11,301 research outputs found
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however
commonly used Markov models are unable to adequately describe these phenomena
due to their strong locality assumptions. We propose a novel hierarchical model
for structured prediction over sequences and trees which exploits global
context by conditioning each generation decision on an unbounded context of
prior decisions. This builds on the success of Markov models but without
imposing a fixed bound in order to better represent global phenomena. To
facilitate learning of this large and unbounded model, we use a hierarchical
Pitman-Yor process prior which provides a recursive form of smoothing. We
propose prediction algorithms based on A* and Markov Chain Monte Carlo
sampling. Empirical results demonstrate the potential of our model compared to
baseline finite-context Markov models on part-of-speech tagging and syntactic
parsing
Programming Not Only by Example
In recent years, there has been tremendous progress in automated synthesis
techniques that are able to automatically generate code based on some intent
expressed by the programmer. A major challenge for the adoption of synthesis
remains in having the programmer communicate their intent. When the expressed
intent is coarse-grained (for example, restriction on the expected type of an
expression), the synthesizer often produces a long list of results for the
programmer to choose from, shifting the heavy-lifting to the user. An
alternative approach, successfully used in end-user synthesis is programming by
example (PBE), where the user leverages examples to interactively and
iteratively refine the intent. However, using only examples is not expressive
enough for programmers, who can observe the generated program and refine the
intent by directly relating to parts of the generated program.
We present a novel approach to interacting with a synthesizer using a
granular interaction model. Our approach employs a rich interaction model where
(i) the synthesizer decorates a candidate program with debug information that
assists in understanding the program and identifying good or bad parts, and
(ii) the user is allowed to provide feedback not only on the expected output of
a program, but also on the underlying program itself. That is, when the user
identifies a program as (partially) correct or incorrect, they can also
explicitly indicate the good or bad parts, to allow the synthesizer to accept
or discard parts of the program instead of discarding the program as a whole.
We show the value of our approach in a controlled user study. Our study shows
that participants have strong preference to using granular feedback instead of
examples, and are able to provide granular feedback much faster
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
A Hybrid Analysis for Security Protocols with State
Cryptographic protocols rely on message-passing to coordinate activity among
principals. Each principal maintains local state in individual local sessions
only as needed to complete that session. However, in some protocols a principal
also uses state to coordinate its different local sessions. Sometimes the
non-local, mutable state is used as a means, for example with smart cards or
Trusted Platform Modules. Sometimes it is the purpose of running the protocol,
for example in commercial transactions.
Many richly developed tools and techniques, based on well-understood
foundations, are available for design and analysis of pure message-passing
protocols. But the presence of cross-session state poses difficulties for these
techniques.
In this paper we provide a framework for modeling stateful protocols. We
define a hybrid analysis method. It leverages theorem-proving---in this
instance, the PVS prover---for reasoning about computations over state. It
combines that with an "enrich-by-need" approach---embodied by CPSA---that
focuses on the message-passing part. As a case study we give a full analysis of
the Envelope Protocol, due to Mark Ryan
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