A practical arbitrary look-ahead LR parsing technique

We present a practical technique that provides an LR (0) parser with either fixed or arbitrary look-ahead. The construction algorithm is based on certain paths in the LR (0) state diagram, which must be restricted to a maximum length m . The technique determines the amount of look-ahead required, and the user is spared the task of guessing it. Instead, the user provides m . In situations where single symbol look-ahead is sufficient, the corresponding grammar class (called LAR ( m )) is identical to the NQLALR (1) class. For practical grammars that require arbitrary look-ahead, the storage requirements typically do not exceed an amount linear in the size of the corresponding LR (0) parser. The technique is shown to work for a practical programming language grammar, and has been used to solve particular cases of the PL/1 keyword problem.

A unifying model for lookahead LR parsing

A compiler generator called Metauncle is introduced. Metauncle produces one-pass compilers in which all attributes are evaluated in conjunction with LR parsing. The description of a language is given to Metauncle as an L-attributed grammar, and the system transforms it before generation of an evaluator to another attribute grammar satisfying the requirements for evaluation. The transformed grammar belongs to the class of so-called uncle-attributed grammars. Besides general information about the system, the definition of uncle-attributed grammars, the idea of the grammar transformation and the default rules of the specification language are presented.

There are two major classes of natural language grammars --- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.

Practical arbitrary lookahead LR parsing

Several computational problems about grammars are studied. Efficient algorithms are presented for the problems of (1) determining, for a given semantic grammar, if there exists a related parsing grammar in some specified grammar class, and (2) finding such a related parsing grammar when one exists. The two grammars are to be related by mergers of nonterminals and/or terminals. Efficient algorithms are presented for most of the grammar classes used in compilers. We also study the problem of (3) determining which terminals of a grammar are good candidates for merger into common lexical tokens of the corresponding parsing grammar.

A wide variety of algorithms have been suggested for the repair of syntactic errors in a computer program. Since there is usually more than one possible repair for any syntax error, many algorithms employ a cost function to guide the the repair, and some [1,3,4,6], guarantee that the repair chosen will be least-cost, according to some definition. (The others, although guided by costs, do not guarantee least-cost in all cases.) Fischer et al. [4,6,7] define a “locally least-cost” repair using insertions and deletions, and provide algorithms for LL and LR parsers. A locally least-cost repair is a least-cost sequence of deletions and insertions such that one more symbol in the original string will be accepted by the parser. Backhouse [2,3] uses a similar definition. In both cases, the repair algorithms operate by examining a single symbol in the input at any time.

Unlike other mildly context-sensitive formalisms, Combinatory Categorial Grammar (CCG) cannot be parsed in polynomial time when the size of the grammar is taken into account. Refining this result, we show that the parsing complexity of CCG is exponential only in the maximum degree of composition. When that degree is fixed, parsing can be carried out in polynomial time. Our finding is interesting from a linguistic perspective because a bounded degree of composition has been suggested as a universal constraint on natural language grammar. Moreover, ours is the first complexity result for a version of CCG that includes substitution rules, which are used in practical grammars but have been ignored in theoretical work.

We describe work toward the construction of a very wide-coverage probabilistic parsing system for natural language (NL), based on LR parsing techniques. The system is intended to rank the large num...

Adding a true 3-D display to raster graphics system: Fuchs, H, Pizer, S M, Tsai, L-C, Bloomberg, S H and Heinz, E R IEEE C G and A (September 1982) pp 73–78

In recent years, a theory of distributional learning of phrase structure grammars has been developed starting with the simple algorithm presented in (Clark and Eyraud, 2007). These ideas are based on the classic ideas of American structuralist linguistics (Wells, 1947; Harris, 1954). Since that initial paper, the algorithms have been extended to large classes of grammars, notably to the class of Multiple Context-Free grammars by (Yoshinaka, 2011). In this talk we will sketch a theory of language acquisition based on these techniques, and contrast it with other proposals, such as the semantic bootstrapping and parameter setting models. This proposal is based on three recent results: first, a weak learning result for a class of languages that plausibly includes all natural languages (Clark and Yoshinaka, 2013), secondly, a strong learning result for some context-free grammars, that includes a general strategy for converting weak learners to strong learners (Clark, 2013a), and finally a theoretical result that all minimal grammars for a language will have distributionally definable syntactic categories (Clark, 2013b). We argue that we now have all of the pieces for a complete and explanatory theory of language acquisition based on distributional learning and sketch some of the nontrivial predictions of this theory about the syntax and syntax-semantics interface.

Parsing Expression Grammar (PEG) and Packrat Parser are the two recent developments in the field of Formal Languages and Automata Theory. The syntax of PEG is similar to the syntax of Context free grammar with ordered choice. It is based on backtracking recursive-descent parser. Packrat parsing technique is used for handling backtracking in recursive decent parser. Backtracking operation in parsing Expression Grammar ensure in polynomial time at huge memory cost. Recursive Descent parsers (RD) are top-down parsing technique in which derivation of string follows the structure of the grammar. It is very easy to write and debug. Recursive Descent parsers admit very limited class of grammar. It is extended by using recursive decent parser with backtracking which takes explosive runtimes. It cannot used for the grammar with left recursion. It also cannot deal with ambiguous grammar. We develop stack based parsing technique which is recursive descent-like. It takes cubic time in worst case. It may be parse even for left recursion grammar.

In this paper we propose and evaluate a method for locating causes of ambiguity in context-free grammars by automatic analysis of parse forests. A parse forest is the set of parse trees of an ambiguous sentence. Deducing causes of ambiguity from observing parse forests is hard for grammar engineers because of (a) the size of the parse forests, (b) the complex shape of parse forests, and (c) the diversity of causes of ambiguity. We first analyze the diversity of ambiguities in grammars for programming languages and the diversity of solutions to these ambiguities. Then we introduce Dr. Ambiguity: a parse forest diagnostics tools that explains the causes of ambiguity by analyzing differences between parse trees and proposes solutions. We demonstrate its effectiveness using a small experiment with a grammar for Java 5.

The syntactic rules for many programming languages have been expressed by formal grammars, generally variants of phrase-structure grammars. The syntactic analysis essential to translation of programming languages can be done entirely mechanically for such languages. Major problems remain in rendering analyzers efficient in use of space and time and in finding fully satisfactory formal grammars for present and future programming languages.

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