Rohan Ramanath, R. V. College of Engineering, Bangalore Monojit Choudhury, Kalika Bali, Microsoft Research India Rishiraj Saha Roy, IIT Kharagpur

Rishiraj Saha Roy and Niloy Ganguly IIT Kharagpur India. Monojit Choudhury and Srivatsan Laxman Microsoft Research India India

Rishiraj Saha Roy and Niloy Ganguly IIT Kharagpur India Monojit Choudhury and Srivatsan Laxman Microsoft Research India India ACM SIGIR 2012, Portland August 15, 2012 Dividing a query into individual semantic

Rishiraj Saha Roy. Ph.D. Student Computer Science and Engineering IIT Kharagpur, India

Rishiraj Saha Roy Ph.D. Student Computer Science and Engineering IIT Kharagpur, India Under the guidance of Prof. Niloy Ganguly (IIT Kharagpur, India) and Dr. Monojit Choudhury (Microsoft Research India)

Improving Document Ranking for Long Queries with Nested Query Segmentation

Improving Document Ranking for Long Queries with Nested Query Segmentation Rishiraj Saha Roy 1, Anusha Suresh 2, Niloy Ganguly 2, and Monojit Choudhury 3 1 Max Planck Institute for Informatics, Saarbrücken,

Lecture 14: Annotation

Lecture 14: Annotation Nathan Schneider (with material from Henry Thompson, Alex Lascarides) ENLP 23 October 2016 1/14 Annotation Why gold 6= perfect Quality Control 2/14 Factors in Annotation Suppose

Improving Document Ranking for Long Queries with Nested Query Segmentation

Improving Document Ranking for Long Queries with Nested Query Segmentation Rishiraj Saha Roy 1, Anusha Suresh 1, Niloy Ganguly 1, Monojit Choudhury 2, Deepak Shankar 3, and Tanwita Nimiar 3 1 Indian Institute

Tools for Annotating and Searching Corpora Practical Session 1: Annotating

Tools for Annotating and Searching Corpora Practical Session 1: Annotating Stefanie Dipper Institute of Linguistics Ruhr-University Bochum Corpus Linguistics Fest (CLiF) June 6-10, 2016 Indiana University,

Ranked Retrieval. Evaluation in IR. One option is to average the precision scores at discrete. points on the ROC curve But which points?

Ranked Retrieval One option is to average the precision scores at discrete Precision 100% 0% More junk 100% Everything points on the ROC curve But which points? Recall We want to evaluate the system, not

Rishiraj Saha Roy Ph.D. Student

Rishiraj Saha Roy Ph.D. Student Supervisors Prof. Niloy Ganguly (IIT Kharagpur) Dr. Monojit Choudhury (Microsoft Research India) Computer Science and Engineering IIT Kharagpur Niloy Ganguly IIT Kharagpur

Complex Linguistic Annotation No Easy Way Out! A Case from Bangla and Hindi POS Labeling Tasks

Complex Linguistic Annotation No Easy Way Out! A Case from Bangla and Hindi POS Labeling Tasks Sandipan Dandapat 1 Priyanka Biswas 1 Monojit Choudhury Kalika Bali Dublin City University LDCIL Microsoft

Retrieval Evaluation. Hongning Wang

Retrieval Evaluation Hongning Wang CS@UVa What we have learned so far Indexed corpus Crawler Ranking procedure Research attention Doc Analyzer Doc Rep (Index) Query Rep Feedback (Query) Evaluation User

A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2

A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2 1 Department of Electronics & Comp. Sc, RTMNU, Nagpur, India 2 Department of Computer Science, Hislop College, Nagpur,

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) CONTEXT SENSITIVE TEXT SUMMARIZATION USING HIERARCHICAL CLUSTERING ALGORITHM

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & 6367(Print), ISSN 0976 6375(Online) Volume 3, Issue 1, January- June (2012), TECHNOLOGY (IJCET) IAEME ISSN 0976 6367(Print) ISSN 0976 6375(Online) Volume

University of Virginia Department of Computer Science. CS 4501: Information Retrieval Fall 2015

University of Virginia Department of Computer Science CS 4501: Information Retrieval Fall 2015 2:00pm-3:30pm, Tuesday, December 15th Name: ComputingID: This is a closed book and closed notes exam. No electronic

GEOFLUENT VS. STRAIGHT MACHINE TRANSLATION: UNDERSTANDING THE DIFFERENCES

SOLUTION BRIEF GEOFLUENT VS. STRAIGHT MACHINE TRANSLATION: UNDERSTANDING THE DIFFERENCES by EXECUTIVE SUMMARY Real-time translation (RTT) technology is an effective solution for instantly translating content

Pull the Plug? Predicting If Computers or Humans Should Segment Images Supplementary Material

In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 2016. Pull the Plug? Predicting If Computers or Humans Should Segment Images Supplementary Material

Multiclass Classification

Multiclass Classification Instructor: Jessica Wu Harvey Mudd College The instructor gratefully acknowledges Eric Eaton (UPenn), David Kauchak (Pomona), Tommi Jaakola (MIT) and the many others who made

Jure Leskovec, Cornell/Stanford University. Joint work with Kevin Lang, Anirban Dasgupta and Michael Mahoney, Yahoo! Research

Jure Leskovec, Cornell/Stanford University Joint work with Kevin Lang, Anirban Dasgupta and Michael Mahoney, Yahoo! Research Network: an interaction graph: Nodes represent entities Edges represent interaction

Determining Reliability of Subjective and Multi-label Emotion Annotation through Novel Fuzzy Agreement Measure

Determining Reliability of Subjective and Multi-label Emotion Annotation through Novel Fuzzy Agreement Measure Plaban Kr. Bhowmick, Anupam Basu, Pabitra Mitra Department of Computer Science & Engineering

Getting Started with DKPro Agreement

Getting Started with DKPro Agreement Christian M. Meyer, Margot Mieskes, Christian Stab and Iryna Gurevych: DKPro Agreement: An Open-Source Java Library for Measuring Inter- Rater Agreement, in: Proceedings

Current areas: Question-answering over knowledge bases and text, User-oriented online privacy and transparency

RISHIRAJ SAHA ROY Post-doctoral Researcher Max Planck Institute for Informatics, Saarbruecken, Germany [November 2015 - Present ] Computer Scientist, Adobe Research, Bangalore, India [April 2014 September

Modern Retrieval Evaluations. Hongning Wang

Modern Retrieval Evaluations Hongning Wang CS@UVa What we have known about IR evaluations Three key elements for IR evaluation A document collection A test suite of information needs A set of relevance

Knowledge Engineering with Semantic Web Technologies

This file is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0) Knowledge Engineering with Semantic Web Technologies Lecture 5: Ontological Engineering 5.3 Ontology Learning

GOOGLE ADDS 4 NEW FEATURES TO ITS MY BUSINESS DASHBOARD HTTPS WEBSITES ARE DOMINATING THE FIRST PAGE

1 GOOGLE ADDS 4 NEW FEATURES TO ITS MY BUSINESS DASHBOARD 2 HTTPS WEBSITES ARE DOMINATING THE FIRST PAGE 3 WHY YOU SHOULD BE PAYING MORE ATTENTION TO REVIEWS! 4 BING ROLLS OUT THREE NEW UPDATES FOR ADVERTISERS

Creating and Managing Autodesk Inventor Nesting Utility Templates

WHITEPAPER Creating and Managing Autodesk Inventor Nesting Utility Templates Autodesk s Nesting Utility introduces a new template type into Autodesk Inventor with the extension of inest. When the utility

Question Answering Systems

Question Answering Systems An Introduction Potsdam, Germany, 14 July 2011 Saeedeh Momtazi Information Systems Group Outline 2 1 Introduction Outline 2 1 Introduction 2 History Outline 2 1 Introduction

ebook library PAGE 1 HOW TO OPTIMIZE TRANSLATIONS AND ACCELERATE TIME TO MARKET

ebook library PAGE 1 HOW TO OPTIMIZE TRANSLATIONS AND ACCELERATE TIME TO MARKET Aligning people, process and technology to improve quality and speed to market To succeed in the global business arena, companies

Alberto Messina, Maurizio Montagnuolo

A Generalised Cross-Modal Clustering Method Applied to Multimedia News Semantic Indexing and Retrieval Alberto Messina, Maurizio Montagnuolo RAI Centre for Research and Technological Innovation Madrid,

TTIC 31190: Natural Language Processing

TTIC 31190: Natural Language Processing Kevin Gimpel Winter 2016 Lecture 2: Text Classification 1 Please email me (kgimpel@ttic.edu) with the following: your name your email address whether you taking

Semantics Isn t Easy Thoughts on the Way Forward

Semantics Isn t Easy Thoughts on the Way Forward NANCY IDE, VASSAR COLLEGE REBECCA PASSONNEAU, COLUMBIA UNIVERSITY COLLIN BAKER, ICSI/UC BERKELEY CHRISTIANE FELLBAUM, PRINCETON UNIVERSITY New York University

Exploiting Conversation Structure in Unsupervised Topic Segmentation for s

Exploiting Conversation Structure in Unsupervised Topic Segmentation for Emails Shafiq Joty, Giuseppe Carenini, Gabriel Murray, Raymond Ng University of British Columbia Vancouver, Canada EMNLP 2010 1

Center for Reflected Text Analytics. Lecture 2 Annotation tools & Segmentation

Center for Reflected Text Analytics Lecture 2 Annotation tools & Segmentation Summary of Part 1 Annotation theory Guidelines Inter-Annotator agreement Inter-subjective annotations Annotation exercise Discuss

A Quick Guide to Localizing Games for Global Markets. Learn how to adapt your game for gamers worldwide

A Quick Guide to Localizing Games for Global Markets Learn how to adapt your game for gamers worldwide In this guide you ll find... What is Localization // Page 1 Why Localize Your Game // Page 1 Step

Most keyword search tools would place a capacitive touch panel further down the results list.

IHS Goldfire Quick Start Guide to Researcher and Innovation Trend Analysis Greg Dance, Technical Account Manager 2014 Background Goldfire provides the ability to research a complete concept found in one

Lightly Supervised Learning of Procedural Dialog Systems. Svitlana Volkova Pallavi Choudhury, Chris Quirk, Bill Dolan, Luke Zettlemoyer

Lightly Supervised Learning of Procedural Dialog Systems Svitlana Volkova Pallavi Choudhury, Chris Quirk, Bill Dolan, Luke Zettlemoyer Motivation Procedural dialog systems aim to assist users with a range

Chapter 6 Evaluation Metrics and Evaluation

Chapter 6 Evaluation Metrics and Evaluation The area of evaluation of information retrieval and natural language processing systems is complex. It will only be touched on in this chapter. First the scientific

Cognos: Crowdsourcing Search for Topic Experts in Microblogs

Cognos: Crowdsourcing Search for Topic Experts in Microblogs Saptarshi Ghosh, Naveen Sharma, Fabricio Benevenuto, Niloy Ganguly, Krishna Gummadi IIT Kharagpur, India; UFOP, Brazil; MPI-SWS, Germany Topic

Data and the Environment: Impacts on Cost and Success

Data and the Environment: Impacts on Cost and Success April 2009 Philip Sampson 630-217-6614 Agenda Cost of Quality Test Objectives Data consideration fundamentals Environment consideration fundamentals

Large-Scale Validation and Analysis of Interleaved Search Evaluation

Large-Scale Validation and Analysis of Interleaved Search Evaluation Olivier Chapelle, Thorsten Joachims Filip Radlinski, Yisong Yue Department of Computer Science Cornell University Decide between two

Streaming Massive Environments From Zero to 200MPH

FORZA MOTORSPORT From Zero to 200MPH Chris Tector (Software Architect Turn 10 Studios) Turn 10 Internal studio at Microsoft Game Studios - we make Forza Motorsport Around 70 full time staff 2 Why am I

Building and Annotating Corpora of Collaborative Authoring in Wikipedia

Building and Annotating Corpora of Collaborative Authoring in Wikipedia Johannes Daxenberger, Oliver Ferschke and Iryna Gurevych Workshop: Building Corpora of Computer-Mediated Communication: Issues, Challenges,

Supplemental Material: Multi-Class Open Set Recognition Using Probability of Inclusion

Supplemental Material: Multi-Class Open Set Recognition Using Probability of Inclusion Lalit P. Jain, Walter J. Scheirer,2, and Terrance E. Boult,3 University of Colorado Colorado Springs 2 Harvard University

Semantic and Multimodal Annotation. CLARA University of Copenhagen August 2011 Susan Windisch Brown

Semantic and Multimodal Annotation CLARA University of Copenhagen 15-26 August 2011 Susan Windisch Brown 2 Program: Monday Big picture Coffee break Lexical ambiguity and word sense annotation Lunch break

FUNctions. Lecture 03 Spring 2018

FUNctions Lecture 03 Spring 2018 Announcements PS0 Due Tomorrow at 11:59pm WS1 Released soon, due next Friday 2/2 at 11:59pm Not quite understand a topic in lecture this week? Come to Tutoring Tomorrow

Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning

Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning V. Zhong, C. Xiong, R. Socher Salesforce Research arxiv: 1709.00103 Reviewed by : Bill Zhang University of Virginia

Bagging & System Combination for POS Tagging. Dan Jinguji Joshua T. Minor Ping Yu

Bagging & System Combination for POS Tagging Dan Jinguji Joshua T. Minor Ping Yu Bagging Bagging can gain substantially in accuracy The vital element is the instability of the learning algorithm Bagging

Extracting Information from Social Networks

Extracting Information from Social Networks Reminder: Social networks Catch-all term for social networking sites Facebook microblogging sites Twitter blog sites (for some purposes) 1 2 Ways we can use

Examining the Limits of Crowdsourcing for Relevance Assessment

Examining the Limits of Crowdsourcing for Relevance Assessment Paul Clough: Information School, University of Sheffield, UK; p.d.clough@sheffield.ac.uk; +441142222664 Mark Sanderson: School of Computer

Error annotation in adjective noun (AN) combinations

Error annotation in adjective noun (AN) combinations This document describes the annotation scheme devised for annotating errors in AN combinations and explains how the inter-annotator agreement has been

PRIVACY THROUGH SOLIDARITY Asia J. Biega Rishiraj Saha Roy Gerhard Weikum

PRIVACY THROUGH SOLIDARITY Asia J. Biega Rishiraj Saha Roy Gerhard Weikum YOUR PROFILE MEANS PERSONALIZATION SEARCH RECOMMENDATIONS python programming, java, inheritance, nltk YOUR PROFILE MEANS YOU SEARCH

Multilingual Image Search from a user s perspective

Multilingual Image Search from a user s perspective Julio Gonzalo, Paul Clough, Jussi Karlgren QUAERO-Image CLEF workshop, 16/09/08 Finding is a matter of two fast stupid smart slow great potential for

The Internet. Search Engines Rank Results According To Relevance

You would learn how to effectively search for information online as well as how to evaluate the quality of online sources. Search Engines Rank Results According To Relevance Making A Site More Noticeable

SEO and UAEX.EDU GETTING YOUR WEB PAGES FOUND IN GOOGLE

SEO and UAEX.EDU GETTING YOUR WEB PAGES FOUND IN GOOGLE What is Search Engine Optimization? SEO is a marketing discipline focused on growing visibility in organic (non-paid) search engine results. Why

Overfitting, Model Selection, Cross Validation, Bias-Variance

Statistical Machine Learning Notes 2 Overfitting, Model Selection, Cross Validation, Bias-Variance Instructor: Justin Domke Motivation Suppose we have some data TRAIN = {(, ), ( 2, 2 ),..., ( N, N )} that

Making Sense Out of the Web

Making Sense Out of the Web Rada Mihalcea University of North Texas Department of Computer Science rada@cs.unt.edu Abstract. In the past few years, we have witnessed a tremendous growth of the World Wide

UX Research in the Product Lifecycle

UX Research in the Product Lifecycle I incorporate how users work into the product early, frequently and iteratively throughout the development lifecycle. This means selecting from a suite of methods and

Triangles, Squares, & Segregation

Triangles, Squares, & Segregation (based on the work of Vi Hart & Nicky Case) Tara T. Craig & Anne M. Ho December 2016 1 Population of Polygons This is a population of Polygons including Triangles and

Comparative Analysis of Clicks and Judgments for IR Evaluation

Comparative Analysis of Clicks and Judgments for IR Evaluation Jaap Kamps 1,3 Marijn Koolen 1 Andrew Trotman 2,3 1 University of Amsterdam, The Netherlands 2 University of Otago, New Zealand 3 INitiative

It is been used to calculate the score which denotes the chances that given word is equal to some other word.

INTRODUCTION While I was tackling a NLP (Natural Language Processing) problem for one of my project "Stephanie", an open-source platform imitating a voice-controlled virtual assistant, it required a specific

Supervised Ranking for Plagiarism Source Retrieval

Supervised Ranking for Plagiarism Source Retrieval Notebook for PAN at CLEF 2013 Kyle Williams, Hung-Hsuan Chen, and C. Lee Giles, Information Sciences and Technology Computer Science and Engineering Pennsylvania

in the NTU Multilingual Corpus (NTU-MC) January 15, 2016

. Sentiment Annotation in the NTU Multilingual Corpus (NTU-MC). 2 nd Wordnet Bahasa Workshop (WBW2016) Francis Bond, Tomoko Ohkuma, Luis Morgado Da Costa, Yasuhide Miura, Rachel Chen, Takayuki Kuribayashi,

Context-Free Grammars

Context-Free Grammars Carl Pollard yntax 2 (Linguistics 602.02) January 3, 2012 Context-Free Grammars (CFGs) A CFG is an ordered quadruple T, N, D, P where a. T is a finite set called the terminals; b.

CPSC 340: Machine Learning and Data Mining. Kernel Trick Fall 2017

CPSC 340: Machine Learning and Data Mining Kernel Trick Fall 2017 Admin Assignment 3: Due Friday. Midterm: Can view your exam during instructor office hours or after class this week. Digression: the other

CSE 546 Machine Learning, Autumn 2013 Homework 2

CSE 546 Machine Learning, Autumn 2013 Homework 2 Due: Monday, October 28, beginning of class 1 Boosting [30 Points] We learned about boosting in lecture and the topic is covered in Murphy 16.4. On page

SEARCH ENGINE OPTIMIZATION Noun The process of maximizing the number of visitors to a particular website by ensuring that the site appears high on the list of results returned by a search engine such as

Multi-objective Optimization

Jugal K. Kalita Single vs. Single vs. Single Objective Optimization: When an optimization problem involves only one objective function, the task of finding the optimal solution is called single-objective

Chapter 13: CODING DESIGN, CODING PROCESS, AND CODER RELIABILITY STUDIES

Chapter 13: CODING DESIGN, CODING PROCESS, AND CODER RELIABILITY STUDIES INTRODUCTION The proficiencies of PISA for Development (PISA-D) respondents were estimated based on their performance on test items

Neuromarketing. Report to accompany the Zotero database. 4 December Nicoline Beun MSc. Department of Marketing Management

Neuromarketing Report to accompany the Zotero database 4 December 2013 Nicoline Beun MSc. Department of Marketing Management Table of Contents Introduction... 2 Basic metrics... 2 Number of items... 2

Heading-Based Sectional Hierarchy Identification for HTML Documents

Heading-Based Sectional Hierarchy Identification for HTML Documents 1 Dept. of Computer Engineering, Boğaziçi University, Bebek, İstanbul, 34342, Turkey F. Canan Pembe 1,2 and Tunga Güngör 1 2 Dept. of

Contents. 1. Survey Background and Methodology. 2. Summary of Key Findings. 3. Survey Results. 4. Appendix

Mobile Trends 2014 Contents 1. Survey Background and Methodology 2. Summary of Key Findings 3. Survey Results 4. Appendix 2 Research Methodology Method Sample Size Online survey programmed and hosted by

Audit Report. The Chartered Institute of Personnel and Development (CIPD)

Audit Report The Chartered Institute of Personnel and Development (CIPD) 24 February 2015 Contents 1 Background 1 1.1 Scope 1 1.2 Audit Report and Action Plan Timescales 2 1.3 Summary of Audit Issues and

What is this Song About?: Identification of Keywords in Bollywood Lyrics

What is this Song About?: Identification of Keywords in Bollywood Lyrics by Drushti Apoorva G, Kritik Mathur, Priyansh Agrawal, Radhika Mamidi in 19th International Conference on Computational Linguistics

Trees, Trees and More Trees

Trees, Trees and More Trees August 9, 01 Andrew B. Kahng abk@cs.ucsd.edu http://vlsicad.ucsd.edu/~abk/ How You ll See Trees in CS Trees as mathematical objects Trees as data structures Trees as tools for

Advanced Topics in Information Retrieval. Learning to Rank. ATIR July 14, 2016

Advanced Topics in Information Retrieval Learning to Rank Vinay Setty vsetty@mpi-inf.mpg.de Jannik Strötgen jannik.stroetgen@mpi-inf.mpg.de ATIR July 14, 2016 Before we start oral exams July 28, the full

1 Machine Learning System Design

Machine Learning System Design Prioritizing what to work on: Spam classification example Say you want to build a spam classifier Spam messages often have misspelled words We ll have a labeled training

Efficient Load Balancing and Fault tolerance Mechanism for Cloud Environment

Efficient Load Balancing and Fault tolerance Mechanism for Cloud Environment Pooja Kathalkar 1, A. V. Deorankar 2 1 Department of Computer Science and Engineering, Government College of Engineering Amravati

09200 Business English - II Certificate in Accounting and Business II Examination September 2012 THE INSTITUTE OF CHARTERED ACCOUNTANTS OF SRI LANKA

SUGGESTED SOLUTIONS 09200 Business English - II Certificate in Accounting and Business II Examination September 2012 THE INSTITUTE OF CHARTERED ACCOUNTANTS OF SRI LANKA All Rights Reserved Answer No. 01

Top 20 Data Quality Solutions for Data Science

Top 20 Data Quality Solutions for Data Science Data Science & Business Analytics Meetup Boulder, CO 2014-12-03 Ken Farmer DQ Problems for Data Science Loom Large & Frequently 4000000 Strikingly visible

Web-based Question Answering: Revisiting AskMSR

Web-based Question Answering: Revisiting AskMSR Chen-Tse Tsai University of Illinois Urbana, IL 61801, USA ctsai12@illinois.edu Wen-tau Yih Christopher J.C. Burges Microsoft Research Redmond, WA 98052,

A tool for Cross-Language Pair Annotations: CLPA

A tool for Cross-Language Pair Annotations: CLPA August 28, 2006 This document describes our tool called Cross-Language Pair Annotator (CLPA) that is capable to automatically annotate cognates and false

The Muc7 T Corpus. 1 Introduction. 2 Creation of Muc7 T

The Muc7 T Corpus Katrin Tomanek and Udo Hahn Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Germany {katrin.tomanek udo.hahn}@uni-jena.de 1 Introduction

Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p.

Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p. 6 What is Web Mining? p. 6 Summary of Chapters p. 8 How

The FreeSearch System

Wolfgang Nejdl 03/05/12 1 The FreeSearch System Search engine for digital libraries Simple to use interface Intuitive functionalities Easily scalable Now with focus on Duplicate detection and duplicate

Final Project Discussion. Adam Meyers Montclair State University

Final Project Discussion Adam Meyers Montclair State University Summary Project Timeline Project Format Details/Examples for Different Project Types Linguistic Resource Projects: Annotation, Lexicons,...

ACE (Automatic Content Extraction) English Annotation Guidelines for Values. Version

ACE (Automatic Content Extraction) English Annotation Guidelines for Values Version 1.2.4 Linguistic Data Consortium http://www.ldc.upenn.edu/projects/ace/ 1. Basic Concepts...3 1.1 What is a Value?...3

Agenda. ! Efficient Coding Hypothesis. ! Response Function and Optimal Stimulus Ensemble. ! Firing-Rate Code. ! Spike-Timing Code

1 Agenda! Efficient Coding Hypothesis! Response Function and Optimal Stimulus Ensemble! Firing-Rate Code! Spike-Timing Code! OSE vs Natural Stimuli! Conclusion 2 Efficient Coding Hypothesis! [Sensory systems]

University of Virginia Department of Computer Science. CS 4501: Information Retrieval Fall 2015

University of Virginia Department of Computer Science CS 4501: Information Retrieval Fall 2015 5:00pm-6:15pm, Monday, October 26th Name: ComputingID: This is a closed book and closed notes exam. No electronic

PRIS at TAC2012 KBP Track

PRIS at TAC2012 KBP Track Yan Li, Sijia Chen, Zhihua Zhou, Jie Yin, Hao Luo, Liyin Hong, Weiran Xu, Guang Chen, Jun Guo School of Information and Communication Engineering Beijing University of Posts and

Annotation and Evaluation

Annotation and Evaluation Digging into Data: Jordan Boyd-Graber University of Maryland April 15, 2013 Digging into Data: Jordan Boyd-Graber (UMD) Annotation and Evaluation April 15, 2013 1 / 21 Exam Solutions

A conceptual model of trademark retrieval based on conceptual similarity

Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 22 (2013 ) 450 459 17 th International Conference on Knowledge Based and Intelligent Information and Engineering Systems

Experiment Design and Evaluation for Information Retrieval Rishiraj Saha Roy Computer Scientist, Adobe Research Labs India

Experiment Design and Evaluation for Information Retrieval Rishiraj Saha Roy Computer Scientist, Adobe Research Labs India rroy@adobe.com 2014 Adobe Systems Incorporated. All Rights Reserved. 1 Introduction

CMU-UKA Syntax Augmented Machine Translation

Outline CMU-UKA Syntax Augmented Machine Translation Ashish Venugopal, Andreas Zollmann, Stephan Vogel, Alex Waibel InterACT, LTI, Carnegie Mellon University Pittsburgh, PA Outline Outline 1 2 3 4 Issues

Optimization of Query Processing in XML Document Using Association and Path Based Indexing

Optimization of Query Processing in XML Document Using Association and Path Based Indexing D.Karthiga 1, S.Gunasekaran 2 Student,Dept. of CSE, V.S.B Engineering College, TamilNadu, India 1 Assistant Professor,Dept.

Information Retrieval

Information Retrieval ETH Zürich, Fall 2012 Thomas Hofmann LECTURE 6 EVALUATION 24.10.2012 Information Retrieval, ETHZ 2012 1 Today s Overview 1. User-Centric Evaluation 2. Evaluation via Relevance Assessment

Meridian Linguistics Testing Service 2018 English Style Guide

STYLE GUIDE ENGLISH INTRODUCTION The following style sheet has been created based on the following external style guides. It is recommended that you use this document as a quick reference sheet and refer

Standardization in Assessment and Reporting of Intercoder Reliability in Content Analyses

Standardization in Assessment and Reporting of Intercoder Reliability in Content Analyses Matthew Lombard, Temple University December 5, 2008 University of Michigan Overview History of interest in topic

DS Machine Learning and Data Mining I. Alina Oprea Associate Professor, CCIS Northeastern University

DS 4400 Machine Learning and Data Mining I Alina Oprea Associate Professor, CCIS Northeastern University January 24 2019 Logistics HW 1 is due on Friday 01/25 Project proposal: due Feb 21 1 page description

Discriminative Training for Phrase-Based Machine Translation

Discriminative Training for Phrase-Based Machine Translation Abhishek Arun 19 April 2007 Overview 1 Evolution from generative to discriminative models Discriminative training Model Learning schemes Featured

Domain-Specific. Languages. Martin Fowler. AAddison-Wesley. Sydney Tokyo. With Rebecca Parsons

Domain-Specific Languages Martin Fowler With Rebecca Parsons AAddison-Wesley Upper Saddle River, NJ Boston Indianapolis San Francisco New York Toronto Montreal London Munich Paris Madrid Sydney Tokyo Singapore

The English House of Commas

This set of slides will illustrate the most common uses of one of the most common punctuation marks: Use the information icon and hyperlinks (this color) to link to sources of further information in the

Takuya FUNAHASHI and Hayato YAMANA Computer Science and Engineering Div., Waseda Univ. JAPAN

Takuya FUNAHASHI and Hayato YAMANA (yamana@acm.org) Computer Science and Engineering Div., Waseda Univ. JAPAN 1 Have you ever experienced? 8,870 8 870 tto 2 2,070? 070? Where have 6,800, web pages p g