H halting problem, 179. homomorphism, 154

Size: px
Start display at page:

Download "H halting problem, 179. homomorphism, 154"

Transcription

1 215 A accept, 23, 28, 120, 132, 160 acceptance by empty stack, 127 acceptance by final states, 128 accepting state, 23 algorithm, 178, 179 alphabet, 4 ambiguous, 99 automaton, 1 B Backus normal form, 84 Backus-Naur form, 84 basis, 10 binary relation, 9 Boolean operation, 73 bottom-up parsing, 98 bottom-up parsing, 143 boundary symbol, 157 C cell, 24, 157 Chomsky hierarchy, 169 Chomsky normal form: CNF, 105 Church s hypothesis, 171 CKY algorithm, CKY 138 class, 8 closure, 58 closure property, 73 comparison tree, 33 complement, 7 computable, 171 concatenation, 3, 57 configuration, 118, 125, 159 context-free grammar: CFG, 93 context-free language: CFL, 95 context-sensitive grammar: CSG, 166 context-sensitive language: CSL, 166 D De Morgan s law, 7 dead state, 43 dead symbol, 100 decidable, 179 decision algorithm, 179 decision problem, 179 derivation diagram, 167 derivation tree, 96 derive, 87, 94 deterministic finite automaton: DFA, 24 deterministic language: DL, 129 deterministic pushdown automaton: DPDA, 124 deterministic pushdown transducer: DPDT, 131 difference, 7 direct product, Cartesian product, 7, 176 directly derive, 86, 94, 155 directly left-recursive, 107 dynamic programming: DP, 138 著作権 : 森北出版株式会社

2 216 E edge, 9 edge, 9 effective procedure, 178 element, 5 emptiness problem, 102 empty, 30 empty sequence, 3 empty set, 5 empty string, 3 endmarker symbol, 123 enumerate, 169 ε-closure, ε- 52 ε-free, ε- 102 ε-mode, ε- 126 ε-move, ε- 50 ε-production, ε- 102 ε-rule, ε- 126 ε-transition, ε- 50, 126 equivalence relation, 9 equivalent, 22, 29, 61, 88 F family, 8 family of languages, 72 final state, 23 finer, 41 finite automaton: FA, 24 finite set, 5 finite state automaton: FSA, 24 finite state language, 28 formal grammar, 82 formal language, 2 G generalized nondeterministic finite automaton: GNFA, 66 generate, 87 Greibach normal form: GNF, 107 H halting problem, 179 handle, 143 height, 118, 125 Hilbert s tenth problem, homomorphism, 154 I induction, inductive proof, 10 inductive conclusion, 11 inductive hypothesis, 10 inductive step, 10 infinite set, 5 inherently ambiguous, 99 initial configuration, 118, 125, 158 initial instantaneous description, 158 initial stack symbol, 118 initial state, 14, 23 initial symbol, 82 input, 1, 14 input sequence, 2 input string, 2 input symbol, 2 instance, 178 instance, 178 instantaneous description: ID, 159 internal node, 10 internal state, 14 intersection, 7 isomorphic, 36 K k-equivalent, k- 37 Kleene closure, 58 L labeled tree, 9 language, 4 language accepted, 28

3 217 language generated, 87 language over Σ, Σ 4 language recognized, 28 leaf, 10 left-linear grammar, 91 left-recursive, 107 leftmost derivation, 94 length, 3, 10 lexical analyzer, 14 linear bounded automaton: LBA, 168 linear bounded automaton: LBA, 168 live symbol, 100 LL(k) grammar, LL(k) 142 LR(k) grammar, LR(k) 144 M mathematical language, 2 Mealy machine, 15 membership problem, 136 minimal form, reduced form, 36 mode, 125 monotonic grammar, 166 Moore machine, 17 move, 119, 126, 158 multitape TM, 161 mutually disjoint, 7 Myhill-Nerode s theorem, 41 N natural language, 2 next move function, 158 node, vertex, 9 non-context-free language, 148 nondeterministic finite automaton with ε-moves, ε- 51 nondeterministic finite automaton: NFA, 45 nondeterministic pushdown automaton: NPDA, 131 nondeterministic TM, 162 non-regular language, 74 nonterminal symbol, 82 nullable symbol, 102 O offspring, son, 10 one-way, 25 output, 1, 14 output function, 15, 17 P parent, father, 10 parse tree, 97 parsing, syntax analysis, 98, 137 partially computable, 171 path, 10 pattern automaton, 76 pattern matching, 76 pattern matching, 76 phrase structure grammar: PSG, 155 phrase structure language: PSL, 156 pop up, 117 positive star closure, 59 Post s Correspondence Problem: PCP, 181 power set, 8 prefix, 3 prefix property, 122 procedure, 178 product, 7 production, 82 programming language, 2 proper, 3 Pumping Theorem, 114 Pumping Theorem, 113 push down, 117

4 218 pushdown stack, 116 pushdown symbol, 117 pushdown tape, 117 R reachable, 30, 101 reading mode, 126 real-time, 126 recognizer, 23 recursive, 169 recursive, 107 recursively enumerable, 168 reduce, 143 reduced, 104 reduction, 182 reflexive law, 9 regular expression, 60 regular expression, 60 regular grammar: RG, 85 regular grammar: RG, 85 regular language: RL, 28 regular language: RL, 28 regular set, 60 regular set, 60 reject, 28 reversal, 121 rewriting rule, 82 right invariant, 40 right-linear grammar, 90 right-recursive, 107 rightmost derivation, 95, 144 root, 9 S self-embedding, 112 sentence, 2, 82, 87, 95, 156 sentential form, 87, 94, 156 sequence, 3 sequential machine, 14 set, 5 simple, 130 simple deterministic grammar, 115 simple deterministic language: SDL, 115, 122 simple deterministic pushdown automaton: simple DPDA, 118 simple language, 122 simplification, 23 solvable, 136, 179 stack, 117 stack symbol, 117 star closure, 3, 58 start symbol, 82 state, 14 state transition, 14, 26 state transition diagram, 16, 17, 25, 45 state transition function, 15 state transition table, 15, 17, 25, 46 strict, 127 string, 3 string over Σ, Σ 3 subset, 6 subset construction, 48 substring, 3 subtree, 10 suffix, 3 symmetric law, 9 syntactic structure, 97 syntactic variable, 82 T terminal configuration, 159 terminal symbol, 82 TM with a one-way infinite tape, 161 top-down parsing, 98 top-down parsing, 142 transducer, 23 transition diagram, 120 transition rule, 118 transition, derivation, 119, 126, 127 transitive law, 9

5 219 tree, 9 Turing machine: TM, 157 type 0 grammar, type 1 grammar, type 2 grammar, 2 93 type 3 grammar, 3 85 U unambiguous, 99 undecidable, 179 union, 6 unit production, single production, 103 universal set, 7 universal set, 7 universal Turing machine: UTM, 171, 174 unsolvable, 179 useless symbol, 100 uvwxy-theorem, uvwxy- 114 V Venn diagram, 8 W word, 4 Y Yes-No problem, Yes-No 178

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 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 information

Grammars & Parsing, Part 1:

Grammars & Parsing, Part 1: Grammars & Parsing, Part 1: Rules, representations, and transformations- oh my! Sentence VP The teacher Verb gave the lecture 2015-02-12 CS 562/662: Natural Language Processing Game plan for today: Review

More information

Language properties and Grammar of Parallel and Series Parallel Languages

Language properties and Grammar of Parallel and Series Parallel Languages arxiv:1711.01799v1 [cs.fl] 6 Nov 2017 Language properties and Grammar of Parallel and Series Parallel Languages Mohana.N 1, Kalyani Desikan 2 and V.Rajkumar Dare 3 1 Division of Mathematics, School of

More information

RANKING AND UNRANKING LEFT SZILARD LANGUAGES. Erkki Mäkinen DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TAMPERE REPORT A ER E P S I M S

RANKING AND UNRANKING LEFT SZILARD LANGUAGES. Erkki Mäkinen DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TAMPERE REPORT A ER E P S I M S N S ER E P S I M TA S UN A I S I T VER RANKING AND UNRANKING LEFT SZILARD LANGUAGES Erkki Mäkinen DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF TAMPERE REPORT A-1997-2 UNIVERSITY OF TAMPERE DEPARTMENT OF

More information

A Version Space Approach to Learning Context-free Grammars

A Version Space Approach to Learning Context-free Grammars Machine Learning 2: 39~74, 1987 1987 Kluwer Academic Publishers, Boston - Manufactured in The Netherlands A Version Space Approach to Learning Context-free Grammars KURT VANLEHN (VANLEHN@A.PSY.CMU.EDU)

More information

A R "! I,,, !~ii ii! A ow ' r.-ii ' i ' JA' V5, 9. MiN, ;

A R ! I,,, !~ii ii! A ow ' r.-ii ' i ' JA' V5, 9. MiN, ; A R "! I,,, r.-ii ' i '!~ii ii! A ow ' I % i o,... V. 4..... JA' i,.. Al V5, 9 MiN, ; Logic and Language Models for Computer Science Logic and Language Models for Computer Science HENRY HAMBURGER George

More information

Informatics 2A: Language Complexity and the. Inf2A: Chomsky Hierarchy

Informatics 2A: Language Complexity and the. Inf2A: Chomsky Hierarchy Informatics 2A: Language Complexity and the Chomsky Hierarchy September 28, 2010 Starter 1 Is there a finite state machine that recognises all those strings s from the alphabet {a, b} where the difference

More information

Enumeration of Context-Free Languages and Related Structures

Enumeration of Context-Free Languages and Related Structures Enumeration of Context-Free Languages and Related Structures Michael Domaratzki Jodrey School of Computer Science, Acadia University Wolfville, NS B4P 2R6 Canada Alexander Okhotin Department of Mathematics,

More information

A General Class of Noncontext Free Grammars Generating Context Free Languages

A General Class of Noncontext Free Grammars Generating Context Free Languages INFORMATION AND CONTROL 43, 187-194 (1979) A General Class of Noncontext Free Grammars Generating Context Free Languages SARWAN K. AGGARWAL Boeing Wichita Company, Wichita, Kansas 67210 AND JAMES A. HEINEN

More information

Proof Theory for Syntacticians

Proof 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 information

Basic Parsing with Context-Free Grammars. Some slides adapted from Julia Hirschberg and Dan Jurafsky 1

Basic Parsing with Context-Free Grammars. Some slides adapted from Julia Hirschberg and Dan Jurafsky 1 Basic Parsing with Context-Free Grammars Some slides adapted from Julia Hirschberg and Dan Jurafsky 1 Announcements HW 2 to go out today. Next Tuesday most important for background to assignment Sign up

More information

CS 598 Natural Language Processing

CS 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 information

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR ROLAND HAUSSER Institut für Deutsche Philologie Ludwig-Maximilians Universität München München, West Germany 1. CHOICE OF A PRIMITIVE OPERATION The

More information

"f TOPIC =T COMP COMP... OBJ

f TOPIC =T COMP COMP... OBJ TREATMENT OF LONG DISTANCE DEPENDENCIES IN LFG AND TAG: FUNCTIONAL UNCERTAINTY IN LFG IS A COROLLARY IN TAG" Aravind K. Joshi Dept. of Computer & Information Science University of Pennsylvania Philadelphia,

More information

Erkki Mäkinen State change languages as homomorphic images of Szilard languages

Erkki Mäkinen State change languages as homomorphic images of Szilard languages Erkki Mäkinen State change languages as homomorphic images of Szilard languages UNIVERSITY OF TAMPERE SCHOOL OF INFORMATION SCIENCES REPORTS IN INFORMATION SCIENCES 48 TAMPERE 2016 UNIVERSITY OF TAMPERE

More information

Objectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition

Objectives. 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 information

Probability and Game Theory Course Syllabus

Probability and Game Theory Course Syllabus Probability and Game Theory Course Syllabus DATE ACTIVITY CONCEPT Sunday Learn names; introduction to course, introduce the Battle of the Bismarck Sea as a 2-person zero-sum game. Monday Day 1 Pre-test

More information

11/29/2010. Statistical Parsing. Statistical Parsing. Simple PCFG for ATIS English. Syntactic Disambiguation

11/29/2010. Statistical Parsing. Statistical Parsing. Simple PCFG for ATIS English. Syntactic Disambiguation tatistical Parsing (Following slides are modified from Prof. Raymond Mooney s slides.) tatistical Parsing tatistical parsing uses a probabilistic model of syntax in order to assign probabilities to each

More information

On the Polynomial Degree of Minterm-Cyclic Functions

On the Polynomial Degree of Minterm-Cyclic Functions On the Polynomial Degree of Minterm-Cyclic Functions Edward L. Talmage Advisor: Amit Chakrabarti May 31, 2012 ABSTRACT When evaluating Boolean functions, each bit of input that must be checked is costly,

More information

PRODUCT PLATFORM DESIGN: A GRAPH GRAMMAR APPROACH

PRODUCT PLATFORM DESIGN: A GRAPH GRAMMAR APPROACH Proceedings of DETC 99: 1999 ASME Design Engineering Technical Conferences September 12-16, 1999, Las Vegas, Nevada DETC99/DTM-8762 PRODUCT PLATFORM DESIGN: A GRAPH GRAMMAR APPROACH Zahed Siddique Graduate

More information

Parsing of part-of-speech tagged Assamese Texts

Parsing 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 information

GACE Computer Science Assessment Test at a Glance

GACE Computer Science Assessment Test at a Glance GACE Computer Science Assessment Test at a Glance Updated May 2017 See the GACE Computer Science Assessment Study Companion for practice questions and preparation resources. Assessment Name Computer Science

More information

Natural Language Processing. George Konidaris

Natural Language Processing. George Konidaris Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans

More information

Refining the Design of a Contracting Finite-State Dependency Parser

Refining the Design of a Contracting Finite-State Dependency Parser Refining the Design of a Contracting Finite-State Dependency Parser Anssi Yli-Jyrä and Jussi Piitulainen and Atro Voutilainen The Department of Modern Languages PO Box 3 00014 University of Helsinki {anssi.yli-jyra,jussi.piitulainen,atro.voutilainen}@helsinki.fi

More information

WSU Five-Year Program Review Self-Study Cover Page

WSU Five-Year Program Review Self-Study Cover Page WSU Five-Year Program Review Self-Study Cover Page Department: Program: Computer Science Computer Science AS/BS Semester Submitted: Spring 2012 Self-Study Team Chair: External to the University but within

More information

The Interface between Phrasal and Functional Constraints

The Interface between Phrasal and Functional Constraints The Interface between Phrasal and Functional Constraints John T. Maxwell III* Xerox Palo Alto Research Center Ronald M. Kaplan t Xerox Palo Alto Research Center Many modern grammatical formalisms divide

More information

ENGBG1 ENGBL1 Campus Linguistics. Meeting 2. Chapter 7 (Morphology) and chapter 9 (Syntax) Pia Sundqvist

ENGBG1 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 information

systems have been developed that are well-suited to phenomena in but is properly contained in the indexed languages. We give a

systems have been developed that are well-suited to phenomena in but is properly contained in the indexed languages. We give a J. LOGIC PROGRAMMING 1993:12:1{199 1 STRING VARIABLE GRAMMAR: A LOGIC GRAMMAR FORMALISM FOR THE BIOLOGICAL LANGUAGE OF DNA DAVID B. SEARLS > Building upon Denite Clause Grammar (DCG), a number of logic

More information

Hyperedge Replacement and Nonprojective Dependency Structures

Hyperedge Replacement and Nonprojective Dependency Structures Hyperedge Replacement and Nonprojective Dependency Structures Daniel Bauer and Owen Rambow Columbia University New York, NY 10027, USA {bauer,rambow}@cs.columbia.edu Abstract Synchronous Hyperedge Replacement

More information

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words, A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994

More information

Radius STEM Readiness TM

Radius 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 information

Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank

Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank Dan Klein and Christopher D. Manning Computer Science Department Stanford University Stanford,

More information

Discriminative Learning of Beam-Search Heuristics for Planning

Discriminative Learning of Beam-Search Heuristics for Planning Discriminative Learning of Beam-Search Heuristics for Planning Yuehua Xu School of EECS Oregon State University Corvallis,OR 97331 xuyu@eecs.oregonstate.edu Alan Fern School of EECS Oregon State University

More information

Learning goal-oriented strategies in problem solving

Learning goal-oriented strategies in problem solving Learning goal-oriented strategies in problem solving Martin Možina, Timotej Lazar, Ivan Bratko Faculty of Computer and Information Science University of Ljubljana, Ljubljana, Slovenia Abstract The need

More information

Context Free Grammars. Many slides from Michael Collins

Context Free Grammars. Many slides from Michael Collins Context Free Grammars Many slides from Michael Collins Overview I An introduction to the parsing problem I Context free grammars I A brief(!) sketch of the syntax of English I Examples of ambiguous structures

More information

Lecture 1: Machine Learning Basics

Lecture 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 information

An Introduction to the Minimalist Program

An 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 information

Content Language Objectives (CLOs) August 2012, H. Butts & G. De Anda

Content Language Objectives (CLOs) August 2012, H. Butts & G. De Anda Content Language Objectives (CLOs) Outcomes Identify the evolution of the CLO Identify the components of the CLO Understand how the CLO helps provide all students the opportunity to access the rigor of

More information

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,

More information

Improved Reordering for Shallow-n Grammar based Hierarchical Phrase-based Translation

Improved Reordering for Shallow-n Grammar based Hierarchical Phrase-based Translation Improved Reordering for Shallow-n Grammar based Hierarchical Phrase-based Translation Baskaran Sankaran and Anoop Sarkar School of Computing Science Simon Fraser University Burnaby BC. Canada {baskaran,

More information

Parallel Evaluation in Stratal OT * Adam Baker University of Arizona

Parallel Evaluation in Stratal OT * Adam Baker University of Arizona Parallel Evaluation in Stratal OT * Adam Baker University of Arizona tabaker@u.arizona.edu 1.0. Introduction The model of Stratal OT presented by Kiparsky (forthcoming), has not and will not prove uncontroversial

More information

Language Evolution, Metasyntactically. First International Workshop on Bidirectional Transformations (BX 2012)

Language Evolution, Metasyntactically. First International Workshop on Bidirectional Transformations (BX 2012) Language Evolution, Metasyntactically First International Workshop on Bidirectional Transformations (BX 2012) Vadim Zaytsev, SWAT, CWI 2012 Introduction Every language document employs its own We focus

More information

ABSTRACT. A major goal of human genetics is the discovery and validation of genetic polymorphisms

ABSTRACT. A major goal of human genetics is the discovery and validation of genetic polymorphisms ABSTRACT DEODHAR, SUSHAMNA DEODHAR. Using Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions in Genetic Epidemiology. (Under the direction of Dr. Alison Motsinger-Reif.) A major

More information

(Sub)Gradient Descent

(Sub)Gradient Descent (Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

More information

Morphotactics as Tier-Based Strictly Local Dependencies

Morphotactics as Tier-Based Strictly Local Dependencies Morphotactics as Tier-Based Strictly Local Dependencies Alëna Aksënova, Thomas Graf, and Sedigheh Moradi Stony Brook University SIGMORPHON 14 Berlin, Germany 11. August 2016 Our goal Received view Recent

More information

Chapter 2 Rule Learning in a Nutshell

Chapter 2 Rule Learning in a Nutshell Chapter 2 Rule Learning in a Nutshell This chapter gives a brief overview of inductive rule learning and may therefore serve as a guide through the rest of the book. Later chapters will expand upon the

More information

A Grammar for Battle Management Language

A Grammar for Battle Management Language Bastian Haarmann 1 Dr. Ulrich Schade 1 Dr. Michael R. Hieb 2 1 Fraunhofer Institute for Communication, Information Processing and Ergonomics 2 George Mason University bastian.haarmann@fkie.fraunhofer.de

More information

Lecture 10: Reinforcement Learning

Lecture 10: Reinforcement Learning Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

Some Principles of Automated Natural Language Information Extraction

Some 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 information

Approaches to control phenomena handout Obligatory control and morphological case: Icelandic and Basque

Approaches to control phenomena handout Obligatory control and morphological case: Icelandic and Basque Approaches to control phenomena handout 6 5.4 Obligatory control and morphological case: Icelandic and Basque Icelandinc quirky case (displaying properties of both structural and inherent case: lexically

More information

Self Study Report Computer Science

Self Study Report Computer Science Computer Science undergraduate students have access to undergraduate teaching, and general computing facilities in three buildings. Two large classrooms are housed in the Davis Centre, which hold about

More information

Inleiding Taalkunde. Docent: Paola Monachesi. Blok 4, 2001/ Syntax 2. 2 Phrases and constituent structure 2. 3 A minigrammar of Italian 3

Inleiding Taalkunde. Docent: Paola Monachesi. Blok 4, 2001/ Syntax 2. 2 Phrases and constituent structure 2. 3 A minigrammar of Italian 3 Inleiding Taalkunde Docent: Paola Monachesi Blok 4, 2001/2002 Contents 1 Syntax 2 2 Phrases and constituent structure 2 3 A minigrammar of Italian 3 4 Trees 3 5 Developing an Italian lexicon 4 6 S(emantic)-selection

More information

Lecture 1: Basic Concepts of Machine Learning

Lecture 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 information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Ch 2 Test Remediation Work Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) High temperatures in a certain

More information

University of Groningen. Systemen, planning, netwerken Bosman, Aart

University of Groningen. Systemen, planning, netwerken Bosman, Aart University of Groningen Systemen, planning, netwerken Bosman, Aart IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document

More information

Pre-Processing MRSes

Pre-Processing MRSes Pre-Processing MRSes Tore Bruland Norwegian University of Science and Technology Department of Computer and Information Science torebrul@idi.ntnu.no Abstract We are in the process of creating a pipeline

More information

Liquid Narrative Group Technical Report Number

Liquid Narrative Group Technical Report Number http://liquidnarrative.csc.ncsu.edu/pubs/tr04-004.pdf NC STATE UNIVERSITY_ Liquid Narrative Group Technical Report Number 04-004 Equivalence between Narrative Mediation and Branching Story Graphs Mark

More information

arxiv: v1 [math.at] 10 Jan 2016

arxiv: v1 [math.at] 10 Jan 2016 THE ALGEBRAIC ATIYAH-HIRZEBRUCH SPECTRAL SEQUENCE OF REAL PROJECTIVE SPECTRA arxiv:1601.02185v1 [math.at] 10 Jan 2016 GUOZHEN WANG AND ZHOULI XU Abstract. In this note, we use Curtis s algorithm and the

More information

Artificial Neural Networks written examination

Artificial Neural Networks written examination 1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14

More information

Language Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus

Language 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 information

EVOLVING POLICIES TO SOLVE THE RUBIK S CUBE: EXPERIMENTS WITH IDEAL AND APPROXIMATE PERFORMANCE FUNCTIONS

EVOLVING POLICIES TO SOLVE THE RUBIK S CUBE: EXPERIMENTS WITH IDEAL AND APPROXIMATE PERFORMANCE FUNCTIONS EVOLVING POLICIES TO SOLVE THE RUBIK S CUBE: EXPERIMENTS WITH IDEAL AND APPROXIMATE PERFORMANCE FUNCTIONS by Robert Smith Submitted in partial fulfillment of the requirements for the degree of Master of

More information

16 WEEKS STUDY PLAN FOR BS(IT)2 nd Semester

16 WEEKS STUDY PLAN FOR BS(IT)2 nd Semester 16 WEEKS STUDY PLAN FOR BS(IT)2 nd Semester COURSE: OBJECT ORIENTED PROGRAMMING Week Ch# Chapter Names 1 1 The Big Picture 2 2 C++ Programming Basics 3 3 Loops and Decisions 4 4 Structures 5 4 Structures

More information

AP Calculus AB. Nevada Academic Standards that are assessable at the local level only.

AP Calculus AB. Nevada Academic Standards that are assessable at the local level only. Calculus AB Priority Keys Aligned with Nevada Standards MA I MI L S MA represents a Major content area. Any concept labeled MA is something of central importance to the entire class/curriculum; it is a

More information

BANGLA TO ENGLISH TEXT CONVERSION USING OPENNLP TOOLS

BANGLA TO ENGLISH TEXT CONVERSION USING OPENNLP TOOLS Daffodil International University Institutional Repository DIU Journal of Science and Technology Volume 8, Issue 1, January 2013 2013-01 BANGLA TO ENGLISH TEXT CONVERSION USING OPENNLP TOOLS Uddin, Sk.

More information

Developing a TT-MCTAG for German with an RCG-based Parser

Developing a TT-MCTAG for German with an RCG-based Parser Developing a TT-MCTAG for German with an RCG-based Parser Laura Kallmeyer, Timm Lichte, Wolfgang Maier, Yannick Parmentier, Johannes Dellert University of Tübingen, Germany CNRS-LORIA, France LREC 2008,

More information

Introduction to HPSG. Introduction. Historical Overview. The HPSG architecture. Signature. Linguistic Objects. Descriptions.

Introduction 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 information

The Strong Minimalist Thesis and Bounded Optimality

The 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 information

The presence of interpretable but ungrammatical sentences corresponds to mismatches between interpretive and productive parsing.

The presence of interpretable but ungrammatical sentences corresponds to mismatches between interpretive and productive parsing. Lecture 4: OT Syntax Sources: Kager 1999, Section 8; Legendre et al. 1998; Grimshaw 1997; Barbosa et al. 1998, Introduction; Bresnan 1998; Fanselow et al. 1999; Gibson & Broihier 1998. OT is not a theory

More information

Towards a MWE-driven A* parsing with LTAGs [WG2,WG3]

Towards a MWE-driven A* parsing with LTAGs [WG2,WG3] Towards a MWE-driven A* parsing with LTAGs [WG2,WG3] Jakub Waszczuk, Agata Savary To cite this version: Jakub Waszczuk, Agata Savary. Towards a MWE-driven A* parsing with LTAGs [WG2,WG3]. PARSEME 6th general

More information

Statewide Framework Document for:

Statewide Framework Document for: Statewide Framework Document for: 270301 Standards may be added to this document prior to submission, but may not be removed from the framework to meet state credit equivalency requirements. Performance

More information

Prediction of Maximal Projection for Semantic Role Labeling

Prediction of Maximal Projection for Semantic Role Labeling Prediction of Maximal Projection for Semantic Role Labeling Weiwei Sun, Zhifang Sui Institute of Computational Linguistics Peking University Beijing, 100871, China {ws, szf}@pku.edu.cn Haifeng Wang Toshiba

More information

Abstractions and the Brain

Abstractions and the Brain Abstractions and the Brain Brian D. Josephson Department of Physics, University of Cambridge Cavendish Lab. Madingley Road Cambridge, UK. CB3 OHE bdj10@cam.ac.uk http://www.tcm.phy.cam.ac.uk/~bdj10 ABSTRACT

More information

Specifying Logic Programs in Controlled Natural Language

Specifying Logic Programs in Controlled Natural Language TECHNICAL REPORT 94.17, DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF ZURICH, NOVEMBER 1994 Specifying Logic Programs in Controlled Natural Language Norbert E. Fuchs, Hubert F. Hofmann, Rolf Schwitter

More information

Language Model and Grammar Extraction Variation in Machine Translation

Language Model and Grammar Extraction Variation in Machine Translation Language Model and Grammar Extraction Variation in Machine Translation Vladimir Eidelman, Chris Dyer, and Philip Resnik UMIACS Laboratory for Computational Linguistics and Information Processing Department

More information

LNGT0101 Introduction to Linguistics

LNGT0101 Introduction to Linguistics LNGT0101 Introduction to Linguistics Lecture #11 Oct 15 th, 2014 Announcements HW3 is now posted. It s due Wed Oct 22 by 5pm. Today is a sociolinguistics talk by Toni Cook at 4:30 at Hillcrest 103. Extra

More information

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts

More information

Mathematics Assessment Plan

Mathematics Assessment Plan Mathematics Assessment Plan Mission Statement for Academic Unit: Georgia Perimeter College transforms the lives of our students to thrive in a global society. As a diverse, multi campus two year college,

More information

AQUA: An Ontology-Driven Question Answering System

AQUA: 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 information

arxiv: v1 [cs.cv] 10 May 2017

arxiv: v1 [cs.cv] 10 May 2017 Inferring and Executing Programs for Visual Reasoning Justin Johnson 1 Bharath Hariharan 2 Laurens van der Maaten 2 Judy Hoffman 1 Li Fei-Fei 1 C. Lawrence Zitnick 2 Ross Girshick 2 1 Stanford University

More information

Noisy SMS Machine Translation in Low-Density Languages

Noisy SMS Machine Translation in Low-Density Languages Noisy SMS Machine Translation in Low-Density Languages Vladimir Eidelman, Kristy Hollingshead, and Philip Resnik UMIACS Laboratory for Computational Linguistics and Information Processing Department of

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 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 information

ENME 605 Advanced Control Systems, Fall 2015 Department of Mechanical Engineering

ENME 605 Advanced Control Systems, Fall 2015 Department of Mechanical Engineering ENME 605 Advanced Control Systems, Fall 2015 Department of Mechanical Engineering Lecture Details Instructor Course Objectives Tuesday and Thursday, 4:00 pm to 5:15 pm Information Technology and Engineering

More information

STA 225: Introductory Statistics (CT)

STA 225: Introductory Statistics (CT) Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic

More information

Second Exam: Natural Language Parsing with Neural Networks

Second Exam: Natural Language Parsing with Neural Networks Second Exam: Natural Language Parsing with Neural Networks James Cross May 21, 2015 Abstract With the advent of deep learning, there has been a recent resurgence of interest in the use of artificial neural

More information

Evolution of Collective Commitment during Teamwork

Evolution of Collective Commitment during Teamwork Fundamenta Informaticae 56 (2003) 329 371 329 IOS Press Evolution of Collective Commitment during Teamwork Barbara Dunin-Kȩplicz Institute of Informatics, Warsaw University Banacha 2, 02-097 Warsaw, Poland

More information

Parsing natural language

Parsing natural language Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 1983 Parsing natural language Leonard E. Wilcox Follow this and additional works at: http://scholarworks.rit.edu/theses

More information

Character Stream Parsing of Mixed-lingual Text

Character Stream Parsing of Mixed-lingual Text Character Stream Parsing of Mixed-lingual Text Harald Romsdorfer and Beat Pfister Speech Processing Group Computer Engineering and Networks Laboratory ETH Zurich {romsdorfer,pfister}@tik.ee.ethz.ch Abstract

More information

Version Space. Term 2012/2013 LSI - FIB. Javier Béjar cbea (LSI - FIB) Version Space Term 2012/ / 18

Version Space. Term 2012/2013 LSI - FIB. Javier Béjar cbea (LSI - FIB) Version Space Term 2012/ / 18 Version Space Javier Béjar cbea LSI - FIB Term 2012/2013 Javier Béjar cbea (LSI - FIB) Version Space Term 2012/2013 1 / 18 Outline 1 Learning logical formulas 2 Version space Introduction Search strategy

More information

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE Edexcel GCSE Statistics 1389 Paper 1H June 2007 Mark Scheme Edexcel GCSE Statistics 1389 NOTES ON MARKING PRINCIPLES 1 Types of mark M marks: method marks A marks: accuracy marks B marks: unconditional

More information

Using dialogue context to improve parsing performance in dialogue systems

Using 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 information

Multimedia Application Effective Support of Education

Multimedia Application Effective Support of Education Multimedia Application Effective Support of Education Eva Milková Faculty of Science, University od Hradec Králové, Hradec Králové, Czech Republic eva.mikova@uhk.cz Abstract Multimedia applications have

More information

Hans-Ulrich Block, Hans Haugeneder Siemens AG, MOnchen ZT ZTI INF W. Germany. (2) [S' [NP who][s does he try to find [NP e]]s IS' $=~

Hans-Ulrich Block, Hans Haugeneder Siemens AG, MOnchen ZT ZTI INF W. Germany. (2) [S' [NP who][s does he try to find [NP e]]s IS' $=~ The Treatment of Movement-Rules in a LFG-Parser Hans-Ulrich Block, Hans Haugeneder Siemens AG, MOnchen ZT ZT NF W. Germany n this paper we propose a way of how to treat longdistance movement phenomena

More information

Efficient Normal-Form Parsing for Combinatory Categorial Grammar

Efficient Normal-Form Parsing for Combinatory Categorial Grammar Proceedings of the 34th Annual Meeting of the ACL, Santa Cruz, June 1996, pp. 79-86. Efficient Normal-Form Parsing for Combinatory Categorial Grammar Jason Eisner Dept. of Computer and Information Science

More information

A Minimalist Approach to Code-Switching. In the field of linguistics, the topic of bilingualism is a broad one. There are many

A Minimalist Approach to Code-Switching. In the field of linguistics, the topic of bilingualism is a broad one. There are many Schmidt 1 Eric Schmidt Prof. Suzanne Flynn Linguistic Study of Bilingualism December 13, 2013 A Minimalist Approach to Code-Switching In the field of linguistics, the topic of bilingualism is a broad one.

More information

Lecture 1.1: What is a group?

Lecture 1.1: What is a group? Lecture 1.1: What is a group? Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4120, Modern Algebra M. Macauley (Clemson) Lecture 1.1:

More information

Compositional Semantics

Compositional 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 information

The Odd-Parity Parsing Problem 1 Brett Hyde Washington University May 2008

The Odd-Parity Parsing Problem 1 Brett Hyde Washington University May 2008 The Odd-Parity Parsing Problem 1 Brett Hyde Washington University May 2008 1 Introduction Although it is a simple matter to divide a form into binary feet when it contains an even number of syllables,

More information

Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing D. Indhumathi Research Scholar Department of Information Technology

More information