NLP tasks differ in the semantic information they require, and at this time no single semantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not capture sentence structure in the same detail as logic-based ap...

We present clustAnalytics, an R package available now on CRAN, which provides methods to validate the results of clustering algorithms unweighted and weighted networks, particularly for cases where existence a community structure is unknown. clustAnalytics comprises set criteria assessing significance stability clustering. To evaluate clusters’ significance, scoring functions, systematically co...

The purpose of this study is to propose new similarity measures namely rough variational coefficient similarity measure under the rough neutrosophic environment. The weighted rough variational coefficient similarity measure has been also defined. The weighted rough variational coefficient similarity measures between the rough ideal alternative and each alternative are xxxxx calculated to find t...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.08.045 ⇑ Corresponding author. E-mail address: [email protected] (S.-J. L We present a novel method for detecting near-duplicates from a large collection of documents. Three major parts are involved in our method, feature selection, similarity measure, and discriminant derivation. To find near-dupli...

Weighted logic programming, a generalization of bottom-up logic programming, is a wellsuited framework for specifying dynamic programming algorithms. In this setting, proofs correspond to the algorithm’s output space, such as a path through a graph or a grammatical derivation, and are given a real-valued score (often interpreted as a probability) that depends on the real weights of the base axi...

We introduce a logical framework suitable to formalize structures of epistemic agents. Such a framework is based on the notion of weighted directed acyclic graphs (WDAGs) that allow one to assign a measure of strength to the knowledge relationships represented by the edges. We present the declarative and operational semantics of such a framework, and give results of correctness. We illustrate t...

We define a translation from Weighted CSP to signed Max-SAT, and a complete resolution-style calculus for solving signed Max-SAT. Based on these results, we then describe an original exact algorithm for solving Weighted CSP. Finally, we define several derived rules and prove that they enforce the main soft arc consistency defined in the literature when applied to Weighted CSP instances.

We present Pavelka-style fuzzy logic for reasoning about attribute implications, i.e. formulas A ⇒ B known also as association rules and functional dependencies. Fuzzy attribute implications allow for two different interpretations, namely, in data tables with graded (fuzzy) attributes and in data tables over domains with similarity relations. In the first interpretation, A ⇒ B reads “each objec...

Weighted automata are used to describe quantitative properties in various areas such as probabilistic systems, image compression, speech-to-text processing. The behaviour of such an automaton is a mapping, called a formal power series, assigning to each word a weight in some semiring. We generalize Büchi’s and Elgot’s fundamental theorems to this quantitative setting. We introduce a weighted ve...