Cross validation of neural network applications for automatic new topic identification

Neural network applications for automatic new topic identification

Using Encyclopedic Knowledge for Automatic Topic Identification

This paper presents a method for automatic topic identification using an encyclopedic graph derived from Wikipedia. The system is found to exceed the performance of previously proposed machine learning algorithms for topic identification, with an annotation consistency comparable to human annotations.

Neural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images

Background: Multiple Sclerosis (MS) is a degenerative disease of central nervous system. MS patients have some dead tissues in their brains called MS lesions. MRI is an imaging technique sensitive to soft tissues such as brain that shows MS lesions as hyper-intense or hypo-intense signals. Since manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation ...

An Automatic Procedure for Topic-Focus Identification

The dichotomy of topic and focus, based, in the Praguean Functional Generative Description, on the scale of communicative dynamism, is relevant not only for a possible placement of the sentence in a context, but also for its semantic interpretation. An automatic identification of topic and focus may use the input information on word order, on the systemic ordering of kinds of complementations (...

Knowledge-Based Automatic Topic Identification

As the first step in an automated text summarization algorithm, this work presents a new method for automatically identifying the central ideas in a text based on a knowledge-based concept counting paradigm. To represent and generalize concepts, we use the hierarchical concept taxonomy WordNet. By setting appropriate cutoff values for such parameters as concept generality and child-to-parent fr...