[PDF] Top 20 Initializing Convolutional Filters with Semantic Features for Text Classification
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Initializing Convolutional Filters with Semantic Features for Text Classification
... Most recently, CNNs are becoming increasingly popular in a variety of NLP tasks. An influential one is the work of (Kim, 2014), where a simple CNN with a single layer of convolution is used for feature extraction. ... See full document
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An Empirical Evaluation of Temporal Convolutional Network for Offensive Text Classification
... employed Convolutional Neural Network (CNN) with word2vec [2] word embedding and CNN without word embedding, for Kaggle’s Toxic ...with text embedding proved to be their best performing ...the ... See full document
6
Very Deep Convolutional Networks for Text Classification
... one convolutional layer (using multiple widths and filters) followed by a max pooling layer over ...important features in a sentence, independent of their specific position, preserving their relative ... See full document
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Cluster Gated Convolutional Neural Network for Short Text Classification
... phrase features and their sequences, outperforms CNN and CNNM on AG News, Amazon Reviews and Yahoo! Answers datasets, while it has poorer performance on Sogou News and Search Snippets ... See full document
10
Understanding Convolutional Neural Networks for Text Classification
... of Convolutional Neural Networks (CNNs) for processing ...projecting filters into image space, but for discrete sequence in- puts CNNs remain a ...classify text. We examine com- mon hypotheses to ... See full document
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Falcon: A Novel Chinese Short Text Classification Method
... them, Convolutional Neural Network (CNN) gets noticeable ...sentence classification in a simple model archi- tecture (also called ...Its text matrix is convolved by multiple filters with ... See full document
11
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
... knowledge features part does not improve ...selected semantic types that are considered most relevant to clinical ...on semantic types did lead to moderate performance improvement over using all ... See full document
9
Relation Extraction: Perspective from Convolutional Neural Networks
... Our convolutional neural network is built upon that of Kalchbrenner et ...relation classification that apply a single window size, our model for relation extrac- tion incorporates various window sizes for ... See full document
10
Sequential Learning of Convolutional Features for Effective Text Classification
... Text classification has been one of the ma- jor problems in natural language ...of text processing, namely in their elementary blocks: convolution filters and max pool- ...tial ... See full document
10
Enhanced Weight Based Convolutional Neural Network (EWCNN) and Fuzzy Clustering For Semantically Rich Multi Label Social Emotion Classification
... Emotion Classification Task A Complex ...Low-Level Features Into Dense High- Level Features, Was The 1 st Contribution, Next Is, An Enhanced Weight Based Convolutional Neural Network (EWCNN) ... See full document
10
OCT Image Classification Model using Hybrid Filters and Convolutional Neural Networks
... multi-label convolutional neural network (MLCNN) to predict multiple attributes in a given framework and tested the algorithm on the PEdesTrian Attribute (PETA) ... See full document
6
Cancer Hallmark Text Classification Using Convolutional Neural Networks
... biomedical text classification using machine learning methods that emphasize feature learning rather than manual feature ...on convolutional neural networks ...in text classification ... See full document
9
Detection of medical text semantic similarity based on convolutional neural network
... latent semantic analysis (LSA) [6], latent Dirichlet allocation (LDA) [7], Doc2Vec [21], Siamese LSTM [22] and a method based on named entity recognition (NER) ...the features contribute most to the final ... See full document
11
Identifying Eyewitness News worthy Events on Twitter
... The filters are particularly appealing in a busi- ness application; with minimal training we were able to teach users of our platform to construct new rules to find eyewitness events in different topical ... See full document
7
A Review of Relation Classification with Convolutional Neural Network Kartik Dhiwar *1 , Abhishek Kumar Dewangan 2
... Multi-Way Classification of Semantic Relations between Pairs of ...relation classification will be our future work and we will design our own CNN architecture for further improved results in relation ... See full document
5
Multi class Animacy Classification with Semantic Features
... As for future work, the fact that animacy is marked explicitly in many languages presents a golden opportunity to alleviate the annotation of this semantic property for languages in which it is not explicitly ... See full document
11
Text Classification Based on LDA and Semantic Analysis
... of text features directly affects the text classification effect, in order to get the text features which have the high contribution to the text classification in ... See full document
7
Khmer handwritten text recognition with Convolution Neural Networks
... As mentioned in the data collection section, each of the six datasets is comprised of 561 (33X17) symbols. Division of each image is filed into an individual image of each syllable, cropping white space, and resizing to ... See full document
6
The construction of Chinese microblog gender-specific thesauruses and user gender classification
... higher classification accuracy, we will continue to explore other ...accurate classification model based on the results of arti- ficial neural networks to further improve the accuracy of gender ... See full document
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1. A review paper on image spam filtering
... When it acts on text e-mails, classification techniques of text seem to be efficient. However, spammers do not stop to invent tricks to avoid filters. One of these tricks is to include the ... See full document
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