225,943 research outputs found
A Flexible Shallow Approach to Text Generation
In order to support the efficient development of NL generation systems, two
orthogonal methods are currently pursued with emphasis: (1) reusable, general,
and linguistically motivated surface realization components, and (2) simple,
task-oriented template-based techniques. In this paper we argue that, from an
application-oriented perspective, the benefits of both are still limited. In
order to improve this situation, we suggest and evaluate shallow generation
methods associated with increased flexibility. We advise a close connection
between domain-motivated and linguistic ontologies that supports the quick
adaptation to new tasks and domains, rather than the reuse of general
resources. Our method is especially designed for generating reports with
limited linguistic variations.Comment: LaTeX, 10 page
Designing Software Architectures As a Composition of Specializations of Knowledge Domains
This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: âA software architecture should be a composition of specializations of knowledge domainsâ. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience
Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules
We describe a case study in the application of {\em symbolic machine
learning} techniques for the discovery of linguistic rules and categories. A
supervised rule induction algorithm is used to learn to predict the correct
diminutive suffix given the phonological representation of Dutch nouns. The
system produces rules which are comparable to rules proposed by linguists.
Furthermore, in the process of learning this morphological task, the phonemes
used are grouped into phonologically relevant categories. We discuss the
relevance of our method for linguistics and language technology
Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation
We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The LevenbergâMarquardt (LâM) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The LâM method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e InnovaciĂłn TEC2008-04920Junta de AndalucĂa P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626
A methodology for the selection of new technologies in the aviation industry
The purpose of this report is to present a technology selection methodology to
quantify both tangible and intangible benefits of certain technology
alternatives within a fuzzy environment. Specifically, it describes an
application of the theory of fuzzy sets to hierarchical structural analysis and
economic evaluations for utilisation in the industry. The report proposes a
complete methodology to accurately select new technologies. A computer based
prototype model has been developed to handle the more complex fuzzy
calculations. Decision-makers are only required to express their opinions on
comparative importance of various factors in linguistic terms rather than exact
numerical values. These linguistic variable scales, such as âvery highâ, âhighâ,
âmediumâ, âlowâ and âvery lowâ, are then converted into fuzzy numbers, since it
becomes more meaningful to quantify a subjective measurement into a range rather
than in an exact value. By aggregating the hierarchy, the preferential weight of
each alternative technology is found, which is called fuzzy appropriate index.
The fuzzy appropriate indices of different technologies are then ranked and
preferential ranking orders of technologies are found. From the economic
evaluation perspective, a fuzzy cash flow analysis is employed. This deals
quantitatively with imprecision or uncertainties, as the cash flows are modelled
as triangular fuzzy numbers which represent âthe most likely possible valueâ,
âthe most pessimistic valueâ and âthe most optimistic valueâ. By using this
methodology, the ambiguities involved in the assessment data can be effectively
represented and processed to assure a more convincing and effective decision-
making process when selecting new technologies in which to invest. The prototype
model was validated with a case study within the aviation industry that ensured
it was properly configured to meet the
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