac.in Chandra Sekhar, gchandra@iitk.ac.in Supervisor : Dr.Amitabha Mukerjee ABSTRACT In natural language processing one of the important

A Deterministic Word Dependency Analyzer Enhanced With Preference Learning

A Deterministic Word Dependency Analyzer Enhanced With Preference Learning Hideki Isozaki and Hideto Kazawa and Tsutomu Hirao NTT Communication Science Laboratories NTT Corporation 2-4 Hikaridai, Seikacho,

$%! & (, -3 / 0 4, 5 6/ 6 +7, 6 8 9/ 5 :/ 5 A BDC EF G H I EJ KL N G H I. ] ^ _ ` _ ^ a b=c o e f p a q i h f i a j k e i l _ ^ m=c n ^

! #" $%! & ' ( ) ) (, -. / ( 0 1#2 ' ( ) ) (, -3 / 0 4, 5 6/ 6 7, 6 8 9/ 5 :/ 5 ;=? @ A BDC EF G H I EJ KL M @C N G H I OPQ ;=R F L EI E G H A S T U S V@C N G H IDW G Q G XYU Z A [ H R C \ G ] ^ _ `

Machine Learning for natural language processing

Machine Learning for natural language processing Classification: k nearest neighbors Laura Kallmeyer Heinrich-Heine-Universität Düsseldorf Summer 2016 1 / 28 Introduction Classification = supervised method

TnT Part of Speech Tagger

TnT Part of Speech Tagger By Thorsten Brants Presented By Arghya Roy Chaudhuri Kevin Patel Satyam July 29, 2014 1 / 31 Outline 1 Why Then? Why Now? 2 Underlying Model Other technicalities 3 Evaluation

Hierachical Name Entity Recognition

Hierachical Name Entity Recognition Dakan Wang, Yu Wu Mentor: David Mcclosky, Mihai Surdeanu March 10, 2011 1 Introduction In this project, we investigte the hierarchical name entity recognition problem

Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems

Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems Shyam Upadhyay 1 Ming-Wei Chang 2 1 University of Illinois at Urbana-Champaign, IL, USA 2 Microsoft Research, Redmond,

Department of Computer Science and Engineering Indian Institute of Technology, Kanpur. Spatial Role Labeling

Department of Computer Science and Engineering Indian Institute of Technology, Kanpur CS 365 Artificial Intelligence Project Report Spatial Role Labeling Submitted by Satvik Gupta (12633) and Garvit Pahal

Lecture 3. Jan C. Willems. University of Leuven, Belgium. Minicourse ECC 2003 Cambridge, UK, September 2, 2003

Lecture 3 The ELIMINATION Problem Jan C. Willems University of Leuven, Belgium Minicourse ECC 2003 Cambridge, UK, September 2, 2003 Lecture 3 The ELIMINATION Problem p.1/22 Problematique Develop a theory

Fast and Effective Kernels for Relational Learning from Texts

Fast and Effective Kernels for Relational Learning from Texts Alessandro Moschitti MOCHITTI@DIT.UNITN.IT Department of Information and Communication Technology, University of Trento, 38050 Povo di Trento,

A Boosting-based Algorithm for Classification of Semi-Structured Text using Frequency of Substructures

A Boosting-based Algorithm for Classification of Semi-Structured Text using Frequency of Substructures Tomoya Iwakura Fujitsu Laboratories Ltd iwakura.tomoya@jp.fujitsu.com Abstract Research in text classification

Maxent Models and Discriminative Estimation

Maxent Models and Discriminative Estimation Generative vs. Discriminative models (Reading: J+M Ch6) Introduction So far we ve looked at generative models Language models, Naive Bayes But there is now much

Statistical NLP for the Web Log Linear Models, MEMM, Conditional Random Fields

Statistical NLP for the Web Log Linear Models, MEMM, Conditional Random Fields Sameer Maskey Week 13, Nov 28, 2012 1 Announcements Next lecture is the last lecture Wrap up of the semester 2 Final Project

Domain Adaptation for Word Sense Disambiguation under the Problem of Covariate Shift

Domain Adaptation for Word Sense Disambiguation under the Problem of Covariate Shift HIRONORI KIKUCHI 1,a) HIROYUKI SHINNOU 1,b) Abstract: Word sense disambiguation(wsd) is the task of identifying the

Multi-Component Word Sense Disambiguation

Multi-Component Word Sense Disambiguation Massimiliano Ciaramita and Mark Johnson Brown University BLLIP: http://www.cog.brown.edu/research/nlp Ciaramita and Johnson 1 Outline Pattern classification for

Constructive Decision Theory

Constructive Decision Theory Joe Halpern Cornell University Joint work with Larry Blume and David Easley Economics Cornell Constructive Decision Theory p. 1/2 Savage s Approach Savage s approach to decision

Word Alignment by Thresholded Two-Dimensional Normalization

Word Alignment by Thresholded Two-Dimensional Normalization Hamidreza Kobdani, Alexander Fraser, Hinrich Schütze Institute for Natural Language Processing University of Stuttgart Germany {kobdani,fraser}@ims.uni-stuttgart.de

Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D

Lesson eight What are characteristics of chemical reactions? Science Constructing Explanations, Engaging in Argument and Obtaining, Evaluating, and Communicating Information ENGLISH LANGUAGE ARTS Reading

SemEval-2013 Task 3: Spatial Role Labeling

SemEval-2013 Task 3: Spatial Role Labeling Oleksandr Kolomiyets, Parisa Kordjamshidi, Steven Bethard and Marie-Francine Moens KU Leuven, Celestijnenlaan 200A, Heverlee 3001, Belgium University of Colorado,

Inducing Value Sparsity for Parallel Inference in Tree-shaped Models

Inducing Value Sparsity for Parallel Inference in Tree-shaped Models Sameer Singh Brian Martin Andrew McCallum Department of Computer Science, University of Massachusetts, Amherst, MA 0003 {sameer, martin,

Transition-based Dependency Parsing with Selectional Branching

Transition-based Dependency Parsing with Selectional Branching Jinho D. Choi Department of Computer Science University of Massachusetts Amherst Amherst, MA, 01003, USA jdchoi@cs.umass.edu Andrew McCallum

Part-of-Speech Tagging + Neural Networks 3: Word Embeddings CS 287

Part-of-Speech Tagging + Neural Networks 3: Word Embeddings CS 287 Review: Neural Networks One-layer multi-layer perceptron architecture, NN MLP1 (x) = g(xw 1 + b 1 )W 2 + b 2 xw + b; perceptron x is the

Framework for functional tree simulation applied to 'golden delicious' apple trees

Purdue University Purdue e-pubs Open Access Theses Theses and Dissertations Spring 2015 Framework for functional tree simulation applied to 'golden delicious' apple trees Marek Fiser Purdue University

Machine Learning for natural language processing

Machine Learning for natural language processing Hidden Markov Models Laura Kallmeyer Heinrich-Heine-Universität Düsseldorf Summer 2016 1 / 33 Introduction So far, we have classified texts/observations

Multiword Expression Identification with Tree Substitution Grammars

Multiword Expression Identification with Tree Substitution Grammars Spence Green, Marie-Catherine de Marneffe, John Bauer, and Christopher D. Manning Stanford University EMNLP 2011 Main Idea Use syntactic

Multi-View Learning of Word Embeddings via CCA

Multi-View Learning of Word Embeddings via CCA Paramveer S. Dhillon Dean Foster Lyle Ungar Computer & Information Science Statistics Computer & Information Science University of Pennsylvania, Philadelphia,

Deep Learning for NLP Part 2

Deep Learning for NLP Part 2 CS224N Christopher Manning (Many slides borrowed from ACL 2012/NAACL 2013 Tutorials by me, Richard Socher and Yoshua Bengio) 2 Part 1.3: The Basics Word Representations The

Large-scale Image Annotation by Efficient and Robust Kernel Metric Learning

Large-scale Image Annotation by Efficient and Robust Kernel Metric Learning Supplementary Material Zheyun Feng Rong Jin Anil Jain Department of Computer Science and Engineering, Michigan State University,

Manifold Regularization

Manifold Regularization Vikas Sindhwani Department of Computer Science University of Chicago Joint Work with Mikhail Belkin and Partha Niyogi TTI-C Talk September 14, 24 p.1 The Problem of Learning is

Determining Word Sense Dominance Using a Thesaurus

Determining Word Sense Dominance Using a Thesaurus Saif Mohammad and Graeme Hirst Department of Computer Science University of Toronto EACL, Trento, Italy (5th April, 2006) Copyright cfl2006, Saif Mohammad

Sample Copy. Not For Distribution.

A Text Book of 'Logic and Sets' (Skill Enhancement Course) CBCS Pattern i Publishing-in-support-of, EDUCREATION PUBLISHING RZ 94, Sector - 6, Dwarka, New Delhi - 110075 Shubham Vihar, Mangla, Bilaspur,

A Markov Logic Approach to Bio-Molecular Event Extraction

A Markov Logic Approach to Bio-Molecular Event Extraction Sebastian Riedel Hong-Woo Chun Toshihisa Takagi Jun'ichi Tsujii Database Center for Life Science, Research Organization of Information and System,

Rank University AMJ AMR ASQ JAP OBHDP OS PPSYCH SMJ SUM 1 University of Pennsylvania (T) Michigan State University

Rank University AMJ AMR ASQ JAP OBHDP OS PPSYCH SMJ SUM 1 University of Pennsylvania 4 1 2 0 2 4 0 9 22 2(T) Michigan State University 2 0 0 9 1 0 0 4 16 University of Michigan 3 0 2 5 2 0 0 4 16 4 Harvard

Introduction to Support Vector Machines

Introduction to Support Vector Machines Hsuan-Tien Lin Learning Systems Group, California Institute of Technology Talk in NTU EE/CS Speech Lab, November 16, 2005 H.-T. Lin (Learning Systems Group) Introduction

Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text

Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text Yi Zhang Machine Learning Department Carnegie Mellon University yizhang1@cs.cmu.edu Jeff Schneider The Robotics Institute

Part of Speech Tagging: Viterbi, Forward, Backward, Forward- Backward, Baum-Welch. COMP-599 Oct 1, 2015

Part of Speech Tagging: Viterbi, Forward, Backward, Forward- Backward, Baum-Welch COMP-599 Oct 1, 2015 Announcements Research skills workshop today 3pm-4:30pm Schulich Library room 313 Start thinking about

Use precise language and domain-specific vocabulary to inform about or explain the topic. CCSS.ELA-LITERACY.WHST D

Lesson seven What is a chemical reaction? Science Constructing Explanations, Engaging in Argument and Obtaining, Evaluating, and Communicating Information ENGLISH LANGUAGE ARTS Reading Informational Text,

Analysis of Spectral Kernel Design based Semi-supervised Learning

Analysis of Spectral Kernel Design based Semi-supervised Learning Tong Zhang IBM T. J. Watson Research Center Yorktown Heights, NY 10598 Rie Kubota Ando IBM T. J. Watson Research Center Yorktown Heights,

Topics in Natural Language Processing

Topics in Natural Language Processing Shay Cohen Institute for Language, Cognition and Computation University of Edinburgh Lecture 9 Administrativia Next class will be a summary Please email me questions

A Probabilistic Forest-to-String Model for Language Generation from Typed Lambda Calculus Expressions

A Probabilistic Forest-to-String Model for Language Generation from Typed Lambda Calculus Expressions Wei Lu and Hwee Tou Ng National University of Singapore 1/26 The Task (Logical Form) λx 0.state(x 0

Planning for Reactive Behaviors in Hide and Seek

University of Pennsylvania ScholarlyCommons Center for Human Modeling and Simulation Department of Computer & Information Science May 1995 Planning for Reactive Behaviors in Hide and Seek Michael B. Moore

Vectors. Teaching Learning Point. Ç, where OP. l m n

Vectors 9 Teaching Learning Point l A quantity that has magnitude as well as direction is called is called a vector. l A directed line segment represents a vector and is denoted y AB Å or a Æ. l Position

Multilingual Semantic Role Labelling with Markov Logic

Multilingual Semantic Role Labelling with Markov Logic Ivan Meza-Ruiz Sebastian Riedel School of Informatics, University of Edinburgh, UK Department of Computer Science, University of Tokyo, Japan Database

17 th Annual GeoElections User s Conference

17 th Annual GeoElections User s Conference When: December 12 th, December 13 th, and December 14 th, 2018 Where: Embassy Suites Hotel Tampa-Brandon 10220 Palm River Road Tampa, FL 33619 Tel: 813-653-1905

A Multi-Modal Combinatory Categorial Grammar. A MMCCG Analysis of Japanese Nonconstituent Clefting

A Multi-Modal Combinatory Categorial Grammar Analysis of Japanese Nonconstituent Clefting Department of Linguistics The Ohio State University {kubota,esmith}@ling.osu.edu A basic cleft sentence Data Clefting

Examination paper for TFY4240 Electromagnetic theory

Department of Physics Examination paper for TFY4240 Electromagnetic theory Academic contact during examination: Associate Professor John Ove Fjærestad Phone: 97 94 00 36 Examination date: 16 December 2015

Linking people in videos with their names using coreference resolution (Supplementary Material)

Linking people in videos with their names using coreference resolution (Supplementary Material) Vignesh Ramanathan, Armand Joulin, Percy Liang, Li Fei-Fei Department of Electrical Engineering, Stanford

What is semi-supervised learning?

What is semi-supervised learning? In many practical learning domains, there is a large supply of unlabeled data but limited labeled data, which can be expensive to generate text processing, video-indexing,

Notation. Pattern Recognition II. Michal Haindl. Outline - PR Basic Concepts. Pattern Recognition Notions

Notation S pattern space X feature vector X = [x 1,...,x l ] l = dim{x} number of features X feature space K number of classes ω i class indicator Ω = {ω 1,...,ω K } g(x) discriminant function H decision

Cross-Entropy and Estimation of Probabilistic Context-Free Grammars

Cross-Entropy and Estimation of Probabilistic Context-Free Grammars Anna Corazza Department of Physics University Federico II via Cinthia I-8026 Napoli, Italy corazza@na.infn.it Giorgio Satta Department

Council on East Asian Libraries Statistics For North American Institutions

Council on East Asian Libraries Statistics 2006-2007 For North American Institutions Collected and Compiled by the CEAL Statistics Committee, 2006-2007 Vickie Fu Doll, Chair