UNIT III SYNTAX ANALYSIS

Size: px
Start display at page:

Download "UNIT III SYNTAX ANALYSIS"

Transcription

1 UNIT III SYNTAX ANALYSIS 2 Marks 1. Eliminate the left recursion from the following grammar A->Ac Aad bd c The rule to eliminate the left recursion is A Aα βâ can be converted as A βa and A αa ε. So the grammar after eliminating left recursion is A->bdA c A A ->c A ad A εå 2. What are the disadvantages of operator precedence parsing? The operator, like minus (unary and binary) has two different precedence. Hence it is hard to handle tokens like minus sign. This kind of parsing is applicable to only small class of grammars. 3. Construct a parse tree of (a+b)*c for the grammar E->E+E E*E (E) id. 4. Derive the string and construct a syntax tree for the input string ceaedbe using the grammar S->SaA A, A->AbB B, B->cSd e. Derivation: S=> A (S->A) => AbB (A->AbB) => BbB (A->B) => csdbb (B->cSd) => csaadbb (S->SaA) => caaadbb (S->A) => cbaadbb (A->B) => ceaadbb (B->e) =>ceabdbb (A->B) =>ceaedbb (B->e) =>ceaedbe (B->e)

2 5. List the factors to be considered for top-down parsing. Top down parsing is an attempt to find a leftmost derivation for an input string. Left recursive grammar can cause a top-down parser to go into an indefinite loop on writing procedure. Backtracking overhead may occur Due to backtracking, it may reject some valid sentences Left factoring Ambiguity The order in which alternates are tried can affect the language accepted When failure is reported, we have very little idea where the error actually occurred 6. What is an ambiguous grammar? A grammar G is said to be ambiguous if it generates more than one parse tree for some sentence of language L(G).i.e. both leftmost and rightmost derivations are same for the given sentence. 7. What is a predictive parser? It is a top down parser. It is a program based on a transition diagram attempts to match the terminal symbols against the input and makes a recursive procedure call whenever it has to follow an edge labeled by a non terminal. A non recursive predictive parser is a program that matches the terminal symbols against the input by maintaining a stack rather than using recursive calls. 8. What is CLR?

3 CLR stands for canonical LR parser. It is a bottom up parsing technique. L stands for left to right scanning of the input and R stands for constricting a rightmost derivation in reverse. 9. What do u meant by parser and its types? A parser for grammar G is a program that takes as input a string w and produces as output either a parse tree for w, if w is a sentence of G, or an error message indicating that w is not a sentence of G. it obtains a string of tokens from the lexical analyzer, verifies that the string generated by the grammar for the source language. a) Top down parsing b) Bottom up parsing 10. What are the different levels of syntax error handler? a) Lexical, such as misspelling an identifier, keyword, or operator. b) Syntactic, such as an arithmetic expression with unbalanced parentheses c) Semantic, such as operator applied to an incompatible operand d) Logical, such as an infinitely recursive call. 11. What are the goals of error handler in a parser? i) It should report the presence of errors clearly and accurately ii) It should recover from each error quickly enough to be able to detect subsequent errors iii) It should not significantly slow down the processing of correct programs. 12. What are error recovery strategies in parser? a) Panic mode b) Phrase level c) Error productions d) Global corrections 13. Define CFG? Many programming language has rules that prescribe the syntactic structure of well-formed programs. The syntax of programming language constructs can be described by CFG. Conditional statement defined by a rule such as ; If S1 and S2 are statements and E is an expression, then If E then S1 else S2 is a statement. 14. Mention the properties of parse tree. The root is labeled by the start symbol. Each leaf is labeled by a token or by. Each interior node is labeled by a non terminal.if A is the Non terminal, labeling some interior node and x1, x2, x3.xn are the labels of the children

4 15. When a grammar is said to be ambiguous? When a grammar produces more than one parse tree for a same sentence then the grammar is said to be ambiguous. 16. What do you mean by a syntax tree? Syntax tree is a variant of a parse tree in which each leaf represents an operand and each interior node represents an operator. 17. Compare production with reduction. The rules that define the ways in which the syntactic categories can be built are productions whereas the replacement of the string by an non-terminal according to a grammar production is called reduction. 18. What are the functions of parser? i. Representation of the tokens by codes (integers) ii. Grouping of tokens together into syntactic structure 19. Define handle pruning. A technique to obtain the rightmost derivation in reverse (called canonical reduction sequence) is known as handle pruning (i.e.) starting with a string of terminals w to be parsed. If w is the sentence of the grammar then = n where n is the nth right sentential form of unknown right most derivation. 20. List various types of grammars. i. Phase sensitive grammar ii. Context sensitive grammar iii. Context-free grammar iv. Regular grammar 21. What are the demerits of SLR? It will not produce uniquely defined parsing action tables for all grammars. Shift-Reduce conflict. 22. Why LR parsing is good and attractive? LR parsers can be constructed for all programming language constructs for which CFG can be written. LR parsing is Non-backtracking Shift-Reduce parsing. Grammars parsed using LR parsers are super set of the class of grammar. LR parser can detect syntactic error as soon as possible, when left-to-right scan of the input. 23. How will you change the given grammar to an augmented grammar? If G is a grammar with start symbol S then G is the augmented grammar for G, such that G is G with a new start symbol S and with production S S.

5 24. Find the item I0 for the following grammar using CLR parsing method. G: S AS S b A SA A a I0: S. S, $ S.AS, $ S.b, $ A.SA, a b A.a, a b S.AS, a b S.b, a b 25. Specify the advantages of LALR. Merging of states with common cores can never produce a shift/reduce conflict that was not present in any one of the original states. Because shift actions depends only one core, not the look ahead. 43. Mention the demerits of LALR parser. Merger will produce reduce / reduce conflict. On erroneous input, LALR parser may proceed to do some reductions after the LR parser has declared an error, but LALR parser never shift a symbol after the LR parser declares an error. 44. What is the syntax for YACC source specification program? Declarations %% Translation rules %% Supporting C-routines 45. How YACC resolves parsing action conflicts? A reduce/reduce conflict is resolved by choosing the conflicting production listed first in the YACC specification. A shift/reduce conflict is resolved by shifting action or introduce associativity and precedence. 46. What is the new production added to the YACC on error recovery When parser generated by YACC encounters errors, then YACC pops symbol from its stack until it finds the topmost state on its stack whose underlying set of items includes an item of the form A. error 47. Define LR parser?

6 LR parsers can be used to parse a large class of context free grammars. The technique is called LR (K) parsing. L denotes that input sequence is processed from left to right R denotes that the right most derivation is performed K denotes that atmost K symbols of the sequence are used to make a decision. 48. What are the drawbacks of LR parser? a) Parsing tables are too complicated to be generated by hand, need an automated parser generator. b) Cannot handle ambiguous grammar without special tricks. 49. Define Operator grammar. A grammar with no production right side is ε or no two adjacent Non Terminalsis called Operator grammar. 50. Define operator precedence grammar. Operator precedence grammar is an operator grammar having disjoint precedence relations between any pair of Terminals. 51.What are the different precedence relations in operator precedence parser? 52. What do you mean by precedence function? The precedence table can be encoded by two precedence functions f and g, that map the terminal symbols to integers. 16Marks 1. Construct predictive parsing table for the grammar S->(L) a L->L,S S Solution:

7 2. What are the different strategies that a parser can employ to recover from syntax errors? Panic mode recovery On discovering an error, the parser discards input symbols one at a time until one of a designated set of synchronizing tokens is found. Phrase level recovery On discovering an error, the parser may perform local correction on the remaining input; e.g. replace prefix of the remaining input by some string that allow the parser to continue Error productions Augment the grammar for the language with productions that generate the erroneous constructs. If it is being used by the parser, generate appropriate error diagnostics to indicate the erroneous construct that has been recognized in the input Global correction It does minimal changes in the incorrect input string to obtain a globally least-cost correction. 3. Construct the CLR parsing table from

8 4. Write Operator-precedence parsing algorithm.

9 5. Find the SLR parsing table for the given grammar and parse the sentence (a+b)*c. E->E+E E*E (E) id.

10

11 6. Given the following grammar S->AS b, A->SA a. Construct a SLR parsing table for the string baab.

12

13

14 7. Explain the role of parser in detail Parser obtains a string of tokens from the lexical analyzer and verifies that the string can be generated by the grammar for the source language. It can report any syntax error in an intelligible fashion. Errors can be of lexical, syntactic, semantic or logical. The error handler in a parser has simple-to-state goals: should report the presence of errors clearly and accurately should recover from each error quickly enough to be able to detect subsequent errors should not significantly slow down the processing of correct programs 8. Give the LALR parsing table for the grammar S-> L=R R L->*R id R->L.

15

16

17 1. What are the reasons for using LR parser technique? LR parsers can be constructed to recognize virtually all programming language constructs for which CFGs can be written LR parsing method is the most general non backtracking shift reduce parsing method known, yet it can be implemented as efficiently as other shift-reduce methods The class of grammars that can be parsed using LR methods is a proper superset of the class of grammars that can be parsed with predictive parsers

18 An LR parser can detect a syntactic error as soon as it is possible to do so on a left-to-right scan of the input 10. What is an operator grammar? Draw the precedence graph for the following table:

19 11. Write a note on error recovery in predictive parsing. An error is detected during predictive parsing, when the terminal on the top of the stack does not match the next input symbol or when non terminal A is on the top of the stack, a is the next input symbol, and the parsing table entry M [A, a] is empty. Panic mode error recovery is based on the idea of skipping symbols on the input until a token in a selected set of synchronizing tokens appears. Heuristics for choosing synchronizing token set: Place all the symbols in Follow(A) into synchronizing set for A Add to the synchronizing set of a lower construct the symbol that begin higher constructs Place all the symbols in First(A) into synchronizing set for A If a non terminal can generate the empty string, then the production deriving  can be used as default. If the terminal on top of stack does not match, just pop the terminal and issue a message saying that the terminal was inserted and continue parsing. Phrase-level recovery is implemented by filling in the blank entries in the predictive parsing table with pointers to error routines. These routines may insert, change or delete symbols on the input and issue appropriate error messages. 12. Write the LR parsing algorithm. Check whether the grammar is SLR (1) or not. Justify the answer with reasons. S->L=R R; L->*R id; R->L

20

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Linking Task: Identifying authors and book titles in verbose queries

Linking Task: Identifying authors and book titles in verbose queries Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,

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

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

Software Maintenance

Software 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 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

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 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

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

Grade 6: Correlated to AGS Basic Math Skills

Grade 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 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

Chinese Language Parsing with Maximum-Entropy-Inspired Parser

Chinese 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 information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A 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

"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

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

An Interactive Intelligent Language Tutor Over The Internet

An 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 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

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

Disambiguation of Thai Personal Name from Online News Articles

Disambiguation of Thai Personal Name from Online News Articles Disambiguation of Thai Personal Name from Online News Articles Phaisarn Sutheebanjard Graduate School of Information Technology Siam University Bangkok, Thailand mr.phaisarn@gmail.com Abstract Since online

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

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

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

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

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

PowerTeacher Gradebook User Guide PowerSchool Student Information System

PowerTeacher 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 information

Ensemble Technique Utilization for Indonesian Dependency Parser

Ensemble Technique Utilization for Indonesian Dependency Parser Ensemble Technique Utilization for Indonesian Dependency Parser Arief Rahman Institut Teknologi Bandung Indonesia 23516008@std.stei.itb.ac.id Ayu Purwarianti Institut Teknologi Bandung Indonesia ayu@stei.itb.ac.id

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

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

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

Houghton Mifflin Online Assessment System Walkthrough Guide

Houghton 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 information

Physics 270: Experimental Physics

Physics 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 information

ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology

ReinForest: 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 information

Semi-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. 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 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

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

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 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

OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR for Arabic using SIFT Descriptors With Online Failure Prediction OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,

More information

EdIt: A Broad-Coverage Grammar Checker Using Pattern Grammar

EdIt: A Broad-Coverage Grammar Checker Using Pattern Grammar EdIt: A Broad-Coverage Grammar Checker Using Pattern Grammar Chung-Chi Huang Mei-Hua Chen Shih-Ting Huang Jason S. Chang Institute of Information Systems and Applications, National Tsing Hua University,

More information

Millersville University Degree Works Training User Guide

Millersville University Degree Works Training User Guide Millersville University Degree Works Training User Guide Page 1 Table of Contents Introduction... 5 What is Degree Works?... 5 Degree Works Functionality Summary... 6 Access to Degree Works... 8 Login

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

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

Moodle 2 Assignments. LATTC Faculty Technology Training Tutorial

Moodle 2 Assignments. LATTC Faculty Technology Training Tutorial LATTC Faculty Technology Training Tutorial Moodle 2 Assignments This tutorial begins with the instructor already logged into Moodle 2. http://moodle.lattc.edu/ Faculty login id is same as email login id.

More information

Guidelines for Writing an Internship Report

Guidelines 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 information

AGS 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 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 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

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

Creating a Test in Eduphoria! Aware

Creating a Test in Eduphoria! Aware in Eduphoria! Aware Login to Eduphoria using CHROME!!! 1. LCS Intranet > Portals > Eduphoria From home: LakeCounty.SchoolObjects.com 2. Login with your full email address. First time login password default

More information

South Carolina English Language Arts

South 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 information

Chunk Parsing for Base Noun Phrases using Regular Expressions. Let s first let the variable s0 be the sentence tree of the first sentence.

Chunk 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 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

SEMAFOR: Frame Argument Resolution with Log-Linear Models

SEMAFOR: 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 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

The Discourse Anaphoric Properties of Connectives

The 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 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

The Smart/Empire TIPSTER IR System

The Smart/Empire TIPSTER IR System The Smart/Empire TIPSTER IR System Chris Buckley, Janet Walz Sabir Research, Gaithersburg, MD chrisb,walz@sabir.com Claire Cardie, Scott Mardis, Mandar Mitra, David Pierce, Kiri Wagstaff Department of

More information

Detecting English-French Cognates Using Orthographic Edit Distance

Detecting English-French Cognates Using Orthographic Edit Distance Detecting English-French Cognates Using Orthographic Edit Distance Qiongkai Xu 1,2, Albert Chen 1, Chang i 1 1 The Australian National University, College of Engineering and Computer Science 2 National

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

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

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

Implementing a tool to Support KAOS-Beta Process Model Using EPF

Implementing a tool to Support KAOS-Beta Process Model Using EPF Implementing a tool to Support KAOS-Beta Process Model Using EPF Malihe Tabatabaie Malihe.Tabatabaie@cs.york.ac.uk Department of Computer Science The University of York United Kingdom Eclipse Process Framework

More information

Developing a concrete-pictorial-abstract model for negative number arithmetic

Developing 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 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

Circuit Simulators: A Revolutionary E-Learning Platform

Circuit Simulators: A Revolutionary E-Learning Platform Circuit Simulators: A Revolutionary E-Learning Platform Mahi Itagi Padre Conceicao College of Engineering, Verna, Goa, India. itagimahi@gmail.com Akhil Deshpande Gogte Institute of Technology, Udyambag,

More information

DegreeWorks Advisor Reference Guide

DegreeWorks Advisor Reference Guide DegreeWorks Advisor Reference Guide Table of Contents 1. DegreeWorks Basics... 2 Overview... 2 Application Features... 3 Getting Started... 4 DegreeWorks Basics FAQs... 10 2. What-If Audits... 12 Overview...

More information

Visual CP Representation of Knowledge

Visual CP Representation of Knowledge Visual CP Representation of Knowledge Heather D. Pfeiffer and Roger T. Hartley Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001, USA email: hdp@cs.nmsu.edu and rth@cs.nmsu.edu

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

Appendix L: Online Testing Highlights and Script

Appendix L: Online Testing Highlights and Script Online Testing Highlights and Script for Fall 2017 Ohio s State Tests Administrations Test administrators must use this document when administering Ohio s State Tests online. It includes step-by-step directions,

More information

Preferences...3 Basic Calculator...5 Math/Graphing Tools...5 Help...6 Run System Check...6 Sign Out...8

Preferences...3 Basic Calculator...5 Math/Graphing Tools...5 Help...6 Run System Check...6 Sign Out...8 CONTENTS GETTING STARTED.................................... 1 SYSTEM SETUP FOR CENGAGENOW....................... 2 USING THE HEADER LINKS.............................. 2 Preferences....................................................3

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

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

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best

More information

UNIVERSITY OF OSLO Department of Informatics. Dialog Act Recognition using Dependency Features. Master s thesis. Sindre Wetjen

UNIVERSITY OF OSLO Department of Informatics. Dialog Act Recognition using Dependency Features. Master s thesis. Sindre Wetjen UNIVERSITY OF OSLO Department of Informatics Dialog Act Recognition using Dependency Features Master s thesis Sindre Wetjen November 15, 2013 Acknowledgments First I want to thank my supervisors Lilja

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

On-Line Data Analytics

On-Line Data Analytics International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] On-Line Data Analytics Yugandhar Vemulapalli #, Devarapalli Raghu *, Raja Jacob

More information

New Features & Functionality in Q Release Version 3.1 January 2016

New Features & Functionality in Q Release Version 3.1 January 2016 in Q Release Version 3.1 January 2016 Contents Release Highlights 2 New Features & Functionality 3 Multiple Applications 3 Analysis 3 Student Pulse 3 Attendance 4 Class Attendance 4 Student Attendance

More information

School of Innovative Technologies and Engineering

School 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 information

NATURAL LANGUAGE PARSING AND REPRESENTATION IN XML EUGENIO JAROSIEWICZ

NATURAL 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 information

How to analyze visual narratives: A tutorial in Visual Narrative Grammar

How to analyze visual narratives: A tutorial in Visual Narrative Grammar How to analyze visual narratives: A tutorial in Visual Narrative Grammar Neil Cohn 2015 neilcohn@visuallanguagelab.com www.visuallanguagelab.com Abstract Recent work has argued that narrative sequential

More information

Seminar - Organic Computing

Seminar - Organic Computing Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts

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

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

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

Introduction to Causal Inference. Problem Set 1. Required Problems

Introduction to Causal Inference. Problem Set 1. Required Problems Introduction to Causal Inference Problem Set 1 Professor: Teppei Yamamoto Due Friday, July 15 (at beginning of class) Only the required problems are due on the above date. The optional problems will not

More information

Schoology Getting Started Guide for Teachers

Schoology Getting Started Guide for Teachers Schoology Getting Started Guide for Teachers (Latest Revision: December 2014) Before you start, please go over the Beginner s Guide to Using Schoology. The guide will show you in detail how to accomplish

More information

LTAG-spinal and the Treebank

LTAG-spinal and the Treebank LTAG-spinal and the Treebank a new resource for incremental, dependency and semantic parsing Libin Shen (lshen@bbn.com) BBN Technologies, 10 Moulton Street, Cambridge, MA 02138, USA Lucas Champollion (champoll@ling.upenn.edu)

More information

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments

Specification 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 information