We conceptualize objects based on sensorimotor information gleaned from real-world experience. To what extent is conceptual information structured according to higher-level linguistic features? We investigate whether classifiers, a grammatical category, shape the conceptual representations of objects. In three experiments native Mandarin speakers (a classifier language) and native Dutch speaker...

This paper presents a new similarity measure to be used for general tasks including supervised learning, which is represented by the K-nearest neighbor classifier (KNN). The proposed similarity measure is invariant to large differences in some dimensions in the feature space. The proposed metric is proved mathematically to be a metric. To test its viability for different applications, the KNN u...

This paper examines a classifier based on similarity measures originating from probabilistic equivalence relations with a generalized mean. Equivalences are weighted and weight optimization is carried out with differential evolution algorithms. In the classifier, a similarity measure based on the Łukasiewicz structure has previously been used, but this paper concentrates on measures which can b...

Paraphrase recognition is the task of identifying whether two pieces of natural language represent similar meanings. This paper describes a system participating in the shared task 1 of SemEval 2015, which is about paraphrase detection and semantic similarity in twitter. Our approach is to exploit semantically meaningful features to detect paraphrases. An existing state-of-the-art model for pred...

Positive and unlabelled learning (PU learning) has been investigated to deal with the situation where only the positive examples and the unlabelled examples are available. Most of the previous works focus on identifying some negative examples from the unlabelled data, so that the supervised learning methods can be applied to build a classifier. However, for the remaining unlabelled data, which ...

An investigation has been conducted on two well known similarity-based learning approaches to text categorization. This includes the k-nearest neighbor (kNN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, we propose a new classifier called the kNN model-based classifier by unifying the strengths of k-NN and Rocchio classifier and adapting t...

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested example and the training examples. This raises a major question about which distance measures to be used for the KNN clas...

This paper describes the system of the team SKL in the NTCIR-11 RITE-VAL workshop. The system consists of two modules: RTE module and text-search module. The RTE module, which is a modified version of our previous system for the binary classification in the RITE-2 workshop, takes two-step classification strategy. The first step classifies a given text pair into positive or negative entailment c...

In this study, we propose a granular classifier (GC) with the aid of a context-based similarity clustering (CSC) method and applied it for network intrusion detection. The proposed CSC supporting the design of information granules is exploited here to determine the so-called contexts. Unlike the conventional similar clustering method, here the CSC built clusters by taking into consideration of ...