The Conversational User Interface
|
|
- Molly Stewart
- 6 years ago
- Views:
Transcription
1 The Conversational User Interface Ronald Kaplan Nuance Sunnyvale NL/AI Lab Department of Linguistics, Stanford May, 2013
2 GUI: The problem Extensional 2
3 CUI: The solution Intensional Bobrow et al Nuance Communications, Inc. All rights reserved. ENTERPRISE SOLUTIONS
4 From then to now: Obstacles Typing is unnatural, speech recognition is hard Language is efficient: much is unsaid but understood Rampant ambiguity without context and expectations The chicken seemed ready to eat Precision is tedious: Conversation with a 3-year-old? Language is complex Many overlapping patterns to encode meaning Conversation is a cooperative social activity Speaker/hearer model each other, share conventions, plan and reason You need something worth talking about Detect goals, track environment, determine/execute useful actions 4
5 The opportunity Ubiquitous computing ubiquitous complexity Mass distribution of cost-effective computation confusion (try controlling a TV, thermostat, irrigation clock ) Phone as portal: the illusion of simplicity Universal: Applification of other connected devices Uniform: Same channel for special interactions Personal and situational: preferred and appropriate behavior Cloud infrastructure for shared information and back-end processing Advances on key components: speech, NL, dialog, reasoning Public/defined interfaces to local devices, remote sources and services (Siri: The NL Summer) 5
6 Speech recognition performance Out-of-the-box performance is becoming good and continues to improve rapidly % users with a given error rate % Users experiencing an error rate of < 10% during first-time mobile use Average word error rate 20% 18% 16% 14% 18% Average word error rate reduction per year 10 12% Average word error rate % 10%
7 Recognition research WHAT S CHANGED? 5 years ago Now Training data 1,000 s of hours 100,000 s of hours Algorithms Maximum-likelihood Deep Belief Neural Networks 100 X more computation Computation 1 workstation 10,000 s of cores Run-time GMM likelihoods + Matrix multiplies Nuance Communications, Inc. All rights reserved. ENTERPRISE SOLUTIONS
8 Conversation and Information Ordinary language to describe what you need When will my package arrive? Clarification/repair No, tomorrow Drill-down discussion What are the 15-year rates? Immediate sentiment You lost my luggage! 8
9 Conversation and Action E-commerce Flight to San Diego Mexican restaurants? No, Italian OK, table for 4 at about 7 TV Direct command: Change to channel 5 Standing order: Turn the volume down during ads Thermostat A little cooler in the afternoon Vacation starting Tuesday Customer service Change my address to xxxx. 9
10 A simple conversation A dialog between Bob and a speech-enabled proactive Conversational Assistant (CA) Bob> Book a table at Zingari s after my last meeting and let Tom and Brian know to meet me there. CA> Sorry, but there aren t any tables open until 9pm. Would you like me to find you another Italian restaurant in the area at about 6:30pm? Bob> Can you find a table at a restaurant with a good wine list? CA> Barbacco has an opening. It s in the Financial District but the travel time is about the same. Bob> Ok. That sounds good. 10
11 A simple conversation A dialog between Bob and a speech-enabled proactive Conversational Assistant (CA) Bob> Book a table at Zingari s after my last meeting and let Tom and Brian know to meet me there. CA> Sorry, but there aren t any tables open until 9pm. Would you like me to find you another Italian restaurant in the area at about 6:30pm? Bob> Can you find a table at a restaurant with a good wine list? CA> Barbacco has an opening. It s before in leaving the Financial work District but the travel time is about the same. Bob> Ok. That sounds good. Ambiguous: booking done now or after last meeting Assumption: last meeting today (check calendar) and dinner tonight (tomorrow also meets constraints) Expectation: Bob usually spends 30 minutes on Factor in travel time: 6:30 for reservation Referent for Tom and Brian 11
12 A simple conversation A dialog between Bob and a speech-enabled proactive Conversational Assistant (CA) Bob> Book a table at Zingari s after my last meeting and let Tom and Brian know to meet me there. CA> Sorry, but there aren t any tables open until 9pm. Would you like me to find you another Italian restaurant in the area at about 6:30pm? Bob> Can you find a table at a restaurant with a good wine list? CA> Barbacco has an opening. Informative It s in the explanation, Financial not District just I can t but the travel time is about the same. Bob> Ok. That sounds good. Initial search fails Relax less important constraints, propose an otherwise similar alternative: type of restaurant and table time Expose most salient of remaining constraints 12
13 A simple conversation A dialog between Bob and a speech-enabled proactive Conversational Assistant (MA) Bob> Book a table at Zingari s after my last meeting and let Tom and Semantic search: an Italian restaurant with good wine Brian know to meet me there. CA> Sorry, but there aren t any tables open until 9pm. Would you like me to find you another Italian restaurant in the area at about 6:30pm? Bob> Can you find a table at a restaurant with a good wine list? CA> Barbacco has an opening. It s in the Financial District but the travel time is about the same. Bob> Ok. That sounds good. Indirect speech act: Reserve, not Yes or No Implicit confirmation: No disagreement with exposed constraints Interpretation: A table at an Italian restaurant with a good wine list Sources: Yelp, restaurant websites, maps 13
14 A simple conversation A dialog between Bob and a speech-enabled proactive Conversational Assistant (CA) Bob> Book a table at Zingari s after my last meeting and let Tom and Brian know to meet me there. CA> Sorry, but there aren t any tables in preference open to until others 9pm. Would you like me to find you another Italian restaurant ( same in travel the area time, at Italian, about Tonight ) 6:30pm? Bob> Can you find a table at a restaurant with a good wine list? CA> Barbacco has an opening. It s in the Financial District but the travel time is about the same. Bob> Ok. That sounds good. Drop one of the constraints ( restaurant in the area ) 14
15 A simple conversation A dialog between Bob and a speech-enabled proactive Conversational Assistant (CA) Bob> Book a table at Zingari s after my last meeting and let Tom and Brian know to meet me there. CA> Sorry, but there aren t any tables open until 9pm. Would you like reservations, sends s to Tom and Brian. me to find you another Italian restaurant in the area at about 6:30pm? Bob> Can you find a table at a restaurant with a good wine list? CA> Barbacco has an opening. It s in the Financial District but the travel time is about the same. Bob> Ok. That sounds good. End of Dialog. CA goes to Opentable, makes the Persistence: The duties of a true assistant are not yet complete. It must monitor the plan for unexpected events such as delays. 15
16 Many components, many disciplines Statistical Training & Symbolic Constraints: Data, grammars Input Language Reasoning Speech Recognition Text, Gesture, Biometrics Context Language Comprehension Sentiment Analysis Dialog Manager Speech Acts Task Planner Web Output Speech Synthesis Text, Graphics Language Generation Visual design Collaboration Model User Model Theorem Prover Apps Devices Knowledge Representation, Ontologies, Facts 16
17 Language and reasoning Morphology Syntax Semantics Pragmatics Discourse & Dialog AI and Reasoning Major technical challenges: Integration of independent best-of-breed components Managing end-to-end ambiguity through hard constraints and probabilistic reasoning Bridging language and logic Inferring intent & learning preferences Global resolution of ambiguity while preserving modularity Deployment at scale Modeling collaboration Representing knowledge 17
18 Computational challenge: Pervasive ambiguity Morphology & Syntax Semantics Mentions Every nominee got an award. The same award or each their own? The chicken is ready to eat. Cooked or hungry? walks untieable knot bank General Mills noun or verb? (untie)able or un(tieable)? river or financial? person or company 18
19 Ambiguity can be explosive if alternatives multiply within or across modules Knowledge Semantics Syntax Mentions Speech 19
20 Pruning Premature Disambiguation Typical approach: Local heuristics to kill as soon as possible Oops: Strong constraints may reject the so-far-best (= only) option Statistics Speech X Mentions X Syntax X X Semantics X Knowledge Semantics may know: The veal is ready to eat. The calf is ready to eat. 20
21 Syntactic ambiguity Bob Book a table after my last meeting (LFG/XLE-Web, Bergen) Book Later Book Now, Table Later Statistics and pragmatic reasoning to choose interpretation 21
22 Packing syntactic ambiguity book now, table later shared Book a table after my last meeting book later Interpretation chosen by later modules (pragmatic reasoning and domain statistics) Choice doesn t depend on meeting structure, so never unpacked 22
23 Technical approaches: data + rules Data driven learning by observation Classification and correlation, on the head (current fad) Automatically (?) populates framework of domain concepts and contexts Probabilistic preference and disambiguation Symbolic learning by instruction Interpretation, on the tail Deep, long-span linguistic structures provide statistical locality Less domain dependent Back-offs for robustness Appropriate combination: Trade data for knowledge 23
24 Semantic analysis Bob> Can you find a table at a restaurant with a good wine list? Syntactic structure mapped to logical representation with event tokens, individual objects, properties and relations Davidsonian representation (event variables) supports incremental addition of new constraints by conjunction Discourse Representation Structures (DRS) for ease of manipulation, with translation to first order logic for more general reasoning e1,e2,x,y Surface_request(e1,e2) Agent(e1,Bob), Agent(e2,CA) Find(e2), Restaurant(x), Object(e2,x) Food(x,Italian), Open(x) Available(y,x), Wine(y), Good(y) Discourse structure Logical representation 24
25 Pragmatics Example: Speech acts Bob> Can you find a table at a restaurant with a good wine list? Transform surface speech act (ability to find a table?) into a request to make a reservation e1,e2,x,y Surface_request(e1,e2) Agent(e1,Bob), Agent(e2,CA) Find(e2), Restaurant(x), Object(e2,x) Food(x,Italian), Open(x) Available(y,x), Wine(y), Good(y) e1,e2,x,y Request(e1,e2) Agent(e1,Bob), Agent(e2,CA) Reserve(e2), Restaurant(x), Object(e2,x) Food(x,Italian), Open(x) Available(y,x), Wine(y), Good(y) 25
26 Conversational interaction: Plan and replan Book a table at Zingari s after my last meeting Task recipe library Book table Get restaurant Get restaurant Get Guide Find Reserve Get restaurant Get time Reserve From user Get Guide Find Yelp Get candidates Compare Opentable Book_table(e1) Agent(e1,CA) Object(e1,r), Restaurant(r) Date(d),Time(t) Get_rest(e2) Agent(e2,CA) From_user(e3) r=zingari 26 Get_time(e4) d=12112 t=6:30pm Dynamic Intention Structures Opentable: not available Reserve(e5) Agent(e5,CA) Object(e5,r) Source(e5, Opentable) Available(r,d,t) Select new recipe and elaborate Book_table(e1) Agent(e1,CA),Object(e1,r), Restaurant(r), Has(r,w), wine(w),good(w), Date(d),Time(t) Get_restaurant(e2), Agent(e2,CA) Get_guide(e3) Agent(e3,CA) Object(e3,y) use(e3 ) Agent(e3,CA) Target(e3,y) y=yelp Find(e4), Agent(e4,CA) Object(e4,r) Source(e4,y) Type(italian), Driving(20m) r=barbacco. Get_time(e5) d=12112 t=6:30pm Reserve(e6) Agent(e6,CA) Object(e6,r) Source(e6, Opentable) Available(r,d,t)
27 Proactive monitoring, replan on failure Anticipate glitches, create standing orders If CA comes to believe that Bob hasn t left the office by 5:30 pm, it will form the intention to replan the book-table action CA> Bob, you re running late. Should I change the reservation? Bob> Yes, I ll be ready to leave in about 30 minutes
28 Standing orders Specific constraints on future/hypothetical events: Intensionality Let me know when I get close to a café but not Starbucks Move $1000 to my savings when my paycheck comes in Linguistic pipeline decodes idiosyncratic intent long tail Planner creates future-situation recognizer Monitor watches and initiates action (location, time, bank ) Also: Collaborative help for big-head situations (e.g. Google Now cards) Infer from common interests and repeated patterns of daily life Little/no linguistic analysis Templatic but flexible use of general planning and monitoring User model and context awareness to suppress unwanted intrusions 28
29 Extending across domains Linguistic analysis, conventions of conversation, planning principles remain General vocabulary and grammatical expressions of meaning are (mostly) domain independent I want Can you Later than that No, French Maybe Monday Structured representations can be interpreted according to context Upper ontology and axioms provide stable background People, places, objects, action, time, cause-effect, desire, belief, intention New domain: augment general framework Add/specialize vocabulary and ontology Define constraints and inferences Provide access to domain information sources and execution interfaces Architecture, algorithms, background are language independent 29
30 Conversation: Natural, efficient, effective Universal way of interacting with Ubiquitous technology: Phone, TV, thermostat Information, Institutions, and services (Many) core technologies now exist Challenge of integration, ambiguity Perfection is not required: People misunderstand too Must set appropriate expectations Must provide for easy repair Confirmation is often unnatural A defensive hangover from the errorful past Needed for actions with consequence 30
31 Conversation: The killer app for NL and AI 31
32 32
Grammar Lesson Plan: Yes/No Questions with No Overt Auxiliary Verbs
Grammar Lesson Plan: Yes/No Questions with No Overt Auxiliary Verbs DIALOGUE: Hi Armando. Did you get a new job? No, not yet. Are you still looking? Yes, I am. Have you had any interviews? Yes. At the
More informationAQUA: An Ontology-Driven Question Answering System
AQUA: An Ontology-Driven Question Answering System Maria Vargas-Vera, Enrico Motta and John Domingue Knowledge Media Institute (KMI) The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom.
More informationThe College Board Redesigned SAT Grade 12
A Correlation of, 2017 To the Redesigned SAT Introduction This document demonstrates how myperspectives English Language Arts meets the Reading, Writing and Language and Essay Domains of Redesigned SAT.
More informationApplications of memory-based natural language processing
Applications of memory-based natural language processing Antal van den Bosch and Roser Morante ILK Research Group Tilburg University Prague, June 24, 2007 Current ILK members Principal investigator: Antal
More informationOn Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC
On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these
More informationSome Principles of Automated Natural Language Information Extraction
Some Principles of Automated Natural Language Information Extraction Gregers Koch Department of Computer Science, Copenhagen University DIKU, Universitetsparken 1, DK-2100 Copenhagen, Denmark Abstract
More informationRule-based Expert Systems
Rule-based Expert Systems What is knowledge? is a theoretical or practical understanding of a subject or a domain. is also the sim of what is currently known, and apparently knowledge is power. Those who
More informationObjective: Add decimals using place value strategies, and relate those strategies to a written method.
NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 9 5 1 Lesson 9 Objective: Add decimals using place value strategies, and relate those strategies to a written method. Suggested Lesson Structure Fluency Practice
More informationM55205-Mastering Microsoft Project 2016
M55205-Mastering Microsoft Project 2016 Course Number: M55205 Category: Desktop Applications Duration: 3 days Certification: Exam 70-343 Overview This three-day, instructor-led course is intended for individuals
More informationCS 598 Natural Language Processing
CS 598 Natural Language Processing Natural language is everywhere Natural language is everywhere Natural language is everywhere Natural language is everywhere!"#$%&'&()*+,-./012 34*5665756638/9:;< =>?@ABCDEFGHIJ5KL@
More informationAn Introduction to the Minimalist Program
An Introduction to the Minimalist Program Luke Smith University of Arizona Summer 2016 Some findings of traditional syntax Human languages vary greatly, but digging deeper, they all have distinct commonalities:
More informationFirst Grade Curriculum Highlights: In alignment with the Common Core Standards
First Grade Curriculum Highlights: In alignment with the Common Core Standards ENGLISH LANGUAGE ARTS Foundational Skills Print Concepts Demonstrate understanding of the organization and basic features
More informationTeachers: Use this checklist periodically to keep track of the progress indicators that your learners have displayed.
Teachers: Use this checklist periodically to keep track of the progress indicators that your learners have displayed. Speaking Standard Language Aspect: Purpose and Context Benchmark S1.1 To exit this
More informationRadius STEM Readiness TM
Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and
More informationSegmented Discourse Representation Theory. Dynamic Semantics with Discourse Structure
Introduction Outline : Dynamic Semantics with Discourse Structure pierrel@coli.uni-sb.de Seminar on Computational Models of Discourse, WS 2007-2008 Department of Computational Linguistics & Phonetics Universität
More informationProof Theory for Syntacticians
Department of Linguistics Ohio State University Syntax 2 (Linguistics 602.02) January 5, 2012 Logics for Linguistics Many different kinds of logic are directly applicable to formalizing theories in syntax
More informationSection 7, Unit 4: Sample Student Book Activities for Teaching Listening
Section 7, Unit 4: Sample Student Book Activities for Teaching Listening I. ACTIVITIES TO PRACTICE THE SOUND SYSTEM 1. Listen and Repeat for elementary school students. It could be done as a pre-listening
More informationFoundations of Knowledge Representation in Cyc
Foundations of Knowledge Representation in Cyc Why use logic? CycL Syntax Collections and Individuals (#$isa and #$genls) Microtheories This is an introduction to the foundations of knowledge representation
More informationSpeech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
More informationEye Movements in Speech Technologies: an overview of current research
Eye Movements in Speech Technologies: an overview of current research Mattias Nilsson Department of linguistics and Philology, Uppsala University Box 635, SE-751 26 Uppsala, Sweden Graduate School of Language
More information10 Tips For Using Your Ipad as An AAC Device. A practical guide for parents and professionals
10 Tips For Using Your Ipad as An AAC Device A practical guide for parents and professionals Introduction The ipad continues to provide innovative ways to make communication and language skill development
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationPart I. Figuring out how English works
9 Part I Figuring out how English works 10 Chapter One Interaction and grammar Grammar focus. Tag questions Introduction. How closely do you pay attention to how English is used around you? For example,
More informationTeacher: Mlle PERCHE Maeva High School: Lycée Charles Poncet, Cluses (74) Level: Seconde i.e year old students
I. GENERAL OVERVIEW OF THE PROJECT 2 A) TITLE 2 B) CULTURAL LEARNING AIM 2 C) TASKS 2 D) LINGUISTICS LEARNING AIMS 2 II. GROUP WORK N 1: ROUND ROBIN GROUP WORK 2 A) INTRODUCTION 2 B) TASK BASED PLANNING
More informationVorlesung Mensch-Maschine-Interaktion
Vorlesung Mensch-Maschine-Interaktion Models and Users (1) Ludwig-Maximilians-Universität München LFE Medieninformatik Heinrich Hußmann & Albrecht Schmidt WS2003/2004 http://www.medien.informatik.uni-muenchen.de/
More informationCompositional Semantics
Compositional Semantics CMSC 723 / LING 723 / INST 725 MARINE CARPUAT marine@cs.umd.edu Words, bag of words Sequences Trees Meaning Representing Meaning An important goal of NLP/AI: convert natural language
More informationLoughton School s curriculum evening. 28 th February 2017
Loughton School s curriculum evening 28 th February 2017 Aims of this session Share our approach to teaching writing, reading, SPaG and maths. Share resources, ideas and strategies to support children's
More informationAn Interactive Intelligent Language Tutor Over The Internet
An Interactive Intelligent Language Tutor Over The Internet Trude Heift Linguistics Department and Language Learning Centre Simon Fraser University, B.C. Canada V5A1S6 E-mail: heift@sfu.ca Abstract: This
More informationLanguage Acquisition Chart
Language Acquisition Chart This chart was designed to help teachers better understand the process of second language acquisition. Please use this chart as a resource for learning more about the way people
More informationObjectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition
Chapter 2: The Representation of Knowledge Expert Systems: Principles and Programming, Fourth Edition Objectives Introduce the study of logic Learn the difference between formal logic and informal logic
More informationAP PSYCHOLOGY VACATION WORK PACKET UNIT 7A: MEMORY
AP PSYCHOLOGY VACATION WORK PACKET UNIT 7A: MEMORY You need to complete the following by class on January 3, 2012: Preread the APA Content Standards to anticipate the content of this unit. Read and take
More informationDeveloping Grammar in Context
Developing Grammar in Context intermediate with answers Mark Nettle and Diana Hopkins PUBLISHED BY THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGE The Pitt Building, Trumpington Street, Cambridge, United
More informationThe Strong Minimalist Thesis and Bounded Optimality
The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this
More informationSample Performance Assessment
Page 1 Content Area: Mathematics Grade Level: Six (6) Sample Performance Assessment Instructional Unit Sample: Go Figure! Colorado Academic Standard(s): MA10-GR.6-S.1-GLE.3; MA10-GR.6-S.4-GLE.1 Concepts
More informationIntensive English Program Southwest College
Intensive English Program Southwest College ESOL 0352 Advanced Intermediate Grammar for Foreign Speakers CRN 55661-- Summer 2015 Gulfton Center Room 114 11:00 2:45 Mon. Fri. 3 hours lecture / 2 hours lab
More informationSoftware Maintenance
1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories
More informationFUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria
FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate
More informationNovember 17, 2017 ARIZONA STATE UNIVERSITY. ADDENDUM 3 RFP Digital Integrated Enrollment Support for Students
November 17, 2017 ARIZONA STATE UNIVERSITY ADDENDUM 3 RFP 331801 Digital Integrated Enrollment Support for Students Please note the following answers to questions that were asked prior to the deadline
More informationIntroduction to HPSG. Introduction. Historical Overview. The HPSG architecture. Signature. Linguistic Objects. Descriptions.
to as a linguistic theory to to a member of the family of linguistic frameworks that are called generative grammars a grammar which is formalized to a high degree and thus makes exact predictions about
More informationSpecification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments
Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,
More informationEnglish Language and Applied Linguistics. Module Descriptions 2017/18
English Language and Applied Linguistics Module Descriptions 2017/18 Level I (i.e. 2 nd Yr.) Modules Please be aware that all modules are subject to availability. If you have any questions about the modules,
More informationSEMAFOR: Frame Argument Resolution with Log-Linear Models
SEMAFOR: Frame Argument Resolution with Log-Linear Models Desai Chen or, The Case of the Missing Arguments Nathan Schneider SemEval July 16, 2010 Dipanjan Das School of Computer Science Carnegie Mellon
More informationDIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits.
DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE Sample 2-Year Academic Plan DRAFT Junior Year Summer (Bridge Quarter) Fall Winter Spring MMDP/GAME 124 GAME 310 GAME 318 GAME 330 Introduction to Maya
More informationIntension, Attitude, and Tense Annotation in a High-Fidelity Semantic Representation
Intension, Attitude, and Tense Annotation in a High-Fidelity Semantic Representation Gene Kim and Lenhart Schubert Presented by: Gene Kim April 2017 Project Overview Project: Annotate a large, topically
More informationDepartment of Anthropology ANTH 1027A/001: Introduction to Linguistics Dr. Olga Kharytonava Course Outline Fall 2017
Department of Anthropology ANTH 1027A/001: Introduction to Linguistics Dr. Olga Kharytonava Course Outline Fall 2017 Lectures: Tuesdays 11:30 am - 1:30 pm, SEB-1059 Tutorials: Thursdays: Section 002 2:30-3:30pm
More informationReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology
ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology Tiancheng Zhao CMU-LTI-16-006 Language Technologies Institute School of Computer Science Carnegie Mellon
More informationKnowledge-Based - Systems
Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University
More information1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature
1 st Grade Curriculum Map Common Core Standards Language Arts 2013 2014 1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature Key Ideas and Details
More informationMASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE
Master of Science (M.S.) Major in Computer Science 1 MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Major Program The programs in computer science are designed to prepare students for doctoral research,
More informationArizona s English Language Arts Standards th Grade ARIZONA DEPARTMENT OF EDUCATION HIGH ACADEMIC STANDARDS FOR STUDENTS
Arizona s English Language Arts Standards 11-12th Grade ARIZONA DEPARTMENT OF EDUCATION HIGH ACADEMIC STANDARDS FOR STUDENTS 11 th -12 th Grade Overview Arizona s English Language Arts Standards work together
More informationGuidelines for Writing an Internship Report
Guidelines for Writing an Internship Report Master of Commerce (MCOM) Program Bahauddin Zakariya University, Multan Table of Contents Table of Contents... 2 1. Introduction.... 3 2. The Required Components
More informationCalifornia Department of Education English Language Development Standards for Grade 8
Section 1: Goal, Critical Principles, and Overview Goal: English learners read, analyze, interpret, and create a variety of literary and informational text types. They develop an understanding of how language
More informationA Pipelined Approach for Iterative Software Process Model
A Pipelined Approach for Iterative Software Process Model Ms.Prasanthi E R, Ms.Aparna Rathi, Ms.Vardhani J P, Mr.Vivek Krishna Electronics and Radar Development Establishment C V Raman Nagar, Bangalore-560093,
More informationHoughton Mifflin Reading Correlation to the Common Core Standards for English Language Arts (Grade1)
Houghton Mifflin Reading Correlation to the Standards for English Language Arts (Grade1) 8.3 JOHNNY APPLESEED Biography TARGET SKILLS: 8.3 Johnny Appleseed Phonemic Awareness Phonics Comprehension Vocabulary
More informationLet's Learn English Lesson Plan
Let's Learn English Lesson Plan Introduction: Let's Learn English lesson plans are based on the CALLA approach. See the end of each lesson for more information and resources on teaching with the CALLA
More informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationCitrine Informatics. The Latest from Citrine. Citrine Informatics. The data analytics platform for the physical world
Citrine Informatics The data analytics platform for the physical world The Latest from Citrine Summit on Data and Analytics for Materials Research 31 October 2016 Our Mission is Simple Add as much value
More informationPROCESS USE CASES: USE CASES IDENTIFICATION
International Conference on Enterprise Information Systems, ICEIS 2007, Volume EIS June 12-16, 2007, Funchal, Portugal. PROCESS USE CASES: USE CASES IDENTIFICATION Pedro Valente, Paulo N. M. Sampaio Distributed
More informationCHAPTER IV RESEARCH FINDING AND DISCUSSION
CHAPTER IV RESEARCH FINDING AND DISCUSSION In this chapter, the writer presents research finding and discussion. In this chapter the writer presents the answer of problem statements that contained in the
More informationA MULTI-AGENT SYSTEM FOR A DISTANCE SUPPORT IN EDUCATIONAL ROBOTICS
A MULTI-AGENT SYSTEM FOR A DISTANCE SUPPORT IN EDUCATIONAL ROBOTICS Sébastien GEORGE Christophe DESPRES Laboratoire d Informatique de l Université du Maine Avenue René Laennec, 72085 Le Mans Cedex 9, France
More informationKindergarten Lessons for Unit 7: On The Move Me on the Map By Joan Sweeney
Kindergarten Lessons for Unit 7: On The Move Me on the Map By Joan Sweeney Aligned with the Common Core State Standards in Reading, Speaking & Listening, and Language Written & Prepared for: Baltimore
More informationGrade Band: High School Unit 1 Unit Target: Government Unit Topic: The Constitution and Me. What Is the Constitution? The United States Government
The Constitution and Me This unit is based on a Social Studies Government topic. Students are introduced to the basic components of the U.S. Constitution, including the way the U.S. government was started
More informationParsing of part-of-speech tagged Assamese Texts
IJCSI International Journal of Computer Science Issues, Vol. 6, No. 1, 2009 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 28 Parsing of part-of-speech tagged Assamese Texts Mirzanur Rahman 1, Sufal
More informationPAGE(S) WHERE TAUGHT If sub mission ins not a book, cite appropriate location(s))
Ohio Academic Content Standards Grade Level Indicators (Grade 11) A. ACQUISITION OF VOCABULARY Students acquire vocabulary through exposure to language-rich situations, such as reading books and other
More informationGERM 3040 GERMAN GRAMMAR AND COMPOSITION SPRING 2017
GERM 3040 GERMAN GRAMMAR AND COMPOSITION SPRING 2017 Instructor: Dr. Claudia Schwabe Class hours: TR 9:00-10:15 p.m. claudia.schwabe@usu.edu Class room: Old Main 301 Office: Old Main 002D Office hours:
More informationProcess improvement, The Agile Way! By Ben Linders Published in Methods and Tools, winter
Process improvement, The Agile Way! By Ben Linders Published in Methods and Tools, winter 2010. http://www.methodsandtools.com/ Summary Business needs for process improvement projects are changing. Organizations
More informationTelekooperation Seminar
Telekooperation Seminar 3 CP, SoSe 2017 Nikolaos Alexopoulos, Rolf Egert. {alexopoulos,egert}@tk.tu-darmstadt.de based on slides by Dr. Leonardo Martucci and Florian Volk General Information What? Read
More informationNAME: East Carolina University PSYC Developmental Psychology Dr. Eppler & Dr. Ironsmith
Module 10 1 NAME: East Carolina University PSYC 3206 -- Developmental Psychology Dr. Eppler & Dr. Ironsmith Study Questions for Chapter 10: Language and Education Sigelman & Rider (2009). Life-span human
More informationADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF
Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationDiploma in Library and Information Science (Part-Time) - SH220
Diploma in Library and Information Science (Part-Time) - SH220 1. Objectives The Diploma in Library and Information Science programme aims to prepare students for professional work in librarianship. The
More informationUsing dialogue context to improve parsing performance in dialogue systems
Using dialogue context to improve parsing performance in dialogue systems Ivan Meza-Ruiz and Oliver Lemon School of Informatics, Edinburgh University 2 Buccleuch Place, Edinburgh I.V.Meza-Ruiz@sms.ed.ac.uk,
More informationChamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform
Chamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform doi:10.3991/ijac.v3i3.1364 Jean-Marie Maes University College Ghent, Ghent, Belgium Abstract Dokeos used to be one of
More informationSOFTWARE EVALUATION TOOL
SOFTWARE EVALUATION TOOL Kyle Higgins Randall Boone University of Nevada Las Vegas rboone@unlv.nevada.edu Higgins@unlv.nevada.edu N.B. This form has not been fully validated and is still in development.
More informationCX 101/201/301 Latin Language and Literature 2015/16
The University of Warwick Department of Classics and Ancient History CX 101/201/301 Latin Language and Literature 2015/16 Module tutor: Clive Letchford Humanities Building 2.21 c.a.letchford@warwick.ac.uk
More informationLanguage Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus
Language Acquisition Fall 2010/Winter 2011 Lexical Categories Afra Alishahi, Heiner Drenhaus Computational Linguistics and Phonetics Saarland University Children s Sensitivity to Lexical Categories Look,
More informationGrade 11 Language Arts (2 Semester Course) CURRICULUM. Course Description ENGLISH 11 (2 Semester Course) Duration: 2 Semesters Prerequisite: None
Grade 11 Language Arts (2 Semester Course) CURRICULUM Course Description ENGLISH 11 (2 Semester Course) Duration: 2 Semesters Prerequisite: None Through the integrated study of literature, composition,
More informationDigital Fabrication and Aunt Sarah: Enabling Quadratic Explorations via Technology. Michael L. Connell University of Houston - Downtown
Digital Fabrication and Aunt Sarah: Enabling Quadratic Explorations via Technology Michael L. Connell University of Houston - Downtown Sergei Abramovich State University of New York at Potsdam Introduction
More informationOrganizing Comprehensive Literacy Assessment: How to Get Started
Organizing Comprehensive Assessment: How to Get Started September 9 & 16, 2009 Questions to Consider How do you design individualized, comprehensive instruction? How can you determine where to begin instruction?
More informationSemi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More informationTarget Language Preposition Selection an Experiment with Transformation-Based Learning and Aligned Bilingual Data
Target Language Preposition Selection an Experiment with Transformation-Based Learning and Aligned Bilingual Data Ebba Gustavii Department of Linguistics and Philology, Uppsala University, Sweden ebbag@stp.ling.uu.se
More informationLITERACY ACROSS THE CURRICULUM POLICY
"Pupils should be taught in all subjects to express themselves correctly and appropriately and to read accurately and with understanding." QCA Use of Language across the Curriculum "Thomas Estley Community
More informationMAILCOM Las Vegas. October 2-4, Senior Director, Proposal Management BrightKey, Inc.
MAILCOM Las Vegas October 2-4, 2017 CRS#: LD250 Session: Mystery Solved! Cracking the Case on Productivity Day/Date: Tuesday, October 3, 2017 Round/Time: Round 5, 11:30am-12:30pm Presented By: Sally S.
More informationCEFR Overall Illustrative English Proficiency Scales
CEFR Overall Illustrative English Proficiency s CEFR CEFR OVERALL ORAL PRODUCTION Has a good command of idiomatic expressions and colloquialisms with awareness of connotative levels of meaning. Can convey
More informationAndroid App Development for Beginners
Description Android App Development for Beginners DEVELOP ANDROID APPLICATIONS Learning basics skills and all you need to know to make successful Android Apps. This course is designed for students who
More informationSkillsoft Acquires SumTotal: Frequently Asked Questions. October 2014
Skillsoft Acquires SumTotal: Frequently Asked Questions October 2014 1. What have we announced? Skillsoft has completed the previously announced acquisition of SumTotal. Skillsoft s acquisition of SumTotal
More informationPHILOSOPHY & CULTURE Syllabus
PHILOSOPHY & CULTURE Syllabus PHIL 1050 FALL 2013 MWF 10:00-10:50 ADM 218 Dr. Seth Holtzman office: 308 Administration Bldg phones: 637-4229 office; 636-8626 home hours: MWF 3-5; T 11-12 if no meeting;
More information1. Answer the questions below on the Lesson Planning Response Document.
Module for Lateral Entry Teachers Lesson Planning Introductory Information about Understanding by Design (UbD) (Sources: Wiggins, G. & McTighte, J. (2005). Understanding by design. Alexandria, VA: ASCD.;
More informationAGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016
AGENDA Advanced Learning Theories Alejandra J. Magana, Ph.D. admagana@purdue.edu Introduction to Learning Theories Role of Learning Theories and Frameworks Learning Design Research Design Dual Coding Theory
More informationSpeak Up 2012 Grades 9 12
2012 Speak Up Survey District: WAYLAND PUBLIC SCHOOLS Speak Up 2012 Grades 9 12 Results based on 130 survey(s). Note: Survey responses are based upon the number of individuals that responded to the specific
More information5. UPPER INTERMEDIATE
Triolearn General Programmes adapt the standards and the Qualifications of Common European Framework of Reference (CEFR) and Cambridge ESOL. It is designed to be compatible to the local and the regional
More informationThis table contains the extended descriptors for Active Learning on the Technology Integration Matrix (TIM).
TIM: Active Learning This table contains the extended descriptors for Active Learning on the Technology Integration Matrix (TIM). The Active attribute makes the distinction between lessons in which students
More informationConstraining X-Bar: Theta Theory
Constraining X-Bar: Theta Theory Carnie, 2013, chapter 8 Kofi K. Saah 1 Learning objectives Distinguish between thematic relation and theta role. Identify the thematic relations agent, theme, goal, source,
More informationSyntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm
Syntax Parsing 1. Grammars and parsing 2. Top-down and bottom-up parsing 3. Chart parsers 4. Bottom-up chart parsing 5. The Earley Algorithm syntax: from the Greek syntaxis, meaning setting out together
More informationUnderstanding and Changing Habits
Understanding and Changing Habits We are what we repeatedly do. Excellence, then, is not an act, but a habit. Aristotle Have you ever stopped to think about your habits or how they impact your daily life?
More informationOntological spine, localization and multilingual access
Start Ontological spine, localization and multilingual access Some reflections and a proposal New Perspectives on Subject Indexing and Classification in an International Context International Symposium
More informationENGBG1 ENGBL1 Campus Linguistics. Meeting 2. Chapter 7 (Morphology) and chapter 9 (Syntax) Pia Sundqvist
Meeting 2 Chapter 7 (Morphology) and chapter 9 (Syntax) Today s agenda Repetition of meeting 1 Mini-lecture on morphology Seminar on chapter 7, worksheet Mini-lecture on syntax Seminar on chapter 9, worksheet
More informationTIM: Table of Summary Descriptors This table contains the summary descriptors for each cell of the Technology Integration Matrix (TIM).
TIM: Table of Summary Descriptors This table contains the summary descriptors for each cell of the Technology Integration Matrix (TIM). The Technology Integration Matrix (TIM) provides a framework for
More informationa) analyse sentences, so you know what s going on and how to use that information to help you find the answer.
Tip Sheet I m going to show you how to deal with ten of the most typical aspects of English grammar that are tested on the CAE Use of English paper, part 4. Of course, there are many other grammar points
More informationA Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
More information