[PDF] Top 20 Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
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Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
... the text re- gions listed in the same row are close to each other as they have a large value in the same ...(or text regions of size n) can con- tribute to accurate prediction even if they did not appear in ... See full document
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Deep Pyramid Convolutional Neural Networks for Text Categorization
... as text or image or the output of the pre- vious layer) at every location ...every word), which can be processed in ...to text, a recurrent unit takes words one by one as well as its own output on ... See full document
9
Sentiment Classification Via Recurrent Convolutional Neural Networks
... Recurrent Neural Network (RecurrentNN). This model uses a word to analyze a text word, and it stores all the previous text semantics in the hidden layer of a fixed ...the ... See full document
9
Chinese Text Sentiment Analysis using Bilinear Character Word Convolutional Neural Networks
... Chinese text sentiment analysis tasks, [7] applies typical traditional machine learning methods ...and word-level pre-trained word vectors, as well as dual-channel input ...segmented word may ... See full document
8
Convolutional Neural Networks for Financial Text Regression
... a text regression problem. Recent studies use a manually la- beled lexicon to filter the annual reports by keeping sentiment words ...In order to re- move the lexicon dependency without decreas- ing ... See full document
7
Understanding Convolutional Neural Networks for Text Classification
... the word-level, but instead form slot activation patterns that give different types of ngrams similar activation ...certain word activations in order to cause the ngrams which contain them to receive ... See full document
10
Applying deep matching networks to Chinese medical question answering: a study and a dataset
... Chinese word segmentation failure in some ...multi-scale convolutional neural network (CNN, [16]) for Chinese medical QA and released a dataset ... See full document
10
Discriminating between Similar Languages with Word level Convolutional Neural Networks
... a word-level convolu- tional neural network (CNN) that learns task-specific vectors with minimal text ...(MLP) networks and another hybrid ... See full document
7
Short Text Clustering via Convolutional Neural Networks
... Short text clustering has become an increas- ing important task with the popularity of so- cial media, and it is a challenging problem due to its sparseness of text ...Short Text Clustering via ... See full document
8
Transparent text quality assessment with convolutional neural networks
... two text corpora (Blogs and ...first word, ‘domen’ (the ...frequent use of ellipsis, and in- formal spellings such as ‘ox˚a’ for ‘ocks˚a’ ...Internet text, and would easily be avoided in ... See full document
5
Semantic Clustering and Convolutional Neural Network for Short Text Categorization
... Neural networks have been used to model lan- guages, and the word embeddings can be learned simultaneously (Mnih and Teh, ...quality word embeddings from large-scale un- structured text ... See full document
6
Sequential Short Text Classification with Recurrent and Convolutional Neural Networks
... Table 4: Accuracy (%) of our models and other methods from the literature. The majority class model predicts the most fre- quent class. SVM: (Dernoncourt et al., 2016). Graphical model: (Ji and Bilmes, 2006). Naive ... See full document
6
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization
... to use as an off-the-shelf solution – re-training the neural network to obtain task-specific embeddings requires a certain amount of training data, admit- tedly unlabeled, but still not optimal under our ... See full document
6
CogALex V Shared Task: GHHH Detecting Semantic Relations via Word Embeddings
... (2004) use a logistic regression clas- sifier for hypernym pair ...not use any lexical, syntactic or semantic features, other than the word embeddings and we score similarity using the well known ... See full document
6
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... feedforward neural network, the depth of the CAPs (thus of the network) is the number of hidden layers plus one (as the output layer is also parameterized), but for recurrent neural networks, in ... See full document
5
Cancer Hallmark Text Classification Using Convolutional Neural Networks
... of convolutional neural networks to the biomedical do- main text classification task of identifying the hallmarks of cancer associated with publication ...document text and word ... See full document
9
The Application of Neural Network in Multiple Object Tracking
... Multiple object tracking is an important matter in the computer vision. It is meaningful for video surveillance, robot navigation and localization, intelligent traffic and other fields. Although some progress has been ... See full document
7
YNUWB at SemEval 2019 Task 6: K max pooling CNN with average meta embedding for identifying offensive language
... In recent years, offensive language has prevailed in social media, and people are increasingly inter- ested in identifying offensive speech, especially on social media platforms. This topic has attracted the attention of ... See full document
5
Extraction of Fingerprint Pore with the use of Convolutional Neural Networks
... to use the finger print images as the input for CNN, than the map of pore concentration is the output resultant with the finger prints ...basically use for features compressions in the objects recognitions, ... See full document
5
Wearable AI Device for Blind
... CCN has been used for face recognition character recognition and object identification. An interestingly new approach based on a convolutional neural architecture which was presented in 2009 designed to ... See full document
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