Semantic similarity measure between concepts is a generic issue for many applications of computational linguistics and artificial intelligence. Information Content (IC) of concept is an important dimension of accessing semantic similarity between two concepts. Recently it has attracted great concern and become a hot topic. The paper gives a general overview of usage of information content in se...

We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize ...

We introduce EXTASEM!, a novel approach for the automatic learning of lexical taxonomies from domain terminologies. First, we exploit a very large semantic network to collect thousands of in-domain textual definitions. Second, we extract (hyponym, hypernym) pairs from each definition with a CRF-based algorithm trained on manually-validated data. Finally, we introduce a graph induction procedure...

This paper investigates the task of noun compound interpretation, building on the sense collocation approach proposed by Moldovan et al. (2004). Our primary task is to evaluate the impact of similar words on the sense collocation method, and decrease the sensitivity of the classifiers by expanding the range of sense collocations via different semantic relations. Our method combines hypernyms, h...

In this paper we illustrate an approach for clustering semantically heterogeneous XML Schemas. The proposed approach is driven by the semantics of the involved Schemas that is defined by means of the interschema properties existing among concepts represented therein; interschema properties taken into account by our approach are synonymies (indicating that two concepts have the same meaning), hy...

In this paper we address the problem of automatically constructing structured knowledge from plain texts. In particular, we present a supervised learning technique to first identify definitions in text data, while then finding hypernym relations within them making use of extracted syntactic structures. Instead of using pattern matching methods that rely on lexico-syntactic patterns, we propose ...

‎let $x$ be a real normed  space, then  $c(subseteq x)$  is  functionally  convex  (briefly, $f$-convex), if  $t(c)subseteq bbb r $ is  convex for all bounded linear transformations $tin b(x,r)$; and $k(subseteq x)$  is  functionally   closed (briefly, $f$-closed), if  $t(k)subseteq bbb r $ is  closed  for all bounded linear transformations $tin b(x,r)$. we improve the    krein-milman theorem  ...

in this article, we have shown, for the add-point monad t, thepartial morphism category set*is isomorphic to the kleisli category sett. alsowe have proved that the category, sett, of t-algebras is isomorphic to thecategory set of pointed sets. finally we have established commutative squaresinvolving these categories.