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Arabic is the main language of Al-Quran. Nowadays, many people are studying the Language of Al-Quran, called Quran Arabic. For the beginners, it is important for them to understand the syntactic relationship in a sentence found in the Qur'an. If they do not understand enough, the interpretation will be different and wrong. It will turn into dangerous because Al-Quran is a source of guidance for Muslims’ life. Dependency parsing is very important for linguistic research, especially for rich languages such as the Arabic Language. This study aims to build dependency parsing, in order to make it easier to get to understand syntactic relationship information in sentences. This study uses a parsing method called deterministic parsing, which the method used is shift-reduce parsing with the Easy-First parsing algorithm. The evaluation used labeled attachment score calculation. The score generated from the evaluation was 69.7, beforehand, the comparison both the system results and the gold standard have been done. 62 sentences found the correct head and relation in each word. The number of words found to be wrong is not more than 3 words in one sentence. Evaluation scores produced are not exorbitant due to the complicated tagset used and lacking test sentences.

We present a framework for C code in C11 syntax deeply integrated into the Isabelle/PIDE development environment. Our framework provides an abstract interface for verification back-ends to be plugged-in independently. Thus, various techniques such as deductive program verification or white-box testing can be applied to the same source, which is part of an integrated PIDE document model. Semantic back-ends are free to choose the supported C fragment and its semantics. In particular, they can differ on the chosen memory model or the specification mechanism for framing conditions. Our framework supports semantic annotations of C sources in the form of comments. Annotations serve to locally control back-end settings, and can express the term focus to which an annotation refers. Both the logical and the syntactic context are available when semantic annotations are evaluated. As a consequence, a formula in an annotation can refer both to HOL or C variables. Our approach demonstrates the degree of maturity and expressive power the Isabelle/PIDE subsystem has achieved in recent years. Our integration technique employs Lex and Yacc style grammars to ensure efficient deterministic parsing. We present two case studies for the integration of (known) semantic back-ends in order to validate the design decisions for our back-end interface., Comment: In Proceedings F-IDE 2019, arXiv:1912.09611

Regular languages (RL) are the simplest family in Chomsky’s hierarchy. Thanks to their simplicity they enjoy various nice algebraic and logic properties that have been successfully exploited in many application fields. Practically all of their related problems are decidable, so that they support automatic verification algorithms. Also, they can be recognized in real-time. Context-free languages (CFL) are another major family well-suited to formalize programming, natural, and many other classes of languages; their increased generative power w.r.t. RL, however, causes the loss of several closure properties and of the decidability of important problems; furthermore they need complex parsing algorithms. Thus, various subclasses thereof have been defined with different goals, spanning from efficient, deterministic parsing to closure properties, logic characterization and automatic verification techniques. Among CFL subclasses, so-called structured ones, i.e., those where the typical tree-structure is visible in the sentences, exhibit many of the algebraic and logic properties of RL, whereas deterministic CFL have been thoroughly exploited in compiler construction and other application fields. After surveying and comparing the main properties of those various language families, we go back to operator precedence languages (OPL), an old family through which R. Floyd pioneered deterministic parsing, and we show that they offer unexpected properties in two fields so far investigated in totally independent ways: they enable parsing parallelization in a more effective way than traditional sequential parsers, and exhibit the same algebraic and logic properties so far obtained only for less expressive language families.

We investigate the possibility of the deterministic parsing (that is, parsing without backtracking) of languages described by (generalized) P colony automata. We define a subclass of these computing devices satisfying a property which resembles the LL(k) property of context-free grammars, and study the possibility of parsing the characterized languages using a k symbol lookahead, as in the LL(k) parsing method for context-free languages.

Purpose: In defining the biological effects of the 10B(n, α)7Li neutron capture reaction, we have proposed a deterministic parsing model (ISHIYAMA-IMAHORI model) to determine the Compound Biological Effectiveness (CBE) factor in Borono-Phenyl-Alanine (BPA)-mediated Boron Neutron Capture Therapy (BNCT). In present paper, we demonstrate a specific method of how the application of the case of application to actual patient data, which is founded on this model for tissues and tumor. Method: To determine the CBE factor, we derived the following new calculation formula founded on the deterministic parsing model with three constants, CBE0, F, n and the eigen value Nth/Nmax. (1), where, Nth and Nmax are the threshold value of boron concentration of N and saturation boron density and CBE0, F and n are given as 0.5, 8 and 3, respectively. In order to determine Nth and Nmax in the formula, sigmoid logistic function was employed for 10B concentration data, Db(t) obtained by dynamic PET technique. (2), where, A, a and t0 are constants. Results and Conclusion: From the application of sigmoid function to dynamic PET data, it is concluded that the Nth and Nmax for tissue and tumor are identified with the parameter constants in the sigmoid function in Equation (2) as: (3). And the calculated CBE factor values obtained from Equation (1), with Nth/Nmax.

Purpose: In defining the biological effects of the 10 B(n, α) 7 Li neutron capture reaction, we have previously developed a deterministic parsing model to determine the Compound Biological Effectiveness (CBE) factor in Borono-Phenyl-Alanine (BPA)-mediated Boron Neutron Capture Therapy (BNCT). In present paper, we demonstrate that the CBE factor is directly and unambiguously derivable by the new formula for any case of intracellular 10 Boron ( 10 B) distribution, which is founded on this model for tissues and tumor. Method: To determine the CBE factor, we derive the following new calculation formula founded on the deterministic parsing model with three constants, CBE0, F, n and the eigen value Nth/Nmax.

A deterministic parallel LL parsing algorithm is presented. The algorithm is based on a transformation from a parsing problem to parallel reduction. First, a nondeterministic version of a parallel LL parser is introduced. Then, it is transformed into the deterministic version—the LLP parser. The deterministic LLP(q,k) parser uses two kinds of information to select the next operation — a lookahead string of length up to k symbols and a lookback string of length up to q symbols. Deterministic parsing is available for LLP grammars, a subclass of LL grammars. Since the presented deterministic and nondeterministic parallel parsers are both based on parallel reduction, they are suitable for most parallel architectures.

The increasing use of multicore processors has deeply transformed computing paradigms and applications. The wide availability of multicore systems had an impact also in the field of compiler technology, although the research on deterministic parsing did not prove to be effective in exploiting the architectural advantages, the main impediment being the inherent sequential nature of traditional LL and LR algorithms. We present PAPAGENO, an automated parser generator relying on operator precedence grammars. We complemented the PAPAGENO-generated parallel parsers with parallel lexing techniques, obtaining near-linear speedups on multicore machines, and the same speed as Bison parsers on sequential execution.

Recent literature extended the analysis of ω-languages from the regular ones to various classes of languages with “visible syntax structure”, such as visibly pushdown languages (VPLs). Operator precedence languages (OPLs), instead, were originally defined to support deterministic parsing and exhibit interesting relations with these classes of languages: OPLs strictly include VPLs, enjoy all relevant closure properties and have been characterized by a suitable automata family and a logic notation. We introduce here operator precedence ω-languages (ωOPLs), investigating various acceptance criteria and their closure properties. Whereas some properties are natural extensions of those holding for regular languages, others require novel investigation techniques.Application-oriented examples show the gain in expressiveness and verifiability offered by ωOPLs w.r.t. smaller classes.

This paper describes an implementation of a deterministic parsing system, described in Nordgard (1993). The syntax of “heuristic rules” and how the rules interact with the basic operations of the parser constitute the bulk of the article. The implementation is written in Medley Interlisp, and the system can be run on Sun or Xerox workstations.

One obstacle to the implementation of modular extensions to programming languages lies in the problem of parsing extended languages. Specifically, the parse tables at the heart of traditional LALR(1) parsers are so monolithic and tightly constructed that, in the general case, it is impossible to extend them without regenerating them from the source grammar. Current extensible frameworks employ a variety of solutions, ranging from a full regeneration to using pluggable binary modules for each different extension. But recompilation is time-consuming, while the pluggable modules in many cases cannot support the addition of more than one extension, or use backtracking or non-deterministic parsing techniques. We present here a middle-ground approach that allows an extension, if it meets certain restrictions, to be compiled into a parse table fragment. The host language parse table and fragments from multiple extensions can then always be efficiently composed to produce a conflict-free parse table for the extended language. This allows for the distribution of deterministic parsers for extensible languages in a pre-compiled format, eliminating the need for the “source code” grammar to be distributed. In practice, we have found these restrictions to be reasonable and admit many useful language extensions.

In Japanese dependency parsing, Kudo's relative preference-based method (Kudo and Matsumoto, 2005) outperforms both deterministic and probabilistic CKY-based parsing methods. In Kudo's method, for each dependent word (or chunk) a log-linear model estimates relative preference of all other candidate words (or chunks) for being as its head. This cannot be considered in the deterministic parsing methods. We propose an algorithm based on a tournament model, in which the relative preferences are directly modeled by one-on-one games in a step-ladder tournament. In an evaluation experiment with Kyoto Text Corpus Version 4.0, the proposed method outperforms previous approaches, including the relative preference-based method.

The major problems in parsing English conjunctions and comparatives are ambiguities of scoping and ellipsis. Scoping ambiguities occur when a parser cannot deterministically detect boundaries of constituents, while ellipsis ambiguities occur when a parser cannot deterministically detect missing components. Since simple lookahead mechanisms cannot collect adequate information to resolve these ambiguities, a parsing strategy that only employs such mechanisms will need to backtrack each time it makes incorrect assumptions. In this paper, we extend the Wait-And-See strategy to parse conjunctions and comparatives deterministically and simultaneously. Several mechanisms, such as bottom-up preparsing, suspension, and pattern matching, are implemented. The bottom-up preparsing accesses the dictionary and recognizes isolated sentence fragments which can be determined without ambiguities. The suspension, which is different from Marcus’s attention shifting, allows the parser to suspend temporally at ambiguous points and continue to parse the rest of the sentence until it obtains the necessary information to resolve the ambiguities. Pattern matching uses the concept of symmetry to detect missing components (the ellipses) in the two conjoined or compared sentence fragments.

This paper presents a three-step dependency parser to parse Chinese deterministically. By dividing a sentence into several parts and parsing them separately, it aims to reduce the error propagation coming from the greedy characteristic of deterministic parsing. Experimental results showed that compared with the deterministic parser which parsed a sentence in sequence, the proposed parser achieved extremely significant improvement on dependency accuracy.

This paper presents a deterministic parsing algorithm for projective dependency grammar. In a bottom-up way the algorithm finds the local optimum dynamically. A constraint procedure is made to use more structure information. The algorithm parses sentences in linear time and labeling is integrated with the parsing. This parser achieves 63.29% labeled attachment score on the average in CoNLL-X Shared Task.

Deterministic parsing guided by treebank-induced classifiers has emerged as a simple and efficient alternative to more complex models for data-driven parsing. We present a systematic comparison of memory-based learning (MBL) and support vector machines (SVM) for inducing classifiers for deterministic dependency parsing, using data from Chinese, English and Swedish, together with a variety of different feature models. The comparison shows that SVM gives higher accuracy for richly articulated feature models across all languages, albeit with considerably longer training times. The results also confirm that classifier-based deterministic parsing can achieve parsing accuracy very close to the best results reported for more complex parsing models.

Deterministic parsing techniques are typically used in favor of general parsing algorithms for efficiency reasons. However, general algorithms such as Earley's method are more powerful and also easier for developers to use, because no seemingly arbitrary restrictions are placed on the grammar. We describe how to narrow the performance gap between general and deterministic parsers, constructing a directly-executable Earley parser that can reach speeds comparable to deterministic methods even on grammars for commonly-used programming languages.

The suitability of different parsing methods for different languages is an important topic in syntactic parsing. Especially lesser-studied languages, typologically different from the languages for which methods have originally been developed, pose interesting challenges in this respect. This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative, free constituent order language that can be seen as the representative of a wider class of languages of similar type. Our investigations show that morphological structure plays an essential role in finding syntactic relations in such a language. In particular, we show that employing sublexical units called inflectional groups, rather than word forms, as the basic parsing units improves parsing accuracy. We test our claim on two different parsing methods, one based on a probabilistic model with beam search and the other based on discriminative classifiers and a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless of the parsing method. We examine the impact of morphological and lexical information in detail and show that, properly used, this kind of information can improve parsing accuracy substantially. Applying the techniques presented in this article, we achieve the highest reported accuracy for parsing the Turkish Treebank.

This paper describes an approach for determining the minimum, maximum, and average times to parse sentences acceptable by a deterministic parser. These quantities are presented in the form of symbolic formulas, called time-formulas. The variables in these formulas represent not only the length of the input string but also the time to perform elementary operations such as pushing, popping, subscripting, iterating, etc. By binding to the variables actual numerical values corresponding to a given compiler-machine configuration, one can determine the execution time for that configuration. Time-formulas are derived by examining the grammar rules and the program representing the algorithm one wishes to analyze. The approach is described by using a specific grammar that defines simple arithmetic expressions. Two deterministic parsers are analyzed: a top-down recursive descent LL(1) parser, and a bottom-up SLR(1) parser. The paper provides estimates for the relative efficiencies of the two parsers. The estimates applicable to a specific machine, the PDP-10, are presented and substantiated by benchmarks. Finally, the paper illustrates the proposed approach by applying it to the analyses of parsers for a simple programming language.

This paper presents a model for deterministic parsing which was designed to simplify the task of writing and understanding a deterministic grammar. While retaining structures and operations similar to those of Marcus' PARSIFAL parser [Marcus 80] the grammar language incorporates the following changes. (1) The use of productions operating in parallel has essentially been eliminated and instead the productions are organized into sequences. Not only does this improve the understandability of the grammar, it is felt that this organization corresponds more closely to the task of performing the sequence of buffer transformations and attachments required to parse the most common constituent types. (2) A general method for interfacing between the parser and a semantic representation system is introduced. This interface is independent of the particular semantic representation used and hides all details of the semantic processing from the grammar writer. (3) The interface also provides a general method for dealing with syntactic ambiguities which arise from the attachment of optional modifiers such as prepositional phrases. This frees the grammar writer from determining each point at which such ambiguities can occur.

It is often remarked that natural language, used naturally, is unnaturally ungrammatical. *Spontaneous speech contains all manner of false starts, hesitations, and self-corrections that disrupt the well-formedness of strings. It is a mystery then, that despite this apparent wide deviation from grammatical norms, people have little difficulty understanding the non-fluent speech that is the essential medium of everyday life. And it is a still greater mystery that children can succeed in acquiring the grammar of a language on the basis of evidence provided by a mixed set of apparently grammatical and ungrammatical strings.

MLR, an extended LR parser, is introduced, and its application to natural language parsing is discussed. An LR parser is a shift-reduce parser which is deterministically guided by a parsing table. A parsing table can be obtained automatically from a context-free phrase structure grammar. LR parsers cannot manage ambiguous grammars such as natural language grammars, because their parsing tables would have multiply-defined entries, which precludes deterministic parsing. MLR, however, can handle multiply-defined entries, using a dynamic programming method. When an input sentence is ambiguous, the MLR parser produces all possible parse trees without parsing any part of the input sentence more than once in the same way, despite the fact that the parser does not maintain a chart as in chart parsing. Our method also provides an elegant solution to the problem of multi-part-of-speech words such as "that". The MLR parser and its parsing table generator have been implemented at Carnegie-Mellon University.

Linguists, including computational linguists, have always been fond of talking about trees. In this paper, we outline a theory of linguistic structure which talks about talking about trees; we call this theory Description theory (D-theory). While important issues must be resolved before a complete picture of D-theory emerges (and also before we can build programs which utilize it), we believe that this theory will ultimately provide a framework for explaining the syntax and semantics of natural language in a manner which is intrinsically computational. This paper will focus primarily on one set of motivations for this theory, those engendered by attempts to handle certain syntactic phenomena within the framework of deterministic parsing.

Marcus' original deterministic parsing included almost no part-of-speech ambiguity. In this paper, the addition of part-of-speech ambiguity to a deterministic parser written in Prolog is described. To handle this ambiguity, it was necessary to add no special mechanisms to the parser. Instead the grammar rules were made to enforce agreement, and reject ungrammatical sentences. The resulting system is very effective and covers many examples of ambiguity.

This paper assesses two new approaches to deterministic parsing with respect to the analysis of unbounded dependencies (UDs). UDs in English are highly locally (and often globally) ambiguous. Several researchers have argued that the difficulty of UDs undermines the programme of deterministic parsing. However, their conclusion is based on critiques of various versions of the Marcus parser which represents only one of many possible approaches to deterministic parsing. We examine the predictions made by a LR(1) deterministic parser and the Lexicat deterministic parser concerning the analysis of UDs. The LR(1) technique is powerful enough to resolve the local ambiguities we examine. However, the Lexicat model provides a more psychologically plausible account of the parsing of UDs, which also offers a unified account of the resolution of local and global ambiguities in these constructions.

To support incremental interpretation, any model of human sentence processing must not only process the sentence incrementally, it must to some degree restrict the number of analyses which it produces for any sentence prefix. Deterministic parsing takes the extreme position that there can only be one analysis for any sentence prefix. Experiments with an incremental statistical parser show that performance is severely degraded when the search for the most probable parse is pruned to only the most probable analysis after each prefix. One method which has been extensively used to address the difficulty of deterministic parsing is lookahead, where information about a bounded number of subsequent words is used to decide which analyses to pursue. We simulate the effects of lookahead by summing probabilities over possible parses for the lookahead words and using this sum to choose which parse to pursue. We find that a large improvement is achieved with one word lookahead, but that more lookahead results in relatively small additional improvements. This suggests that one word lookahead is sufficient, but that other modifications to our left-corner parsing model could make deterministic parsing more effective.

"A Theory of Syntactic Recognition for Natural Language" makes use of the hypothesis that the syntax of any natural language can be parsed by a mechanism which operates "strictly deterministically" in that it does not simulate a nondeterministic machine. Basing his research strictly on English, Marcus sets forth some principles of processing that interact to offer explanations for some fundamental properties of language. He shows that language should have a certain design in order to be efficiently processed by the system he has constructed; specifically, rules should have certain locality properties, left-right asymmetries, and hierarchic structures that enter into rule types in special ways.Included in this volume are sections on the Determinism Hypothesis, Historical Perspective, the Grammar Interpreter, Structure of Grammar, Capturing Linguistic Generalizations, The Grammar Interpreter and Chomsky's Constraints, Parsing Relative Clauses, Parsing Noun Phrases, Differential Diagnosis and Garden Path Sentences, and the Necessity of Some Semantic/Syntactic Interactions.