Extension of Context Free Grammar for Intermediate Code and Peephole Optimization Rule Parsers

This paper describes a system that accepts compact specifications of an intermediate code and target machine and produces program code for an integrated code generator and peephole optimizer. A compiler for most of C uses this packa.ge. It emits code comparable to PCCI’S, but it runs over five times faster on preliminary benchmarks. This compiler also runs over twice as fast as a version of pcc2 with a hand-coded, VAX-specific code generator. The code generators are produced as follows. A programmer describes a naive code generator by means of a non-procedural specification. The programmer also prepares a machine description for a retargetable peephole optimizer [2]. These two systems are used together to compile a testbed, and the compiler records each peephole optimization as it is made. This record and the specification of the naive code generator are compiled into a fast, integrated code generator and optimizer. This production code generator then takes the place of the slower “training” version. The production code generator and optimizer are integrated to the point that the code to be generated is communicated from one to the other by encoding it in the program counter, which obviates most inter-phase communication costs. Interpretive peephole optimizers have been driven by traces from retargetable peephole optimizers [3] and integrated with interpretive code generators [4], but the current work is distinguished by the production of a hard-coded, optimizing code generator. Historically, retargetable code generators (i.e., those not largely rewritten for each new machine)

Many graph analysis problems can be formulated as formal language-constrained path querying problems where the formal languages are used as constraints for navigational path queries. Recently, the context-free language (CFL) reachability formulation has become very popular and can be used in many areas, for example, querying graph databases, Resource Description Framework (RDF) analysis. However, the generative capacity of context-free grammars (CFGs) is too weak to generate some complex queries, for example, from natural languages, and the various extensions of CFGs have been proposed. Multiple context-free grammar (MCFG) is one of such extensions of CFGs. Despite the fact that, to the best of our knowledge, there is no algorithm for MCFL-reachability, this problem is known to be decidable. This paper is devoted to developing the first such algorithm for the MCFL-reachability problem. The essence of the proposed algorithm is to use a set of Boolean matrices and operations on them to find paths in a graph that satisfy the given constraints. The main operation here is Boolean matrix multiplication. As a result, the algorithm returns a set of matrices containing all information needed to solve the MCFL-reachability problem. The presented algorithm is implemented in Python using GraphBLAS API. An analysis of real RDF data and synthetic graphs for some MCFLs is performed. The study showed that using a sparse format for matrix storage and parallel computing for graphs with tens of thousands of edges the analysis time can be 10–20 minutes. The result of the analysis provides tens of millions of reachable vertex pairs. The proposed algorithm can be applied in problems of static code analysis, bioinformatics, network analysis, as well as in graph databases when a path query cannot be expressed using context-free grammars. The provided algorithm is linear algebra-based, hence, it allows one to use high-performance libraries and utilize modern parallel hardware.

The Generalized LR parsing algorithm for context-free grammars is notable for having a decent worst-case running time (cubic in the length of the input string, if implemented efficiently), as well as much better performance on “good” grammars. This paper extends the Generalized LR algorithm to the case of “grammars with left contexts” (M. Barash, A. Okhotin, “An extension of context-free grammars with one-sided context specifications”, Inform. Comput., 2014), which augment the context-free grammars with special operators for referring to the left context of the current substring, along with a conjunction operator (as in conjunctive grammars) for combining syntactical conditions. All usual components of the LR algorithm, such as the parsing table, shift and reduce actions, etc., are extended to handle the context operators. The resulting algorithm is applicable to any grammar with left contexts and has the same worst-case cubic-time performance as in the case of context-free grammars.

Like most of the South-Asian languages Urdu and Sindhi are partial word order languages. Conventional syntax representation models like Context Free Grammars are not capable enough to cope with partial word order syntax. Linear Specification Language (LSL) is an extension of Context-Free Grammars (CFGs) which allows arbitrary partial order (free word order) on the right hand side of grammar rule. Partial word order in LSL is handled by using different types of linear precedence (LP) constraints. LSL by using LP constraints is capable enough to represent the syntax of partial word order sentence. Issues related to represent Urdu/Sindhi language sentences with their constituent parts in LSL are discussed. LSL versions for different types of Urdu and Sindhi sentences are presented.

In this article we study the time complexity of the recognition problem for some “moderate” extensions of context-free grammars or pushdown automata. It is well known that, for a given context-free grammar G, the recognition problem “x ∈ L(G)” can be decided in 0(|x|)3 steps by a suitable algorithm. How do extensions behave in this respect? In particular, do they admit recognition algorithms whose time is polynomially bounded by the length of the input?

Permitting semi-conditional grammars (pSCGs) are extensions of context-free grammars where each rule is associated with a word [Formula: see text] and such a rule can be applied to a sentential form [Formula: see text] only if [Formula: see text] is a subword of [Formula: see text]. We consider permitting generalized SCGs (pgSCGs) where each rule [Formula: see text] is associated with a set of words [Formula: see text] and [Formula: see text] is applicable only if every word in [Formula: see text] occurs in [Formula: see text]. We investigate the generative power of pgSCGs with no erasing rules and prove a pumping lemma for their languages. Using this lemma we show that pgSCGs are strictly weaker than context-sensitive grammars. This solves a long-lasting open problem concerning the generative power of pSCGs. Moreover, we give a comparison of the generating power of pgSCGs and that of forbidding random context grammars with no erasing rules.

We introduce annotated grammars, an extension of context-free grammars which allows annotations on terminals. Our model extends the standard notion of regular spanners, and is more expressive than the extraction grammars recently introduced by Peterfreund. We study the enumeration problem for annotated grammars: fixing a grammar, and given a string as input, enumerate all annotations of the string that form a word derivable from the grammar. Our first result is an algorithm for unambiguous annotated grammars, which preprocesses the input string in cubic time and enumerates all annotations with output-linear delay. This improves over Peterfreund's result, which needs quintic time preprocessing to achieve this delay bound. We then study how we can reduce the preprocessing time while keeping the same delay bound, by making additional assumptions on the grammar. Specifically, we present a class of grammars which only have one derivation shape for all outputs, for which we can enumerate with quadratic time preprocessing. We also give classes that generalize regular spanners for which linear time preprocessing suffices.

This doctoral thesis presents a framework for machine based interpretation of Malaysian Sign Language (MSL). The goal is to explore and develop necessary core algorithms that are capable of recognizing hand signs, based on the monocular/2D visual input of a low-cost hardware (Digital Webcam), without the assistance of visual-markers on hands. Successful hand sign interpretation with the aforementioned constrained visual system is challenging. Economical hardware devices often provide noisy/cluttered raw data with limited useful information. Non-involvement of visual markers imposes the requirement of additional strategies to disambiguate occlusions and background complexities. Natural variations in hand posture/gesture performances further convolute the recognition task. Addressing these challenges require computationally heavy algorithms in image processing, machine learning and pattern recognition. Thus vision-based sign language translator frameworks have to be multifaceted and versatile, in order to address innumerable practical concerns. In this research, hand postures interpretation is achieved by statistical projection methods. Analysis of high dimensional hand shape data is a complex task. Thus reducing dimensions via known projection methods aids in alleviating some of these complexities. Locality Preserving Projection (LPP) has been employed to accomplish this goal, while Principal Component Analysis (PCA) has been used as the benchmark method. Both methods were extensively tested against a hand posture database of 24 alphabet signs from MSL. As for the hand gesture interpretation, parameter-less Gaussian process based machine learning methods are proposed, as an alternative to current status quo. A variation of the Gaussian latent variable model – Gaussian Process Dynamic Model (GPDM) – has been employed for hand gesture interpretation. This has been comparatively studied against the established Hidden Markov Model (HMM). These machine learning methods were thoroughly tested against a hand gesture database of 64 signs from MSL. Grammatical sentence construction offers both intelligible sentences, and a mean of identifying erroneous sign classifications. In this thesis, a proof-of-concept on: use of Context Free Grammar (CFG) in successful parsing of meaningful sentences based on recognised gesture words, and how it can be extended in identifying (and correcting) erroneous sign classifications, is demonstrated with 92 sentences and the output of GPDM gesture classification. The novelty and contribution of this research: this is the first undertaking of markerless vision-based translation for MSL. Both LPP and GPDM are introduced to the sign language research field as an outcome of this investigation. The preliminary study and demonstration of grammatical sentence construction for translated gesture signs, is the first of its kind to the best of author's knowledge.

Compiler Design is one of the core courses of Computer Science and Engineering. Syntax Analyzer or Parser is important phase in designing the compiler. So while designing this phase of the compiler, students must have thorough knowledge about the types of parser such as LL(1), SLR, Canonical LR and LALR. In the parser phase, the parsing table is built for given Context Free Grammar (CFG). While implementing this parsing phase in laboratory session, two Free Open Source Software (FOSS) - JFLAP and Parsing Emulator, are considered to explain the types of parser and building the parsing table for the CFG using these parser types. JFLAP is software for experimenting with formal languages topics including nondeterministic finite automata, nondeterministic pushdown automata, multi-tape Turing machines, several types of grammars, parsing, and Lsystems. So this tool is considered for parsing phase of Compiler. JFLAP tool is used to explain the steps to be followed to solve the given problem using the parsing technique. Parsing simulator is a software which implements the parsing table for the Context Free Grammars in tool. This simulator is used to generate parsing table (LL1, SLR, LR, and LALR). Parsing Emulator tool is used for practicing the problem statement given in the tool. In current study, use of FOSS such as JFLAP and Parsing Emulator is considered for teaching parser types to improve problem solving ability. The Learning Objectives (LOs) of this study are: To build the parsing table for given CFG using LL(1) and SLR parser (LO1) and to parse the given string using LL(1) and SLR parser (LO2). The research questions for this study are – whether the use of FOSS improves the problem solving ability of the students? and whether gender bias is there in education? Two groups post-test method is considered to check the effectiveness of this use of FOSS in learning the parser types. Also the students’ perception about this method is also considered. The result shows that the students’ problem solving ability of experimental group is improved as compared to the control group irrespective of gender. Keywords- FOSS (Free Open Source Software) JFLAP, Parsing Emulator, Bloom’s Taxonomy, Post-test, t-Test.

Context-free grammars and cubic parse time are so related in people's minds that they often think that parsing any extension of context-free grammars must need some extra time. Of course, this is not necessarily true and this paper presents a generalization of context-free grammars which nonetheless still has a cubic parse time complexity. This extension, which defines a subclass of context-sensitive languages, has both a theoretical and a practical interest. The class of languages defined by these grammars is closed under both intersection and complement (in fact this class contains both the intersection and the complement of context-free languages). Moreover, these languages belong to an extension of mildly context-sensitive languages in which the constant growth property is relaxed and which can thus potentially be used in natural language processing.

The semantic analysis phase of a compiler is the last phase directly concerned with the analysis of the source program. The syntax analyser has produced a syntax tree or some equivalent data structure and the next step is to deal with all those remaining analysis tasks that are difficult or impossible to do in a conventional syntax analyser. These tasks are principally concerned with context-sensitive analysis.

This paper introduces an approach for extracting sentence elements from Slovak sentences based on linguistic analysis. The key idea lies in the assumption that the sentence elements relates to the meaning and they can be helpful in the process of the semantic roles identification. The system for extracting sentence elements from Slovak sentences has been developed with a morphological analyzer, disambiguator and syntactic analyzer as fundamental components. The morphological analyzer uses data obtained from the Slovak National Corpus. The syntactic analyzer uses context-free grammars. Several evaluation experiments were done with limited range of sentences for obtaining information about success of the proposed approach.KeywordsNatural language understandingsentence elementsparsertagger

A new natural language system, TINA, has been developed for applications involving speech understanding tasks, which integrates key ideas from context free grammars, Augmented Transition Networks (ATN's) [1], and Lexical Functional Grammars (LFG's) [2]. The parser uses a best-first search strategy, with probability assignments on all arcs obtained automatically from a set of example sentences. An initial context-free grammar, derived from the example sentences, is first converted to a probabilistic network structure. Control includes both top-down and bottom-up cycles, and key parameters are passed among nodes to deal with long-distance movement and agreement constraints. The probabilities provide a natural mechanism for exploring more common grammatical constructions first. Arc probabilities also reduced test-set perplexity by nearly an order of magnitude. Included is a new strategy for dealing with movement, which can handle efficiently nested and chained gaps, and rejects crossed gaps.

This paper describes the development of parser algorithm which is used for Hindi-English machine translation (MT). Machine translation requires analysis, transfer and generation steps to produce target language output from a source language input. Structural representation of Hindi sentences codes the information of Hindi sentences and a transfer module can be designed to generate English sentences using Context Free Grammar (CFG). Context Free Grammar Parsing technique is a tool in which (CKY) Coke-Kasami-Younger algorithms are used to analyze the structure of the sentence when user input the Hindi text in Unicode format it verifies the Hindi text according to the correct grammar. Coke-Kasami-Younger algorithms produce better result 91.4% by enhancing the grammatical rule in databases and resolving issues in parsing the sentence according to the grammatical structure like root form of the word, category, masculine/feminine/neuter, oblique, direct case, suffix.

This paper describes an intermediate language to be generated by a syntax analyzer and processed by a code generator. An (essentially) optional code optimization phase may be used before code generation. The language is designed to exclude source and target dependencies (these are specified in a set of auxiliary tables) and has been used to implement a general purpose code optimization module. This module has been incorporated into compilers for several source languages and has resulted in production-quality target code.The paper focuses on the features of the language and reviews the optimizations for which it has been designed. A major benefit of this approach has been to isolate many of the characteristics which lead to code optimization opportunities. Of particular note are numerous facts which can be gathered in a target-independent manner, even though their utilization will depend on the details of the target machine.