Recognizing polarity requires a list of polar words and phrases. For the purpose of building such lexicon automatically, a lot of studies have investigated (semi-) unsupervised method of learning polarity of words and phrases. In this paper, we explore to use structural clues that can extract polar sentences from Japanese HTML documents, and build lexicon from the extracted polar sentences. The...

DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Currently most research focuses on the interpretation of the meaning of the data. However, majority methods are supervised-based, less attention has been paid on unsupervised approaches which is important when domain knowledge is incomplete or hard to obtain. In this paper, we present a new framework f...

The analysis of gene expression data involves the observation of a very large number of variables (genes) on a few units (tissues). In such a context the recourse to conventional classification methods may be hard both for analytical and interpretative reasons. In this work a gene selection procedure for classification problems is addressed. The dimensionality reduction is based on the projecti...

Adaptation is a ubiquitous neural and psychological phenomenon, with a wealth of instantiations and implications. Although a basic form of plasticity, it has, bar some notable exceptions, attracted computational theory of only one main variety. In this paper, we study adaptation from the perspective of factor analysis, a paradigmatic technique of unsupervised learning. We use factor analysis to...

We discuss the detection of two Gaussian clusters given a cloud of points. The optimal learning curve for this unsupervised learning scenario is determined with a replica calculation. A comparison with principal component analysis and supervised learning allows to understand the three diierent learning phases observed.

A method for multidimensional hierarchical clustering that is invariant to monotonic transformations of the distance metric is presented. The method derives a tree of clusters organized according to the homogeneity of intracluster and interpoint distances. Higher levels correspond to coarser clusters. At any level the method can detect clusters of different densities, shapes and sizes. The numb...

Neural conversational models require substantial amounts of dialogue data to estimate their parameters and are therefore usually learned on large corpora such as chat forums, Twitter discussions or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue i...

While unsupervised learning methods are usually proposed to handle document clustering, in the literature, there exist practices that apply these methods to document classification as well. This paper analyzes the feasibility and the limitation of such practice, and studies its efficacy through a preliminary case study on the Reuters-21578 document collection.