3,592 research outputs found
Efficient Analysis of Complex Diagrams using Constraint-Based Parsing
This paper describes substantial advances in the analysis (parsing) of
diagrams using constraint grammars. The addition of set types to the grammar
and spatial indexing of the data make it possible to efficiently parse real
diagrams of substantial complexity. The system is probably the first to
demonstrate efficient diagram parsing using grammars that easily be retargeted
to other domains. The work assumes that the diagrams are available as a flat
collection of graphics primitives: lines, polygons, circles, Bezier curves and
text. This is appropriate for future electronic documents or for vectorized
diagrams converted from scanned images. The classes of diagrams that we have
analyzed include x,y data graphs and genetic diagrams drawn from the biological
literature, as well as finite state automata diagrams (states and arcs). As an
example, parsing a four-part data graph composed of 133 primitives required 35
sec using Macintosh Common Lisp on a Macintosh Quadra 700.Comment: 9 pages, Postscript, no fonts, compressed, uuencoded. Composed in
MSWord 5.1a for the Mac. To appear in ICDAR '95. Other versions at
ftp://ftp.ccs.neu.edu/pub/people/futrell
Coding-theorem Like Behaviour and Emergence of the Universal Distribution from Resource-bounded Algorithmic Probability
Previously referred to as `miraculous' in the scientific literature because
of its powerful properties and its wide application as optimal solution to the
problem of induction/inference, (approximations to) Algorithmic Probability
(AP) and the associated Universal Distribution are (or should be) of the
greatest importance in science. Here we investigate the emergence, the rates of
emergence and convergence, and the Coding-theorem like behaviour of AP in
Turing-subuniversal models of computation. We investigate empirical
distributions of computing models in the Chomsky hierarchy. We introduce
measures of algorithmic probability and algorithmic complexity based upon
resource-bounded computation, in contrast to previously thoroughly investigated
distributions produced from the output distribution of Turing machines. This
approach allows for numerical approximations to algorithmic
(Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a
computational hierarchy. We demonstrate that all these estimations are
correlated in rank and that they converge both in rank and values as a function
of computational power, despite fundamental differences between computational
models. In the context of natural processes that operate below the Turing
universal level because of finite resources and physical degradation, the
investigation of natural biases stemming from algorithmic rules may shed light
on the distribution of outcomes. We show that up to 60\% of the
simplicity/complexity bias in distributions produced even by the weakest of the
computational models can be accounted for by Algorithmic Probability in its
approximation to the Universal Distribution.Comment: 27 pages main text, 39 pages including supplement. Online complexity
calculator: http://complexitycalculator.com
Parsing for agile modeling
Agile modeling refers to a set of methods that allow for a quick initial development of an importer and its further refinement. These requirements are not met simultaneously by the current parsing technology. Problems with parsing became a bottleneck in our research of agile modeling.
In this thesis we introduce a novel approach to specify and build parsers. Our approach allows for expressive, tolerant and composable parsers without sacrificing performance. The approach is based on a context-sensitive extension of parsing expression grammars that allows a grammar engineer to specify complex language restrictions. To insure high parsing performance we automatically analyze a grammar definition and choose different parsing strategies for different parts of the grammar.
We show that context-sensitive parsing expression grammars allow for highly composable, tolerant and variable-grained parsers that can be easily refined. Different parsing strategies significantly insure high-performance of parsers without sacrificing expressiveness of the underlying grammars
Semantic Types, Lexical Sorts and Classifiers
We propose a cognitively and linguistically motivated set of sorts for
lexical semantics in a compositional setting: the classifiers in languages that
do have such pronouns. These sorts are needed to include lexical considerations
in a semantical analyser such as Boxer or Grail. Indeed, all proposed lexical
extensions of usual Montague semantics to model restriction of selection,
felicitous and infelicitous copredication require a rich and refined type
system whose base types are the lexical sorts, the basis of the many-sorted
logic in which semantical representations of sentences are stated. However,
none of those approaches define precisely the actual base types or sorts to be
used in the lexicon. In this article, we shall discuss some of the options
commonly adopted by researchers in formal lexical semantics, and defend the
view that classifiers in the languages which have such pronouns are an
appealing solution, both linguistically and cognitively motivated
Attribute grammar evolution
The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_19Proceedings of First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, June 15-18, 2005This paper describes Attribute Grammar Evolution (AGE), a new Automatic Evolutionary Programming algorithm that extends standard Grammar Evolution (GE) by replacing context-free grammars by attribute grammars. GE only takes into account syntactic restrictions to generate valid individuals. AGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. Attribute grammars make it possible to semantically describe the solution. The paper shows empirically that AGE is as good as GE for a classical problem, and proves that including semantics in the grammar can improve GE performance. An important conclusion is that adding too much semantics
can make the search difficult
The Grail theorem prover: Type theory for syntax and semantics
As the name suggests, type-logical grammars are a grammar formalism based on
logic and type theory. From the prespective of grammar design, type-logical
grammars develop the syntactic and semantic aspects of linguistic phenomena
hand-in-hand, letting the desired semantics of an expression inform the
syntactic type and vice versa. Prototypical examples of the successful
application of type-logical grammars to the syntax-semantics interface include
coordination, quantifier scope and extraction.This chapter describes the Grail
theorem prover, a series of tools for designing and testing grammars in various
modern type-logical grammars which functions as a tool . All tools described in
this chapter are freely available
Selected and extended papers of the Brazilian Symposium on Programming Languages 2012
The Brazilian Symposium on Programming Languages (SBLP) is an annual conference romoted by the Brazilian Computer Society (SBC), which has become an important forum for researchers and practitioners in programming language design and implementation from around the world to interact and present their results and experience. This issue of Science of Computer Programming includes extended versions of selected papers from the XVI SBLP, held in Natal, Brazil on September 23–28, 2012
Analysing symbolic music with probabilistic grammars
Recent developments in computational linguistics offer ways to approach the analysis of musical structure by inducing probabilistic models (in the form of grammars) over a corpus of music. These can produce idiomatic sentences from a probabilistic model of the musical language and thus offer explanations of the musical structures they model. This chapter surveys historical and current work in musical analysis using grammars, based on computational linguistic approaches. We outline the theory of probabilistic grammars and illustrate their implementation in Prolog using PRISM. Our experiments on learning the probabilities for simple grammars from pitch sequences in two kinds of symbolic musical corpora are summarized. The results support our claim that probabilistic grammars are a promising framework for computational music analysis, but also indicate that further work is required to establish their superiority over Markov models
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