930 research outputs found
A Practical Type Analysis for Verification of Modular Prolog Programs
Regular types are a powerful tool for computing very precise descriptive types for logic programs. However, in the context of real life, modular Prolog programs, the accurate results obtained by regular types often come at the price of efficiency. In this paper we propose a combination of techniques aimed at improving analysis efficiency in this context. As a first technique we allow optionally reducing the accuracy of inferred types by using only the types defined by the user or present in the libraries. We claim that, for the purpose of verifying type signatures given in the form of assertions the precision obtained using this approach is sufficient, and show that analysis times can be reduced significantly. Our second technique is aimed at dealing with situations where we would like to limit the amount of reanalysis performed, especially for library modules. Borrowing some ideas from polymorphic type systems, we show how to solve the problem by admitting parameters in type specifications. This allows us to compose new call patterns with some pre computed analysis info without losing any information. We argue that together these two techniques contribute to the practical and scalable analysis and verification of types in Prolog programs
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing for a high-coverage syntactic and
semantic text analysis combined with logical inference. For the syntactic and
semantic analysis we combine a deep semantic analysis with a shallow one
supported by statistical models in order to increase the quality and the
accuracy of results. For RTE we use logical inference of first-order employing
model-theoretic techniques and automated reasoning tools. The inference is
supported with problem-relevant background knowledge extracted automatically
and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or
other, more experimental sources with, e.g., manually defined presupposition
resolutions, or with axiomatized general and common sense knowledge. The
results show that fine-grained and consistent knowledge coming from diverse
sources is a necessary condition determining the correctness and traceability
of results.Comment: 25 pages, 10 figure
Trustworthy Formal Natural Language Specifications
Interactive proof assistants are computer programs carefully constructed to
check a human-designed proof of a mathematical claim with high confidence in
the implementation. However, this only validates truth of a formal claim, which
may have been mistranslated from a claim made in natural language. This is
especially problematic when using proof assistants to formally verify the
correctness of software with respect to a natural language specification. The
translation from informal to formal remains a challenging, time-consuming
process that is difficult to audit for correctness.
This paper shows that it is possible to build support for specifications
written in expressive subsets of natural language, within existing proof
assistants, consistent with the principles used to establish trust and
auditability in proof assistants themselves. We implement a means to provide
specifications in a modularly extensible formal subset of English, and have
them automatically translated into formal claims, entirely within the Lean
proof assistant. Our approach is extensible (placing no permanent restrictions
on grammatical structure), modular (allowing information about new words to be
distributed alongside libraries), and produces proof certificates explaining
how each word was interpreted and how the sentence's structure was used to
compute the meaning.
We apply our prototype to the translation of various English descriptions of
formal specifications from a popular textbook into Lean formalizations; all can
be translated correctly with a modest lexicon with only minor modifications
related to lexicon size.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0781
Wide-coverage deep statistical parsing using automatic dependency structure annotation
A number of researchers (Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004;Miyao and Tsujii 2004) have convincingly argued for the use of dependency (rather than CFG-tree) representations
for parser evaluation. Preiss (2003) and Kaplan et al. (2004) conducted a number of experiments comparing ādeepā hand-crafted wide-coverage with āshallowā treebank- and machine-learning based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit the experiments
in Preiss (2003) and Kaplan et al. (2004), this time using the sophisticated automatic LFG f-structure annotation methodologies of Cahill et al. (2002b, 2004) and Burke (2006), with surprising results. We compare various PCFG and history-based parsers (based on Collins, 1999; Charniak, 2000; Bikel, 2002) to find a baseline parsing system that fits best into our automatic dependency structure annotation technique. This combined system of syntactic parser and dependency structure annotation is compared to two hand-crafted, deep constraint-based parsers (Carroll and Briscoe 2002; Riezler et al. 2002). We evaluate using dependency-based gold standards (DCU 105, PARC 700, CBS 500 and dependencies for WSJ Section 22) and use the Approximate Randomization Test (Noreen 1989) to test the statistical significance of the results. Our experiments show that machine-learning-based shallow grammars augmented with sophisticated automatic dependency annotation technology outperform hand-crafted, deep, widecoverage constraint grammars. Currently our best system achieves an f-score of 82.73% against the PARC 700 Dependency Bank (King et al. 2003), a statistically significant improvement of 2.18%over the most recent results of 80.55%for the hand-crafted LFG grammar and XLE parsing system of Riezler et al. (2002), and an f-score of 80.23% against the CBS 500 Dependency Bank (Carroll, Briscoe, and Sanfilippo 1998), a statistically significant 3.66% improvement over the 76.57% achieved by the hand-crafted RASP grammar and parsing system of Carroll and
Briscoe (2002)
Designing and Implementing Embodied Agents: Learning from Experience
In this paper, we provide an overview of part of our experience in designing and implementing some of the embodied agents and talking faces that we have used for our research into human computer interaction. We focus on the techniques that were used and evaluate this with respect to the purpose that the agents and faces were to serve and the costs involved in producing and maintaining the software. We discuss the function of this research and development in relation to the educational programme of our graduate students
- ā¦