5,906 research outputs found
Graph-Based Shape Analysis Beyond Context-Freeness
We develop a shape analysis for reasoning about relational properties of data
structures. Both the concrete and the abstract domain are represented by
hypergraphs. The analysis is parameterized by user-supplied indexed graph
grammars to guide concretization and abstraction. This novel extension of
context-free graph grammars is powerful enough to model complex data structures
such as balanced binary trees with parent pointers, while preserving most
desirable properties of context-free graph grammars. One strength of our
analysis is that no artifacts apart from grammars are required from the user;
it thus offers a high degree of automation. We implemented our analysis and
successfully applied it to various programs manipulating AVL trees,
(doubly-linked) lists, and combinations of both
CHR as grammar formalism. A first report
Grammars written as Constraint Handling Rules (CHR) can be executed as
efficient and robust bottom-up parsers that provide a straightforward,
non-backtracking treatment of ambiguity. Abduction with integrity constraints
as well as other dynamic hypothesis generation techniques fit naturally into
such grammars and are exemplified for anaphora resolution, coordination and
text interpretation.Comment: 12 pages. Presented at ERCIM Workshop on Constraints, Prague, Czech
Republic, June 18-20, 200
AutoBayes: A System for Generating Data Analysis Programs from Statistical Models
Data analysis is an important scientific task which is required whenever information needs to be extracted from raw data. Statistical approaches to data analysis, which use methods from probability theory and numerical analysis, are well-founded but difficult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe AutoBayes, a program synthesis system for the generation of data analysis programs from statistical models. A statistical model specifies the properties for each problem variable (i.e., observation or parameter) and its dependencies in the form of a probability distribution. It is a fully declarative problem description, similar in spirit to a set of differential equations. From such a model, AutoBayes generates optimized and fully commented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Code is produced by a schema-guided deductive synthesis process. A schema consists of a code template and applicability constraints which are checked against the model during synthesis using theorem proving technology. AutoBayes augments schema-guided synthesis by symbolic-algebraic computation and can thus derive closed-form solutions for many problems. It is well-suited for tasks like estimating best-fitting model parameters for the given data. Here, we describe AutoBayes's system architecture, in particular the schema-guided synthesis kernel. Its capabilities are illustrated by a number of advanced textbook examples and benchmarks
Hidden covariation detection produces faster, not slower, social judgments
In Lewicki’s (1986a) demonstration of Hidden Co-variation Detection (HCD), responses were slower to faces that corresponded with a co-variation encountered previously than to faces with novel co-variations. This slowing contrasts with the typical finding that priming leads to faster responding, and might suggest that HCD is a unique type of implicit process. We extended Lewicki’s (1986a) methodology and showed that participants exposed to nonsalient
co-variations between hair length and personality were subsequently faster to respond to faces with those co-variations than to faces without, despite lack of awareness of the critical co-variations. This result confirms that people can detect subtle relationships between features of stimuli and that, as with other types of implicit cognition, this detection facilitates responding.</p
An Abstract Machine for Unification Grammars
This work describes the design and implementation of an abstract machine,
Amalia, for the linguistic formalism ALE, which is based on typed feature
structures. This formalism is one of the most widely accepted in computational
linguistics and has been used for designing grammars in various linguistic
theories, most notably HPSG. Amalia is composed of data structures and a set of
instructions, augmented by a compiler from the grammatical formalism to the
abstract instructions, and a (portable) interpreter of the abstract
instructions. The effect of each instruction is defined using a low-level
language that can be executed on ordinary hardware.
The advantages of the abstract machine approach are twofold. From a
theoretical point of view, the abstract machine gives a well-defined
operational semantics to the grammatical formalism. This ensures that grammars
specified using our system are endowed with well defined meaning. It enables,
for example, to formally verify the correctness of a compiler for HPSG, given
an independent definition. From a practical point of view, Amalia is the first
system that employs a direct compilation scheme for unification grammars that
are based on typed feature structures. The use of amalia results in a much
improved performance over existing systems.
In order to test the machine on a realistic application, we have developed a
small-scale, HPSG-based grammar for a fragment of the Hebrew language, using
Amalia as the development platform. This is the first application of HPSG to a
Semitic language.Comment: Doctoral Thesis, 96 pages, many postscript figures, uses pstricks,
pst-node, psfig, fullname and a macros fil
TRX: A Formally Verified Parser Interpreter
Parsing is an important problem in computer science and yet surprisingly
little attention has been devoted to its formal verification. In this paper, we
present TRX: a parser interpreter formally developed in the proof assistant
Coq, capable of producing formally correct parsers. We are using parsing
expression grammars (PEGs), a formalism essentially representing recursive
descent parsing, which we consider an attractive alternative to context-free
grammars (CFGs). From this formalization we can extract a parser for an
arbitrary PEG grammar with the warranty of total correctness, i.e., the
resulting parser is terminating and correct with respect to its grammar and the
semantics of PEGs; both properties formally proven in Coq.Comment: 26 pages, LMC
Learning Grammars for Architecture-Specific Facade Parsing
International audienceParsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four diff erent datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images from Paris following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework
AMR Dependency Parsing with a Typed Semantic Algebra
We present a semantic parser for Abstract Meaning Representations which
learns to parse strings into tree representations of the compositional
structure of an AMR graph. This allows us to use standard neural techniques for
supertagging and dependency tree parsing, constrained by a linguistically
principled type system. We present two approximative decoding algorithms, which
achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see
https://acl2018.org/programme/papers/
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