Using Contextual Representations to Efficiently Learn Context-Free
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Probabilistic Grammars and Their Applications This Discretion to Pursue Political and Economic Ends
Probabilistic Grammars and their Applications this discretion to pursue political and economic ends. and the Law; Monetary Policy; Multinational Cor- Most experiences, however, suggest the limited power porations; Regulation, Economic Theory of; Regu- of privatization in changing the modes of governance lation: Working Conditions; Smith, Adam (1723–90); which are prevalent in each country’s large private Socialism; Venture Capital companies. Further, those countries which have chosen the mass (voucher) privatization route have done so largely out of necessity and face ongoing efficiency problems as a result. In the UK, a country Bibliography whose privatization policies are often referred to as a Armijo L 1998 Balance sheet or ballot box? Incentives to benchmark, ‘control [of privatized companies] is not privatize in emerging democracies. In: Oxhorn P, Starr P exerted in the forms of threats of take-over or (eds.) The Problematic Relationship between Economic and bankruptcy; nor has it for the most part come from Political Liberalization. Lynne Rienner, Boulder, CO Bishop M, Kay J, Mayer C 1994 Introduction: privatization in direct investor intervention’ (Bishop et al. 1994, p. 11). After the steep rise experienced in the immediate performance. In: Bishop M, Kay J, Mayer C (eds.) Pri ati- zation and Economic Performance. Oxford University Press, aftermath of privatizations, the slow but constant Oxford, UK decline in the number of small shareholders highlights Boubakri N, Cosset J-C 1998 The financial and operating the difficulties in sustaining people’s capitalism in the performance of newly privatized firms: evidence from develop- longer run. In Italy, for example, privatization was ing countries. -
Grammars and Normal Forms
Grammars and Normal Forms Read K & S 3.7. Recognizing Context-Free Languages Two notions of recognition: (1) Say yes or no, just like with FSMs (2) Say yes or no, AND if yes, describe the structure a + b * c Now it's time to worry about extracting structure (and doing so efficiently). Optimizing Context-Free Languages For regular languages: Computation = operation of FSMs. So, Optimization = Operations on FSMs: Conversion to deterministic FSMs Minimization of FSMs For context-free languages: Computation = operation of parsers. So, Optimization = Operations on languages Operations on grammars Parser design Before We Start: Operations on Grammars There are lots of ways to transform grammars so that they are more useful for a particular purpose. the basic idea: 1. Apply transformation 1 to G to get of undesirable property 1. Show that the language generated by G is unchanged. 2. Apply transformation 2 to G to get rid of undesirable property 2. Show that the language generated by G is unchanged AND that undesirable property 1 has not been reintroduced. 3. Continue until the grammar is in the desired form. Examples: • Getting rid of ε rules (nullable rules) • Getting rid of sets of rules with a common initial terminal, e.g., • A → aB, A → aC become A → aD, D → B | C • Conversion to normal forms Lecture Notes 16 Grammars and Normal Forms 1 Normal Forms If you want to design algorithms, it is often useful to have a limited number of input forms that you have to deal with. Normal forms are designed to do just that. -
CS351 Pumping Lemma, Chomsky Normal Form Chomsky Normal
CS351 Pumping Lemma, Chomsky Normal Form Chomsky Normal Form When working with a context-free grammar a simple and useful form is called the Chomsky Normal Form (CNF). A CFG in CNF is one where every rule is of the form: A ! BC A ! a Where a is any terminal and A,B, and C are any variables, except that B and C may not be the start variable. Note that we have two and only two variables on the right hand side of the rule, with the exception that the rule S!ε is permitted where S is the start variable. Theorem: Any context free language may be generated by a context-free grammar in Chomsky normal form. To show how to make this conversion, we will need to do three things: 1. Eliminate all ε rules of the form A!ε 2. Eliminate all unit rules of the form A!B 3. Convert remaining rules into rules of the form A!BC Proof: 1. First add a new start symbol S0 and the rule S0 ! S, where S was the original start symbol. This guarantees that the start symbol doesn’t occur on the right hand side of a rule. 2. Remove all ε rules. Remove a rule A!ε where A is not the start symbol For each occurrence of A on the right-hand side of a rule, add a new rule with that occurrence of A deleted. Ex: R!uAv becomes R!uv This must be done for each occurrence of A, so the rule: R!uAvAw becomes R! uvAw and R! uAvw and R!uvw This step must be repeated until all ε rules are removed, not including the start. -
Regular Expressions with a Brief Intro to FSM
Regular Expressions with a brief intro to FSM 15-123 Systems Skills in C and Unix Case for regular expressions • Many web applications require pattern matching – look for <a href> tag for links – Token search • A regular expression – A pattern that defines a class of strings – Special syntax used to represent the class • Eg; *.c - any pattern that ends with .c Formal Languages • Formal language consists of – An alphabet – Formal grammar • Formal grammar defines – Strings that belong to language • Formal languages with formal semantics generates rules for semantic specifications of programming languages Automaton • An automaton ( or automata in plural) is a machine that can recognize valid strings generated by a formal language . • A finite automata is a mathematical model of a finite state machine (FSM), an abstract model under which all modern computers are built. Automaton • A FSM is a machine that consists of a set of finite states and a transition table. • The FSM can be in any one of the states and can transit from one state to another based on a series of rules given by a transition function. Example What does this machine represents? Describe the kind of strings it will accept. Exercise • Draw a FSM that accepts any string with even number of A’s. Assume the alphabet is {A,B} Build a FSM • Stream: “I love cats and more cats and big cats ” • Pattern: “cat” Regular Expressions Regex versus FSM • A regular expressions and FSM’s are equivalent concepts. • Regular expression is a pattern that can be recognized by a FSM. • Regex is an example of how good theory leads to good programs Regular Expression • regex defines a class of patterns – Patterns that ends with a “*” • Regex utilities in unix – grep , awk , sed • Applications – Pattern matching (DNA) – Web searches Regex Engine • A software that can process a string to find regex matches. -
LING83600: Context-Free Grammars
LING83600: Context-free grammars Kyle Gorman 1 Introduction Context-free grammars (or CFGs) are a formalization of what linguists call “phrase structure gram- mars”. The formalism is originally due to Chomsky (1956) though it has been independently discovered several times. The insight underlying CFGs is the notion of constituency. Most linguists believe that human languages cannot be even weakly described by CFGs (e.g., Schieber 1985). And in formal work, context-free grammars have long been superseded by “trans- formational” approaches which introduce movement operations (in some camps), or by “general- ized phrase structure” approaches which add more complex phrase-building operations (in other camps). However, unlike more expressive formalisms, there exist relatively-efficient cubic-time parsing algorithms for CFGs. Nearly all programming languages are described by—and parsed using—a CFG,1 and CFG grammars of human languages are widely used as a model of syntactic structure in natural language processing and understanding tasks. Bibliographic note This handout covers the formal definition of context-free grammars; the Cocke-Younger-Kasami (CYK) parsing algorithm will be covered in a separate assignment. The definitions here are loosely based on chapter 11 of Jurafsky & Martin, who in turn draw from Hopcroft and Ullman 1979. 2 Definitions 2.1 Context-free grammars A context-free grammar G is a four-tuple N; Σ; R; S such that: • N is a set of non-terminal symbols, corresponding to phrase markers in a syntactic tree. • Σ is a set of terminal symbols, corresponding to words (i.e., X0s) in a syntactic tree. • R is a set of production rules. -
Generating Context-Free Grammars Using Classical Planning
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) Generating Context-Free Grammars using Classical Planning Javier Segovia-Aguas1, Sergio Jimenez´ 2, Anders Jonsson 1 1 Universitat Pompeu Fabra, Barcelona, Spain 2 University of Melbourne, Parkville, Australia [email protected], [email protected], [email protected] Abstract S ! aSa S This paper presents a novel approach for generating S ! bSb /|\ Context-Free Grammars (CFGs) from small sets of S ! a S a /|\ input strings (a single input string in some cases). a S a Our approach is to compile this task into a classical /|\ planning problem whose solutions are sequences b S b of actions that build and validate a CFG compli- | ant with the input strings. In addition, we show that our compilation is suitable for implementing the two canonical tasks for CFGs, string produc- (a) (b) tion and string recognition. Figure 1: (a) Example of a context-free grammar; (b) the corre- sponding parse tree for the string aabbaa. 1 Introduction A formal grammar is a set of symbols and rules that describe symbols in the grammar and (2), a bounded maximum size of how to form the strings of certain formal language. Usually the rules in the grammar (i.e. a maximum number of symbols two tasks are defined over formal grammars: in the right-hand side of the grammar rules). Our approach is compiling this inductive learning task into • Production : Given a formal grammar, generate strings a classical planning task whose solutions are sequences of ac- that belong to the language represented by the grammar. -
Formal Grammar Specifications of User Interface Processes
FORMAL GRAMMAR SPECIFICATIONS OF USER INTERFACE PROCESSES by MICHAEL WAYNE BATES ~ Bachelor of Science in Arts and Sciences Oklahoma State University Stillwater, Oklahoma 1982 Submitted to the Faculty of the Graduate College of the Oklahoma State University iri partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE July, 1984 I TheSIS \<-)~~I R 32c-lf CO'f· FORMAL GRAMMAR SPECIFICATIONS USER INTER,FACE PROCESSES Thesis Approved: 'Dean of the Gra uate College ii tta9zJ1 1' PREFACE The benefits and drawbacks of using a formal grammar model to specify a user interface has been the primary focus of this study. In particular, the regular grammar and context-free grammar models have been examined for their relative strengths and weaknesses. The earliest motivation for this study was provided by Dr. James R. VanDoren at TMS Inc. This thesis grew out of a discussion about the difficulties of designing an interface that TMS was working on. I would like to express my gratitude to my major ad visor, Dr. Mike Folk for his guidance and invaluable help during this study. I would also like to thank Dr. G. E. Hedrick and Dr. J. P. Chandler for serving on my graduate committee. A special thanks goes to my wife, Susan, for her pa tience and understanding throughout my graduate studies. iii TABLE OF CONTENTS Chapter Page I. INTRODUCTION . II. AN OVERVIEW OF FORMAL LANGUAGE THEORY 6 Introduction 6 Grammars . • . • • r • • 7 Recognizers . 1 1 Summary . • • . 1 6 III. USING FOR~AL GRAMMARS TO SPECIFY USER INTER- FACES . • . • • . 18 Introduction . 18 Definition of a User Interface 1 9 Benefits of a Formal Model 21 Drawbacks of a Formal Model . -
The Logic of Categorial Grammars: Lecture Notes Christian Retoré
The Logic of Categorial Grammars: Lecture Notes Christian Retoré To cite this version: Christian Retoré. The Logic of Categorial Grammars: Lecture Notes. RR-5703, INRIA. 2005, pp.105. inria-00070313 HAL Id: inria-00070313 https://hal.inria.fr/inria-00070313 Submitted on 19 May 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE The Logic of Categorial Grammars Lecture Notes Christian Retoré N° 5703 Septembre 2005 Thème SYM apport de recherche ISRN INRIA/RR--5703--FR+ENG ISSN 0249-6399 The Logic of Categorial Grammars Lecture Notes Christian Retoré ∗ Thème SYM — Systèmes symboliques Projet Signes Rapport de recherche n° 5703 — Septembre 2005 —105 pages Abstract: These lecture notes present categorial grammars as deductive systems, and include detailed proofs of their main properties. The first chapter deals with Ajdukiewicz and Bar-Hillel categorial grammars (AB grammars), their relation to context-free grammars and their learning algorithms. The second chapter is devoted to the Lambek calculus as a deductive system; the weak equivalence with context free grammars is proved; we also define the mapping from a syntactic analysis to a higher-order logical formula, which describes the seman- tics of the parsed sentence. -
Algorithm for Analysis and Translation of Sentence Phrases
Masaryk University Faculty}w¡¢£¤¥¦§¨ of Informatics!"#$%&'()+,-./012345<yA| Algorithm for Analysis and Translation of Sentence Phrases Bachelor’s thesis Roman Lacko Brno, 2014 Declaration Hereby I declare, that this paper is my original authorial work, which I have worked out by my own. All sources, references and literature used or excerpted during elaboration of this work are properly cited and listed in complete reference to the due source. Roman Lacko Advisor: RNDr. David Sehnal ii Acknowledgement I would like to thank my family and friends for their support. Special thanks go to my supervisor, RNDr. David Sehnal, for his attitude and advice, which was of invaluable help while writing this thesis; and my friend František Silváši for his help with the revision of this text. iii Abstract This thesis proposes a library with an algorithm capable of translating objects described by natural language phrases into their formal representations in an object model. The solution is not restricted by a specific language nor target model. It features a bottom-up chart parser capable of parsing any context-free grammar. Final translation of parse trees is carried out by the interpreter that uses rewrite rules provided by the target application. These rules can be extended by custom actions, which increases the usability of the library. This functionality is demonstrated by an additional application that translates description of motifs in English to objects of the MotiveQuery language. iv Keywords Natural language, syntax analysis, chart parsing, -
Finite-State Automata and Algorithms
Finite-State Automata and Algorithms Bernd Kiefer, [email protected] Many thanks to Anette Frank for the slides MSc. Computational Linguistics Course, SS 2009 Overview . Finite-state automata (FSA) – What for? – Recap: Chomsky hierarchy of grammars and languages – FSA, regular languages and regular expressions – Appropriate problem classes and applications . Finite-state automata and algorithms – Regular expressions and FSA – Deterministic (DFSA) vs. non-deterministic (NFSA) finite-state automata – Determinization: from NFSA to DFSA – Minimization of DFSA . Extensions: finite-state transducers and FST operations Finite-state automata: What for? Chomsky Hierarchy of Hierarchy of Grammars and Languages Automata . Regular languages . Regular PS grammar (Type-3) Finite-state automata . Context-free languages . Context-free PS grammar (Type-2) Push-down automata . Context-sensitive languages . Tree adjoining grammars (Type-1) Linear bounded automata . Type-0 languages . General PS grammars Turing machine computationally more complex less efficient Finite-state automata model regular languages Regular describe/specify expressions describe/specify Finite describe/specify Regular automata recognize languages executable! Finite-state MACHINE Finite-state automata model regular languages Regular describe/specify expressions describe/specify Regular Finite describe/specify Regular grammars automata recognize/generate languages executable! executable! • properties of regular languages • appropriate problem classes Finite-state • algorithms for FSA MACHINE Languages, formal languages and grammars . Alphabet Σ : finite set of symbols Σ . String : sequence x1 ... xn of symbols xi from the alphabet – Special case: empty string ε . Language over Σ : the set of strings that can be generated from Σ – Sigma star Σ* : set of all possible strings over the alphabet Σ Σ = {a, b} Σ* = {ε, a, b, aa, ab, ba, bb, aaa, aab, ...} – Sigma plus Σ+ : Σ+ = Σ* -{ε} Strings – Special languages: ∅ = {} (empty language) ≠ {ε} (language of empty string) . -
Cs.FL] 7 Dec 2020 4 a Model Programming Language and Its Grammar 11 4.1 Alphabet
Describing the syntax of programming languages using conjunctive and Boolean grammars∗ Alexander Okhotin† December 8, 2020 Abstract A classical result by Floyd (\On the non-existence of a phrase structure grammar for ALGOL 60", 1962) states that the complete syntax of any sensible programming language cannot be described by the ordinary kind of formal grammars (Chomsky's \context-free"). This paper uses grammars extended with conjunction and negation operators, known as con- junctive grammars and Boolean grammars, to describe the set of well-formed programs in a simple typeless procedural programming language. A complete Boolean grammar, which defines such concepts as declaration of variables and functions before their use, is constructed and explained. Using the Generalized LR parsing algorithm for Boolean grammars, a pro- gram can then be parsed in time O(n4) in its length, while another known algorithm allows subcubic-time parsing. Next, it is shown how to transform this grammar to an unambiguous conjunctive grammar, with square-time parsing. This becomes apparently the first specifica- tion of the syntax of a programming language entirely by a computationally feasible formal grammar. Contents 1 Introduction 2 2 Conjunctive and Boolean grammars 5 2.1 Conjunctive grammars . .5 2.2 Boolean grammars . .6 2.3 Ambiguity . .7 3 Language specification with conjunctive and Boolean grammars 8 arXiv:2012.03538v1 [cs.FL] 7 Dec 2020 4 A model programming language and its grammar 11 4.1 Alphabet . 11 4.2 Lexical conventions . 12 4.3 Identifier matching . 13 4.4 Expressions . 14 4.5 Statements . 15 4.6 Function declarations . 16 4.7 Declaration of variables before use . -
Appendix A: Hierarchy of Grammar Formalisms
Appendix A: Hierarchy of Grammar Formalisms The following figure recalls the language hierarchy that we have developed in the course of the book. '' $$ '' $$ '' $$ ' ' $ $ CFG, 1-MCFG, PDA n n L1 = {a b | n ≥ 0} & % TAG, LIG, CCG, tree-local MCTAG, EPDA n n n ∗ L2 = {a b c | n ≥ 0}, L3 = {ww | w ∈{a, b} } & % 2-LCFRS, 2-MCFG, simple 2-RCG & % 3-LCFRS, 3-MCFG, simple 3-RCG n n n n n ∗ L4 = {a1 a2 a3 a4 a5 | n ≥ 0}, L5 = {www | w ∈{a, b} } & % ... LCFRS, MCFG, simple RCG, MG, set-local MCTAG, finite-copying LFG & % Thread Automata (TA) & % PMCFG 2n L6 = {a | n ≥ 0} & % RCG, simple LMG (= PTIME) & % mildly context-sensitive L. Kallmeyer, Parsing Beyond Context-Free Grammars, Cognitive Technologies, DOI 10.1007/978-3-642-14846-0, c Springer-Verlag Berlin Heidelberg 2010 216 Appendix A For each class the different formalisms and automata that generate/accept exactly the string languages contained in this class are listed. Furthermore, examples of typical languages for this class are added, i.e., of languages that belong to this class while not belonging to the next smaller class in our hier- archy. The inclusions are all proper inclusions, except for the relation between LCFRS and Thread Automata (TA). Here, we do not know whether the in- clusion is a proper one. It is possible that both devices yield the same class of languages. Appendix B: List of Acronyms The following table lists all acronyms that occur in this book. (2,2)-BRCG Binary bottom-up non-erasing RCG with at most two vari- ables per left-hand side argument 2-SA Two-Stack Automaton