A novel unsupervised method for new word extraction

A new Unsupervised Clustering-based Feature Extraction Method

In manipulating data such as in supervised or unsupervised learning, we need to extract new features from the original features for the purpose of reducing the dimension of feature space and achieving better performance. In this paper, we investigate a novel schema for unsupervised feature extraction for classification problems. We based our method on clustering to achieve feature extraction. A...

Effective Unsupervised Arabic Word Stemming: Towards an Unsupervised Radicals Extraction

Audio keyword extraction by unsupervised word discovery

In real audio data, frequently occurring patterns often convey relevant information on the overall content of the data. The possibility to extract meaningful portions of the main content by identifying such key patterns, can be exploited for providing audio summaries and speeding up the access to relevant parts of the data. We refer to these patterns as audio motifs in analogy with the nomencla...

A New Unsupervised Approach to Word Segmentation

This article proposes ESA, a new unsupervised approach to word segmentation. ESA is an iterative process consisting of three phases: Evaluation, Selection, and Adjustment. In Evaluation, both the certainty and uncertainty of character sequence co-occurrence in corpora are considered as statistical evidence supporting goodness measurement. Additionally, the statistical data of character sequence...

a novel method for fast extraction of auditory evoked potentials

we compare performance of adaptive schemes which are based on radial-basis functions and kalman filters for fast extraction of auditory evoked potentials. moreover, we propose a new method based on evoked potential modeling in the kalman filter framework, which can improve the accuracy compared to the existing methods. simulation results show that adaptive schemes and the kalman method are not ...