Traditionally, Controlled Natural Languages (CNLs) are designed either to avoid ambiguity for human readers, or to facilitate automatic semantic analysis, so that texts can be transcoded to a knowledge representation language. CNLs of the second kind have recently been adapted to the requirements of knowledge formation in OWL for the Semantic Web. We suggest in this paper a variant approach bas...

Traditionally, Controlled Natural Languages (CNLs) are designed either to avoid ambiguity for human readers, or to facilitate automatic semantic analysis, so that texts can be transcoded to a knowledge representation language. CNLs of the second kind have recently been adapted to the requirements of knowledge formation in OWL for the Semantic Web. We suggest in this paper a variant approach bas...

Short texts usually encounter data sparsity and ambiguity problems in representations for their lack of context. In this paper, we propose a novel method to model short texts based on semantic clustering and convolutional neural network. Particularly, we first discover semantic cliques in embedding spaces by a fast clustering algorithm. Then, multi-scale semantic units are detected under the su...

The huge volumes of unstructured texts available online drives the increasing need for automated techniques to analyze and extract knowledge from these repositories of information. Resolving the ambiguity in these texts is an important step for any following analysis tasks. In this paper, we present a new method for one type of ambiguity resolving -term disambiguation. The method is based on ma...

In an age of widespread background checks, we ask how managers in different organizational contexts navigate legal ambiguity in assessing applicants’ criminal history information, based on interview data obtained in a recent field experiment. The study builds on institutional analyses of the social sources of workplace legality to describe how employers consider applicants with criminal histori...