An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities

We describe an extension of Earley's parser for stochastic context-free grammars that computes the following quantities given a stochastic context-free grammar and an input string: a) probabilities of successive prefixes being generated by the grammar; b) probabilities of substrings being generated by the nonterminals, including the entire string being generated by the grammar; c) most likely (Viterbi) parse of the string; d) posterior expected number of applications of each grammar production, as required for reestimating rule probabilities. (a) and (b) are computed incrementally in a single left-to-right pass over the input. Our algorithm compares favorably to standard bottom-up parsing methods for SCFGs in that it works efficiently on sparse grammars by making use of Earley's top-down control structure. It can process any context-free rule format without conversion to some normal form, and combines computations for (a) through (d) in a single algorithm. Finally, the algorithm has simple extensions for processing partially bracketed inputs, and for finding partial parses and their likelihoods on ungrammatical inputs.Comment: 45 pages. Slightly shortened version to appear in Computational Linguistics 2

On the Relation between Context-Free Grammars and Parsing Expression Grammars

Context-Free Grammars (CFGs) and Parsing Expression Grammars (PEGs) have several similarities and a few differences in both their syntax and semantics, but they are usually presented through formalisms that hinder a proper comparison. In this paper we present a new formalism for CFGs that highlights the similarities and differences between them. The new formalism borrows from PEGs the use of parsing expressions and the recognition-based semantics. We show how one way of removing non-determinism from this formalism yields a formalism with the semantics of PEGs. We also prove, based on these new formalisms, how LL(1) grammars define the same language whether interpreted as CFGs or as PEGs, and also show how strong-LL(k), right-linear, and LL-regular grammars have simple language-preserving translations from CFGs to PEGs

Pattern matching in compilers

In this thesis we develop tools for effective and flexible pattern matching. We introduce a new pattern matching system called amethyst. Amethyst is not only a generator of parsers of programming languages, but can also serve as an alternative to tools for matching regular expressions. Our framework also produces dynamic parsers. Its intended use is in the context of IDE (accurate syntax highlighting and error detection on the fly). Amethyst offers pattern matching of general data structures. This makes it a useful tool for implementing compiler optimizations such as constant folding, instruction scheduling, and dataflow analysis in general. The parsers produced are essentially top-down parsers. Linear time complexity is obtained by introducing the novel notion of structured grammars and regularized regular expressions. Amethyst uses techniques known from compiler optimizations to produce effective parsers.Comment: master thesi

Jparsec - a parser combinator for Javascript

Parser combinators have been a popular parsing approach in recent years. Compared with traditional parsers, a parser combinator has both readability and maintenance advantages. This project aims to construct a lightweight parser construct library for Javascript called Jparsec. Based on the modular nature of a parser combinator, the implementation uses higher-order functions. JavaScript provides a friendly and simple way to use higher-order functions, so the main construction method of this project will use JavaScript\u27s lambda functions. In practical applications, a parser combinator is mainly used as a tool, such as parsing JSON files. In order to verify the utility of parser combinators, this project uses a parser combinator to parse a partial Lua grammar. Lua is a widely used programming language, serving as a good test case for my parser combinator

LLLR Parsing: a Combination of LL and LR Parsing

A new parsing method called LLLR parsing is defined and a method for producing LLLR parsers is described. An LLLR parser uses an LL parser as its backbone and parses as much of its input string using LL parsing as possible. To resolve LL conflicts it triggers small embedded LR parsers. An embedded LR parser starts parsing the remaining input and once the LL conflict is resolved, the LR parser produces the left parse of the substring it has just parsed and passes the control back to the backbone LL parser. The LLLR(k) parser can be constructed for any LR(k) grammar. It produces the left parse of the input string without any backtracking and, if used for a syntax-directed translation, it evaluates semantic actions using the top-down strategy just like the canonical LL(k) parser. An LLLR(k) parser is appropriate for grammars where the LL(k) conflicting nonterminals either appear relatively close to the bottom of the derivation trees or produce short substrings. In such cases an LLLR parser can perform a significantly better error recovery than an LR parser since the most part of the input string is parsed with the backbone LL parser. LLLR parsing is similar to LL(^*) parsing except that it (a) uses LR(k) parsers instead of finite automata to resolve the LL(k) conflicts and (b) does not perform any backtracking

Syntactic analysis of LR(k) languages

PhD ThesisA method of syntactic analysis, termed LA(m)LR(k), is discussed theoretically. Knuth's LR(k) algorithm is included as the special case m = k. A simpler variant, SLA(m)LR(k) is also described, which in the case SLA(k)LR(O) is equivalent to the SLR(k) algorithm as defined by DeRemer. Both variants have the LR(k) property of immediate detection of syntactic errors. The case m = 1 k = 0 is examined in detail, when the methods provide a practical parsing technique of greater generality than precedence methods in current use. A formal comparison is made with the weak precedence algorithm. The implementation of an SLA(1)LR(O) parser (SLR) is described, involving numerous space and time optimisations. Of importance is a technique for bypassing unnecessary steps in a syntactic derivation. Direct comparisons are made, primarily with the simple precedence parser of the highly efficient Stanford AlgolW compiler, and confirm the practical feasibility of the SLR parser.The Science Research Council

Practical LR Parser Generation

Parsing is a fundamental building block in modern compilers, and for industrial programming languages, it is a surprisingly involved task. There are known approaches to generate parsers automatically, but the prevailing consensus is that automatic parser generation is not practical for real programming languages: LR/LALR parsers are considered to be far too restrictive in the grammars they support, and LR parsers are often considered too inefficient in practice. As a result, virtually all modern languages use recursive-descent parsers written by hand, a lengthy and error-prone process that dramatically increases the barrier to new programming language development. In this work we demonstrate that, contrary to the prevailing consensus, we can have the best of both worlds: for a very general, practical class of grammars -- a strict superset of Knuth's canonical LR -- we can generate parsers automatically, and the resulting parser code, as well as the generation procedure itself, is highly efficient. This advance relies on several new ideas, including novel automata optimization procedures; a new grammar transformation ("CPS"); per-symbol attributes; recursive-descent actions; and an extension of canonical LR parsing, which we refer to as XLR, which endows shift/reduce parsers with the power of bounded nondeterministic choice. With these ingredients, we can automatically generate efficient parsers for virtually all programming languages that are intuitively easy to parse -- a claim we support experimentally, by implementing the new algorithms in a new software tool called langcc, and running them on syntax specifications for Golang 1.17.8 and Python 3.9.12. The tool handles both languages automatically, and the generated code, when run on standard codebases, is 1.2x faster than the corresponding hand-written parser for Golang, and 4.3x faster than the CPython parser, respectively

Stream Processing using Grammars and Regular Expressions

In this dissertation we study regular expression based parsing and the use of grammatical specifications for the synthesis of fast, streaming string-processing programs. In the first part we develop two linear-time algorithms for regular expression based parsing with Perl-style greedy disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle and action machines, and a finite-state specialization of the streaming parsing algorithm presented in the first part. In the second part we also develop a new linear-time streaming parsing algorithm for parsing expression grammars (PEG) which generalizes the regular grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm reformulated using least fixed points and evaluated using an instance of the chaotic iteration scheme by Cousot and Cousot