scholarly journals SUMMARIZING INDONESIAN NEWS ARTICLES USING GRAPH CONVOLUTIONAL NETWORK

2019 ◽  
Author(s):  
Garmastewira Garmastewira ◽  
Masayu Leylia Khodra
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recall Score ◽  
Word Embedding ◽  
Graph Representation ◽  
Representation System ◽  
Convolutional Network ◽  
Document Summarization ◽  
Main Components ◽  
Representation Types

Multi-document summarization transforms a set of related documents into one concise summary. Existing Indonesian news articles summarizations do not take relationships between sentences into account and heavily depends on Indonesian language tools and resources. In this paper, we employ Graph Convolutional Network (GCN) which accepts word embedding sequence and sentence relationship graph as input for Indonesian news articles summarization. Our system is comprised of four main components, which are preprocess, graph construction, sentence scoring, and sentence selection components. Sentence scoring component is a neural network that uses Recurrent Neural Network (RNN) and GCN to produce the scores of all sentences. We use three different representation types for the sentence relationship graph. Sentence selection component then generates summary with two different techniques, which are by greedily choosing sentences with the highest scores and by using Maximum Marginal Relevance (MMR) technique. The evaluation shows that GCN summarizer with Personalized Discourse Graph (PDG) graph representation system achieves the best results with average ROUGE-2 recall score of 0.370 for 100-word summary and 0.378 for 200-word summary. Sentence selection using greedy technique gives better results for generating 100-word summary, while MMR performs better for generating 200-word summary.  

scholarly journals Real-time event detection using recurrent neural network in social sensors

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985649 ◽  
Author(s):  
Van Quan Nguyen ◽  
Tien Nguyen Anh ◽  
Hyung-Jeong Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Word Embedding ◽  
Series Data ◽  
Accuracy Score ◽  
Data Set ◽  
Size Limitation

We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on available embedding vector. In this article, word-embedding features at the embedding layer are combined and fed to convolutional neural network. The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. An error distribution estimation model is built to calculate the anomaly score of observation. Events are detected using a window-based method on the anomaly scores.

scholarly journals Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation

2017 ◽  
Vol 25 (1) ◽  
pp. 72-80 ◽  
Author(s):  
Jiaheng Xie ◽  
Xiao Liu ◽  
Daniel Dajun Zeng
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Adverse Events ◽  
Recurrent Neural Network ◽  
Deep Neural Network ◽  
Word Embedding ◽  
Named Entity ◽  
Text Input ◽  
Safety Surveillance ◽  
F Measure

Abstract Objective Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers’ e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media. Methods Our deep neural language model utilizes word embedding as the representation of text input and recognizes named entity types with the state-of-the-art Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Network. Results Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of 91.80%, and an F-measure of 92.94%. We identified 1591 unique adverse events and 9930 unique e-cigarette components (ie, chemicals, flavors, and devices) from our research testbed. Conclusion Although the conditional random field baseline model had slightly better precision than our approach, our Bi-LSTM model achieved much higher recall, resulting in the best F-measure. Our method can be generalized to extract medical concepts from social media for other medical applications.

scholarly journals Solving the problem of classification of material properties using a neural network

2021 ◽  
Vol 2131 (3) ◽  
pp. 032084
Author(s):  
N E Babushkina ◽  
A A Lyapin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Recurrent Neural Network ◽  
Material Properties ◽  
Practical Significance ◽  
Experimental Measurements ◽  
Statistical Parameters ◽  
The Neural Network ◽  
Main Components

Abstract The article sets the task of classifying various materials and determining their belonging to a specified group using a recurrent neural network. The practical significance of the article is to obtain the results of the neural network, confirming the possibility of classifying materials by the hardness parameter using a neural network. As part of the study, a number of experimental measurements were carried out. The structure of the neural network and its main components are described. The statistical parameters of the experimental data are estimated.

scholarly journals SISTEM PERINGKAS OTOMATIS ABSTRAKTIF DENGAN MENGGUNAKAN RECURRENT NEURAL NETWORK

2018 ◽  
Vol 2 (1) ◽  
pp. 65 ◽  
Author(s):  
Kuncoro Yoko ◽  
Viny Christanti Mawardi ◽  
Janson Hendryli
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Model System ◽  
Text Summarization ◽  
Word Embedding ◽  
System Evaluation ◽  
Data Set ◽  
Hidden States ◽  
Bahasa Indonesia

Abstractive Text Summarization try to creates a shorter version of a text while preserve its meaning. We try to use Recurrent Neural Network (RNN) to create summaries of Bahasa Indonesia text. We get corpus from Detik dan Kompas site news. We used word2vec to create word embedding from our corpus then train our data set with RNN to create a model. This model used to generate news. We search the best model by changing word2vec size and RNN hidden states. We use system evaluation and Q&A Evaluation to evaluate our model. System evaluation showed that model with 6457 data set, 200 word2vec size, and 256 RNN hidden states gives best accuracy for 99.8810%. This model evaluated by Q&A Evaluation. Q&A Evaluation showed that the model gives 46.65% accurary.

scholarly journals Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition

2018 ◽  
Vol 10 (12) ◽  
pp. 123 ◽  
Author(s):  
Mohammed Ali ◽  
Guanzheng Tan ◽  
Aamir Hussain
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Word Embedding ◽  
Entity Recognition ◽  
Named Entity ◽  
Lstm Network

Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task of NLP and can be considered a classification problem. We propose a bidirectional long short-term memory (LSTM) model for this entity recognition task of the Arabic text. The LSTM network can process sequences and relate to each part of it, which makes it useful for the NER task. Moreover, we use pre-trained word embedding to train the inputs that are fed into the LSTM network. The proposed model is evaluated on a popular dataset called “ANERcorp.” Experimental results show that the model with word embedding achieves a high F-score measure of approximately 88.01%.

scholarly journals Temporal convolutional networks allow early prediction of events in critical care

2019 ◽  
Vol 27 (3) ◽  
pp. 355-365 ◽  
Author(s):  
Finneas J R Catling ◽  
Anthony H Wolff
Keyword(s):  
Neural Network ◽  
Longitudinal Data ◽  
Recurrent Neural Network ◽  
Demographic Data ◽  
Fluid Challenge ◽  
Health Data ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Clinical Events ◽  
Partial Dependence

Abstract Objective Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients’ longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action. Materials and Methods We use a temporal convolutional network to encode longitudinal data and a feedforward neural network to encode demographic data from 4713 ICU admissions in 2014–2018. For each hour of each admission, we predict events in the subsequent 1–6 hours. We compare performance with other models including a recurrent neural network. Results Our model performed similarly to the recurrent neural network for some events and outperformed it for others. This performance increase was more evident in a sensitivity analysis where the prediction timeframe was varied. Average positive predictive value (95% CI) was 0.786 (0.781–0.790) and 0.738 (0.732–0.743) for up- and down-titrating FiO2, 0.574 (0.519–0.625) for extubation, 0.139 (0.117–0.162) for intubation, 0.533 (0.492–0.572) for starting noradrenaline, 0.441 (0.433–0.448) for fluid challenge, and 0.315 (0.282–0.352) for death. Discussion Events were better predicted where their important determinants were captured in structured electronic health data, and where they occurred in homogeneous circumstances. We produce partial dependence plots that show our model learns clinically-plausible associations between its inputs and predictions. Conclusion Temporal convolutional networks improve prediction of clinical events when used to represent longitudinal ICU data.

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