Lecture 9: More on Grammars
|
|
- Ross Jacobs
- 6 years ago
- Views:
Transcription
1 Lecture 9: More on Grammars Dr Kieran T. Herley Department of Computer Science University College Cork 2016/17 KH (11/10/16) Lecture 9: More on Grammars 2016/17 1 / 1
2 Expressive Power of CFGs Not every language is describable using CFG notation; e.g. {a n b n c n : n 0} But syntax of most programming languages can be captured by CFG and are generally designed with this in mind Many aspects of programming language syntax stem from this KH (11/10/16) Lecture 9: More on Grammars 2016/17 2 / 1
3 Extended Notations Backus-Naur Notation BNF Essentially our CFG notation: non-terminals in <>, terminals without 1 EBNF: Extended BNF Grammar metasymbols {α} mean zero or more occurrences of α Example: term factor { ( * / ) factor } means term can be replaced by factor followed by zero or more factor s separated by + s and - s. Terminals ater shown in red to distinguish terminal ( from metasymbol (. 1 But BNF traditionally uses ::= symbol instead of. KH (11/10/16) Lecture 9: More on Grammars 2016/17 3 / 1
4 Extended Notations EBNF Grammar for Simple Arithmetic Expressions expr term { ( + - ) term } term factor { ( * / ) factor } factor NUM ID ( expr ) KH (11/10/16) Lecture 9: More on Grammars 2016/17 4 / 1
5 Extended Notations Syntax Diagrams Diagrammatic representation occasionally used to summarize programming language syntax term factor { ( * / ) factor } Essentially one diagram per production. Arguably more human-friendly than BNF, but less useful for compiler tools as we will see later on KH (11/10/16) Lecture 9: More on Grammars 2016/17 5 / 1
6 Ambiguity Ambiguity Some grammars are ambiguous string string + string string string - string string Allows two different parse-trees/derivations for some strings 2 Ambiguity is generally undesirable for programming-language grammars: allows alternative interpretations of what code means. 2 Technically sentences will have distinct right-derivations: derivation in which rightmost nonterminal is the one replaced at each stage. KH (11/10/16) Lecture 9: More on Grammars 2016/17 6 / 1
7 Ambiguity Example string string + string string string - string string ? KH (11/10/16) Lecture 9: More on Grammars 2016/17 7 / 1
8 Ambiguity Another Example S S + S S S * S S ( S ) S a Here a stand for any integer literal KH (11/10/16) Lecture 9: More on Grammars 2016/17 8 / 1
9 Ambiguity Why Does It Matter? Ambiguity can cause complicate our attempt to understand the meaning of a string May get two different meanings or interpretations for ambiguous string: KH (11/10/16) Lecture 9: More on Grammars 2016/17 9 / 1
10 Ambiguity The Dangling Else Problem statement if-stmt other if-stmt if ( exp ) statement if ( exp ) statement else statement exp 0 1 Consider if (0) if (1) other else other Dilemma: to which if does the else belong? KH (11/10/16) Lecture 9: More on Grammars 2016/17 10 / 1
11 Ambiguity Two Possible Parse Trees Which is the correct interpretation for if (0) if (1) other else other? Some languages live with the ambiguity by adopting the rule that the else matches the most closely nested if not yet coupled with an else (i.e. second tree) KH (11/10/16) Lecture 9: More on Grammars 2016/17 11 / 1
12 Ambiguity Interpretation 1 if (0) if (1) other else other? Else attaches to outer if KH (11/10/16) Lecture 9: More on Grammars 2016/17 12 / 1
13 Ambiguity Interpretation 2 if (0) if (1) other else other Else attaches to inner if KH (11/10/16) Lecture 9: More on Grammars 2016/17 13 / 1
14 Ambiguity Aside Can also re-engineer grammar to work around the problem statement matched-stmt unmatched-stmt matched-stmt if ( exp ) matched-stmt else matched-stmt unmatched-stmt if ( exp ) statement if ( exp ) matched-stmt else unmatched-stmt exp 0 1 KH (11/10/16) Lecture 9: More on Grammars 2016/17 14 / 1
15 Grammar for Tiny Tiny Grammar program stmtseq stmtseq stmtseq ; statement statement statement ifstmt repeatstmt assignstmt readstmt writestmt Note: i) program as sequence of statements; (ii) each individual statement can be of be of five differnet varieties KH (11/10/16) Lecture 9: More on Grammars 2016/17 15 / 1
16 Grammar for Tiny Sequences Production stmtseq stmtseq ; statement statement Form stmtseq we can derive statement statement ; statement statement ; statement ; statement The grammar captures idea that semicolons separate statements KH (11/10/16) Lecture 9: More on Grammars 2016/17 16 / 1
17 Grammar for Tiny Tiny Grammar cont d ifstmt if exp then stmtseq end if exp then stmtseq else stmtseq end repeatstmt repeat stmtseq until exp assignstmt identifier := exp readstmt read identifier writestmt write exp KH (11/10/16) Lecture 9: More on Grammars 2016/17 17 / 1
18 Grammar for Tiny Dangling Else? ifstmt if exp then stmtseq end if exp then stmtseq else stmtseq end KH (11/10/16) Lecture 9: More on Grammars 2016/17 18 / 1
19 Grammar for Tiny Tiny Grammar cont d exp simple-expr comp-op simple-expr simple-expr comp-op < = simple-expr simple-expr addop term term addop + - term term mulop factor factor mulop * / factor ( exp ) number identifier KH (11/10/16) Lecture 9: More on Grammars 2016/17 19 / 1
20 Grammar for Tiny Grammar for Java cont d Can find grammars for most programming languages e.g. [Java Grammar] 3 Can use these to facilitate generation of parsers 3 Google bnf grammar for java KH (11/10/16) Lecture 9: More on Grammars 2016/17 20 / 1
21 Grammar for Tiny Some Tiny Examples test1.tny write 17 test2.tny read x; write x fact.tny { Sample program in TINY language computes factorial } read x; { input an integer } if 0 < x then { don t compute if x 0 } fact := 1; repeat fact := fact x; x := x 1 until x = 0; write fact { output factorial of x } end KH (11/10/16) Lecture 9: More on Grammars 2016/17 21 / 1
22 Grammar for Tiny Tiny Example 1 test1.tny write 17 KH (11/10/16) Lecture 9: More on Grammars 2016/17 22 / 1
23 Grammar for Tiny Tiny Example 2 test2.tny read x; write x KH (11/10/16) Lecture 9: More on Grammars 2016/17 23 / 1
24 Grammar for Tiny Tiny Example 3 KH (11/10/16) Lecture 9: More on Grammars 2016/17 24 / 1
25 CFGs and Pushdown Automata Fact For every regular expression there is an equivalent finite automaton and vice versa. Fact For every context-free grammar there is an equivalent pushdown automaton and vice versa. KH (11/10/16) Lecture 9: More on Grammars 2016/17 25 / 1
26 CFGs and Pushdown Automata Pushdown Automata Next Step dictated by next input symbol top stack symbol Each Step involves change state of finite control push/pop symbol on/off stack advance one symbol in input KH (11/10/16) Lecture 9: More on Grammars 2016/17 26 / 1
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 informationGrammars & 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 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 informationA 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 informationLanguage 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 informationErkki 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 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 informationRANKING 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 informationLanguage 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 informationRefining 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 informationBasic 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 informationContext 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 informationABSTRACT. 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 informationGRAMMAR IN CONTEXT 2 PDF
GRAMMAR IN CONTEXT 2 PDF ==> Download: GRAMMAR IN CONTEXT 2 PDF GRAMMAR IN CONTEXT 2 PDF - Are you searching for Grammar In Context 2 Books? Now, you will be happy that at this time Grammar In Context
More informationInformatics 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 informationA 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 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 information11/29/2010. 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 informationGACE 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 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 informationWSU 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"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 informationsystems 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 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 informationDeveloping 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 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 informationChinese Language Parsing with Maximum-Entropy-Inspired Parser
Chinese Language Parsing with Maximum-Entropy-Inspired Parser Heng Lian Brown University Abstract The Chinese language has many special characteristics that make parsing difficult. The performance of state-of-the-art
More informationParsing 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 informationAGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS
AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS 1 CALIFORNIA CONTENT STANDARDS: Chapter 1 ALGEBRA AND WHOLE NUMBERS Algebra and Functions 1.4 Students use algebraic
More informationNatural 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 informationHans-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 informationSouth Carolina English Language Arts
South Carolina English Language Arts A S O F J U N E 2 0, 2 0 1 0, T H I S S TAT E H A D A D O P T E D T H E CO M M O N CO R E S TAT E S TA N DA R D S. DOCUMENTS REVIEWED South Carolina Academic Content
More informationParallel 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 informationTowards 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 informationThe 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 informationType Theory and Universal Grammar
Type Theory and Universal Grammar Aarne Ranta Department of Computer Science and Engineering Chalmers University of Technology and Göteborg University Abstract. The paper takes a look at the history of
More informationSpecifying 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 informationSINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)
SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,
More informationLearning to Think Mathematically With the Rekenrek
Learning to Think Mathematically With the Rekenrek A Resource for Teachers A Tool for Young Children Adapted from the work of Jeff Frykholm Overview Rekenrek, a simple, but powerful, manipulative to help
More informationGrade 6: Correlated to AGS Basic Math Skills
Grade 6: Correlated to AGS Basic Math Skills Grade 6: Standard 1 Number Sense Students compare and order positive and negative integers, decimals, fractions, and mixed numbers. They find multiples and
More informationBANGLA 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 informationRule discovery in Web-based educational systems using Grammar-Based Genetic Programming
Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de
More informationCOMPUTATIONAL 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 informationHyperedge 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 informationModeling Attachment Decisions with a Probabilistic Parser: The Case of Head Final Structures
Modeling Attachment Decisions with a Probabilistic Parser: The Case of Head Final Structures Ulrike Baldewein (ulrike@coli.uni-sb.de) Computational Psycholinguistics, Saarland University D-66041 Saarbrücken,
More informationDeveloping a concrete-pictorial-abstract model for negative number arithmetic
Developing a concrete-pictorial-abstract model for negative number arithmetic Jai Sharma and Doreen Connor Nottingham Trent University Research findings and assessment results persistently identify negative
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 informationarxiv: 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 informationARNE - A tool for Namend Entity Recognition from Arabic Text
24 ARNE - A tool for Namend Entity Recognition from Arabic Text Carolin Shihadeh DFKI Stuhlsatzenhausweg 3 66123 Saarbrücken, Germany carolin.shihadeh@dfki.de Günter Neumann DFKI Stuhlsatzenhausweg 3 66123
More informationEnhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities
Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities Yoav Goldberg Reut Tsarfaty Meni Adler Michael Elhadad Ben Gurion
More informationPRODUCT 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 informationUniversiteit Leiden ICT in Business
Universiteit Leiden ICT in Business Ranking of Multi-Word Terms Name: Ricardo R.M. Blikman Student-no: s1184164 Internal report number: 2012-11 Date: 07/03/2013 1st supervisor: Prof. Dr. J.N. Kok 2nd supervisor:
More informationMath 96: Intermediate Algebra in Context
: Intermediate Algebra in Context Syllabus Spring Quarter 2016 Daily, 9:20 10:30am Instructor: Lauri Lindberg Office Hours@ tutoring: Tutoring Center (CAS-504) 8 9am & 1 2pm daily STEM (Math) Center (RAI-338)
More informationThe Discourse Anaphoric Properties of Connectives
The Discourse Anaphoric Properties of Connectives Cassandre Creswell, Kate Forbes, Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi Λ, Bonnie Webber y Λ University of Pennsylvania 3401 Walnut Street Philadelphia,
More informationpreassessment was administered)
5 th grade Math Friday, 3/19/10 Integers and Absolute value (Lesson taught during the same period that the integer preassessment was administered) What students should know and be able to do at the end
More informationPhysics 270: Experimental Physics
2017 edition Lab Manual Physics 270 3 Physics 270: Experimental Physics Lecture: Lab: Instructor: Office: Email: Tuesdays, 2 3:50 PM Thursdays, 2 4:50 PM Dr. Uttam Manna 313C Moulton Hall umanna@ilstu.edu
More informationA 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 informationCS 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 informationStatewide 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 informationNumeracy Medium term plan: Summer Term Level 2C/2B Year 2 Level 2A/3C
Numeracy Medium term plan: Summer Term Level 2C/2B Year 2 Level 2A/3C Using and applying mathematics objectives (Problem solving, Communicating and Reasoning) Select the maths to use in some classroom
More informationAnalysis of Probabilistic Parsing in NLP
Analysis of Probabilistic Parsing in NLP Krishna Karoo, Dr.Girish Katkar Research Scholar, Department of Electronics & Computer Science, R.T.M. Nagpur University, Nagpur, India Head of Department, Department
More informationChunk Parsing for Base Noun Phrases using Regular Expressions. Let s first let the variable s0 be the sentence tree of the first sentence.
NLP Lab Session Week 8 October 15, 2014 Noun Phrase Chunking and WordNet in NLTK Getting Started In this lab session, we will work together through a series of small examples using the IDLE window and
More informationMath-U-See Correlation with the Common Core State Standards for Mathematical Content for Third Grade
Math-U-See Correlation with the Common Core State Standards for Mathematical Content for Third Grade The third grade standards primarily address multiplication and division, which are covered in Math-U-See
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 informationWelcome to the Purdue OWL. Where do I begin? General Strategies. Personalizing Proofreading
Welcome to the Purdue OWL This page is brought to you by the OWL at Purdue (http://owl.english.purdue.edu/). When printing this page, you must include the entire legal notice at bottom. Where do I begin?
More informationContent 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 informationEfficient 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 informationSoftware Development: Programming Paradigms (SCQF level 8)
Higher National Unit Specification General information Unit code: HL9V 35 Superclass: CB Publication date: May 2017 Source: Scottish Qualifications Authority Version: 01 Unit purpose This unit is intended
More informationCS 101 Computer Science I Fall Instructor Muller. Syllabus
CS 101 Computer Science I Fall 2013 Instructor Muller Syllabus Welcome to CS101. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts of
More informationPowerTeacher Gradebook User Guide PowerSchool Student Information System
PowerSchool Student Information System Document Properties Copyright Owner Copyright 2007 Pearson Education, Inc. or its affiliates. All rights reserved. This document is the property of Pearson Education,
More informationComputer Organization I (Tietokoneen toiminta)
581305-6 Computer Organization I (Tietokoneen toiminta) Teemu Kerola University of Helsinki Department of Computer Science Spring 2010 1 Computer Organization I Course area and goals Course learning methods
More informationMathematics 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 informationChapter 4: Valence & Agreement CSLI Publications
Chapter 4: Valence & Agreement Reminder: Where We Are Simple CFG doesn t allow us to cross-classify categories, e.g., verbs can be grouped by transitivity (deny vs. disappear) or by number (deny vs. denies).
More informationHoughton Mifflin Online Assessment System Walkthrough Guide
Houghton Mifflin Online Assessment System Walkthrough Guide Page 1 Copyright 2007 by Houghton Mifflin Company. All Rights Reserved. No part of this document may be reproduced or transmitted in any form
More informationFocus of the Unit: Much of this unit focuses on extending previous skills of multiplication and division to multi-digit whole numbers.
Approximate Time Frame: 3-4 weeks Connections to Previous Learning: In fourth grade, students fluently multiply (4-digit by 1-digit, 2-digit by 2-digit) and divide (4-digit by 1-digit) using strategies
More informationNetsmart Sandbox Tour Guide Script
Netsmart Sandbox Tour Guide Script October 2012 This document is to be used in conjunction with the Netsmart Sandbox environment as a guide. Following the steps included in this guide will allow you to
More informationOrganizational Knowledge Distribution: An Experimental Evaluation
Association for Information Systems AIS Electronic Library (AISeL) AMCIS 24 Proceedings Americas Conference on Information Systems (AMCIS) 12-31-24 : An Experimental Evaluation Surendra Sarnikar University
More informationKBS : Knowledge Representation. Motivation. Epistemology. Objectives
KBS : Knowledge Representation Motivation Motivation Objectives Chapter Introduction Review of relevant concepts Overview new topics Terminology Knowledge and its Meaning Epistemology Types of Knowledge
More informationPre-Algebra A. Syllabus. Course Overview. Course Goals. General Skills. Credit Value
Syllabus Pre-Algebra A Course Overview Pre-Algebra is a course designed to prepare you for future work in algebra. In Pre-Algebra, you will strengthen your knowledge of numbers as you look to transition
More informationLearning Computational Grammars
Learning Computational Grammars John Nerbonne, Anja Belz, Nicola Cancedda, Hervé Déjean, James Hammerton, Rob Koeling, Stasinos Konstantopoulos, Miles Osborne, Franck Thollard and Erik Tjong Kim Sang Abstract
More informationNATURAL LANGUAGE PARSING AND REPRESENTATION IN XML EUGENIO JAROSIEWICZ
NATURAL LANGUAGE PARSING AND REPRESENTATION IN XML By EUGENIO JAROSIEWICZ A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE
More informationThe 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 informationLesson M4. page 1 of 2
Lesson M4 page 1 of 2 Miniature Gulf Coast Project Math TEKS Objectives 111.22 6b.1 (A) apply mathematics to problems arising in everyday life, society, and the workplace; 6b.1 (C) select tools, including
More informationIntroduction, Organization Overview of NLP, Main Issues
HG2051 Language and the Computer Computational Linguistics with Python Introduction, Organization Overview of NLP, Main Issues Francis Bond Division of Linguistics and Multilingual Studies http://www3.ntu.edu.sg/home/fcbond/
More informationCAAP. Content Analysis Report. Sample College. Institution Code: 9011 Institution Type: 4-Year Subgroup: none Test Date: Spring 2011
CAAP Content Analysis Report Institution Code: 911 Institution Type: 4-Year Normative Group: 4-year Colleges Introduction This report provides information intended to help postsecondary institutions better
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 informationSchool of Innovative Technologies and Engineering
School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius
More informationImproving Fairness in Memory Scheduling
Improving Fairness in Memory Scheduling Using a Team of Learning Automata Aditya Kajwe and Madhu Mutyam Department of Computer Science & Engineering, Indian Institute of Tehcnology - Madras June 14, 2014
More informationcambridge occasional papers in linguistics Volume 8, Article 3: 41 55, 2015 ISSN
C O P i L cambridge occasional papers in linguistics Volume 8, Article 3: 41 55, 2015 ISSN 2050-5949 THE DYNAMICS OF STRUCTURE BUILDING IN RANGI: AT THE SYNTAX-SEMANTICS INTERFACE H a n n a h G i b s o
More informationDRAFT VERSION 2, 02/24/12
DRAFT VERSION 2, 02/24/12 Incentive-Based Budget Model Pilot Project for Academic Master s Program Tuition (Optional) CURRENT The core of support for the university s instructional mission has historically
More informationMathematics. Mathematics
Mathematics Program Description Successful completion of this major will assure competence in mathematics through differential and integral calculus, providing an adequate background for employment in
More informationIdentifying Novice Difficulties in Object Oriented Design
Identifying Novice Difficulties in Object Oriented Design Benjy Thomasson, Mark Ratcliffe, Lynda Thomas University of Wales, Aberystwyth Penglais Hill Aberystwyth, SY23 1BJ +44 (1970) 622424 {mbr, ltt}
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 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 informationMontana Content Standards for Mathematics Grade 3. Montana Content Standards for Mathematical Practices and Mathematics Content Adopted November 2011
Montana Content Standards for Mathematics Grade 3 Montana Content Standards for Mathematical Practices and Mathematics Content Adopted November 2011 Contents Standards for Mathematical Practice: Grade
More informationMULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY
MULTILINGUAL INFORMATION ACCESS IN DIGITAL LIBRARY Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei, Taiwan E-mail: hh_chen@csie.ntu.edu.tw Abstract
More informationTABE 9&10. Revised 8/2013- with reference to College and Career Readiness Standards
TABE 9&10 Revised 8/2013- with reference to College and Career Readiness Standards LEVEL E Test 1: Reading Name Class E01- INTERPRET GRAPHIC INFORMATION Signs Maps Graphs Consumer Materials Forms Dictionary
More informationSchool Inspection in Hesse/Germany
Hessisches Kultusministerium School Inspection in Hesse/Germany Contents 1. Introduction...2 2. School inspection as a Procedure for Quality Assurance and Quality Enhancement...2 3. The Hessian framework
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 informationFunction Number 1 Work as part of a team. Thorough knowledge of theoretical procedures and ability to integrate knowledge and performance into
Function Essential Functions EMT PARAMEDIC 1 Work as part of a team. Thorough knowledge of theoretical procedures and ability to integrate knowledge and performance into practical situations is critical.
More information