Text classification remains one of the major fields of research in natural language processing. This paper evaluates the use of the computational tool Coh-Metrix as a means to dis...
Scott A. Crossley, Philip M. McCarthy, Danielle S....
Constraint Satisfaction Problems are ubiquitous in Artificial Intelligence. Over the past decade significant advances have been made in terms of the size of problem instance tha...
Margarita Razgon, Barry O'Sullivan, Gregory M. Pro...
In this paper, we present a machine learning based approach for estimating antecedents of anaphorically used personal pronouns in Turkish sentences using a decision tree classific...
1 This paper describes two major student projects for the artificial intelligence course – Mapping using Bayesian filter and Monte Carlo Localization. These projects are also sui...
Myles F. McNally, Frank Klassner, Christopher Cont...
A low-effort data mining approach to labeling network event records in a WLAN is proposed. The problem being addressed is often observed in an AI and data mining strategy to netwo...
Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliy...
Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily sl...
Matthew E. Taylor, Cynthia Matuszek, Pace Reagan S...
Temporal Networks play an important role in solving planning problems and they are also used, though not as frequently, when solving scheduling problems. In this paper we propose ...
Researchers have reported successful deployments of diagnosis decision support systems based on Bayesian networks. However, the methodology for evaluating the diagnosability for s...
Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for le...