Knowledge Based Neural Network for Text Classification

2007 ◽  
Author(s):  
Ram Dayal Goyal
Keyword(s):  
Neural Network ◽  
Text Classification ◽  
Knowledge Based

scholarly journals Background Knowledge Based Multi-Stream Neural Network for Text Classification

2018 ◽  
Vol 8 (12) ◽  
pp. 2472 ◽  
Author(s):  
Fuji Ren ◽  
Jiawen Deng
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Background Knowledge ◽  
Sequence Information ◽  
Stream Network ◽  
Original Sequence ◽  
Training Corpus ◽  
Knowledge Based ◽  
Classification Tasks

As a foundation and typical task in natural language processing, text classification has been widely applied in many fields. However, as the basis of text classification, most existing corpus are imbalanced and often result in the classifier tending its performance to those categories with more texts. In this paper, we propose a background knowledge based multi-stream neural network to make up for the imbalance or insufficient information caused by the limitations of training corpus. The multi-stream network mainly consists of the basal stream, which retained original sequence information, and background knowledge based streams. Background knowledge is composed of keywords and co-occurred words which are extracted from external corpus. Background knowledge based streams are devoted to realizing supplemental information and reinforce basal stream. To better fuse the features extracted from different streams, early-fusion and two after-fusion strategies are employed. According to the results obtained from both Chinese corpus and English corpus, it is demonstrated that the proposed background knowledge based multi-stream neural network performs well in classification tasks.

GHS-NET A Generic Hybridized Shallow Neural Network for Multi-Label Biomedical Text Classification

2021 ◽  
pp. 103699
Author(s):  
Muhammad Ali Ibrahim ◽  
Muhammad Usman Ghani Khan ◽  
Faiza Mehmood ◽  
Muhammad Nabeel Asim ◽  
Waqar Mahmood
Keyword(s):  
Neural Network ◽  
Text Classification ◽  
Biomedical Text ◽  
Biomedical Text Classification

A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis

2006 ◽  
Vol 45 (06) ◽  
pp. 610-621 ◽  
Author(s):  
A. T. Tzallas ◽  
P. S. Karvelis ◽  
C. D. Katsis ◽  
S. Giannopoulos ◽  
S. Konitsiotis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Epileptic Activity ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Epilepsy Diagnosis ◽  
Transient Events ◽  
Artificial Neural ◽  
Eeg Recordings

Summary Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. Conclusions: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

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