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Multi-document hybrid text summarization with bi-LSTM RNN for Telugu language

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2024年第2期49卷 1-12页

作者： Babu, G. L. Anand Badugu, Srinivasu Anurag Univ Dept Informat Technol Hyderabad 500088 Telangana India Stanley Coll Engn & Technol Women Dept Comp Sci & Engn Hyderabad 500001 Telangana India

One of the most popular south Indian languages in India is the Telugu language which is currently spoken by 84 million native Telugu speakers in Andhra Pradesh and Telangana. With the rapid growth of the Telugu digital content, the need for the automatic text summarizer is arisen to provide short text from huge text documents. Extractive text summarization model generates only significant sentences. Abstractive text summarization method requires more training time. In this paper, a novel hybrid model is proposed for generating text summaries by combining extractive and abstractive approach to reduce the training time. For extractive method TextRank algorithm is utilized and for abstractive method attention-based sequence to sequence model with bidirectional long short-term memory (Bi-LSTM) is utilized. Moreover, coverage mechanism is included into the proposed hybrid approach to reduce the repetition in summaries and to improve the quality of summaries. The performance of the proposed hybrid model is evaluated by the ROUGE toolkit in terms of F-measure, recall and precision. The results of the proposed model are compared with other existing models which shows that the proposed hybrid model outperforms other existing text summarization models for Telugu Language.

关键词： TextRank algorithm sequence to sequence model bidirectional long short-term memory (Bi-LSTM) attention mechanism coverage mechanism ROUGE

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Multi-step traffic speed prediction model with auxiliary features on urban road networks and its understanding

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第14期14卷 1997-2009页

作者： Guo, Jinlong Song, Chunyue Zhang, Hao Wang, Hui Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou Peoples R China

Multi-step prediction of long-term traffic speed is an important part of the intelligent transportation system. Traffic speed is affected by temporal features, spatial features, and various environmental features. The prediction of traffic speed considering the above features is a big challenge. This study proposed a multi-step prediction model named embedding graph convolutional long short-term memory network (EGC-LSTM) for urban road network traffic speed prediction which can deal with spatial-temporal correlation and auxiliary features at the same time. Firstly, a graph convolutional network (GCN) for capturing directed graph properties is proposed. Based on the GCN, the LSTM and sequence to sequence model are further applied to realise multi-step prediction considering the spatial-temporal correlation of the traffic network. To improve the performance of the model and obtain the importance of each step in the historical data, the attention mechanism is introduced. Then, one-hot encoding is applied to the category-type auxiliary features. Considering that the dimension becomes larger after the features are one-hot encoded, the dimensions are reduced using embedding. The experiment results prove that the proposed model's performance is better than other models, and the model is interpreted in detail.

关键词： road traffic traffic engineering computing directed graphs convolutional neural nets intelligent transportation systems recurrent neural nets multistep traffic speed prediction model urban road networks long-term traffic speed intelligent transportation system temporal features spatial features environmental features embedding graph convolutional long short-term memory network urban road network traffic speed prediction spatial-temporal correlation graph convolutional network directed graph properties sequence to sequence model traffic network category-type auxiliary features EGC-LSTM attention mechanism one-hot encoding

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Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach

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JOURNAL OF CLEANER PRODUCTION 2020年 253卷 119841-119841页

作者： Wang, Hong-Wei Li, Xiao-Bing Wang, Dongsheng Zhao, Juanhao He, Hong-di Peng, Zhong-Ren Shanghai Jiao Tong Univ Ctr Intelligent Transportat Syst & Unmanned Aeria Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China Jinan Univ Inst Environm & Climate Res Guangzhou 510632 Peoples R China Univ Southern Calif Viterbi Sch Engn Dept Comp Sci Los Angeles CA 90089 USA Univ Florida Coll Design Construct & Planning Int Ctr Adaptat Planning & Design iAdapt Sch Landscape Architecture & Planning POB 115706 Gainesville FL 32611 USA

Ozone is one of the most important greenhouse gases and air pollutants in urban areas, and has significantly negative impacts both on the climate change and human health. In addition to alert the public for health concerns, accurate ozone prediction is also crucial to understand the formation mechanisms of surface ozone episodes, and has significant implications for making emission control strategies of ozone precursors such as methane, carbon monoxide, and volatile organic compounds. However, existing methods of ozone concentration prediction could not effectively capture temporal dependency, and most neglect spatial correlations. In this study, a hybrid sequence to sequence model embedded with the attention mechanism is proposed for predicting regional ground-level ozone concentration. In the proposed model, the inherent spatiotemporal correlations in air quality monitoring network are simultaneously extracted, learned and incorporated, and auxiliary air pollution and meteorological information are adaptively involved. The hourly data collected from 35 air quality monitoring stations and 651 meteorological girds in Beijing, China are used to validate the present model. The results demonstrate that the spatiotemporal correlations in the monitoring network present enormous advantages for the regional ozone prediction. Auxiliary data and time lags matching day-of-week or diurnal periods of ozone are confirmed to benefit the improvement of prediction accuracy. Monitoring stations in urban areas exhibit better prediction performances than stations in remote areas. The addressed model outperforms the baseline models, and is proven to have excellent performance in all monitoring station categories of Beijing and different months with significant disparity of ozone concentrations. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Ozone prediction Spatiotemporal correlation Air quality monitoring network sequence to sequence model Deep learning

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Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network

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ENERGY 2020年 190卷 1页

作者： Xie, Peiran Gao, Mingming Zhang, Hongfu Niu, Yuguang Wang, Xiaowen North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Sch Control & Comp Engn Beijing 102206 Peoples R China State Grid Shanxi Integrated Energy Serv Co Ltd Taiyuan 030001 Peoples R China

As environmental protection policies become more stringent, lower and lower NOx emission targets are required. Accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. This study aims to accurately predict the future sequence of NOx emission in the next horizon. Through the analysis on formation mechanism of NOx and the reaction mechanism of SCR reactor, a sequence to sequence dynamic prediction model is proposed, which can fit multivariable coupling, nonlinear and large delay systems. In particular, considering the different effects of multivariate on NOx, a new attention mechanism is necessary to be put forward. A large amount of historical data is used to fully train this dynamic prediction model. The results show that, the prediction accuracy of the NOx concentration and fluctuation trend based on this model is superior to comparison algorithms. Furthermore, some interesting features of this prediction model, such as error accumulation and bidirectional encoder, are also discussed in depth. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Selective catalytic reduction NOx sequence prediction sequence to sequence model Long short-term memory

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Forecasting People's Needs in Hurricane Events from Social Network

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IEEE TRANSACTIONS ON BIG DATA 2022年第1期8卷 229-240页

作者： Nguyen, Long Yang, Zhou Li, Jia Pan, Zhenhe Cao, Guofeng Jin, Fang Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Texas Tech Univ Dept Civil Environm & Construct Engn Lubbock TX 79409 USA Kinet DB Inc Arlington VA 22203 USA Texas Tech Univ Dept Geosci Lubbock TX 79409 USA

Social networks can serve as a valuable communication channel for asking for help, offering assistance, and coordinating rescue activities in disaster because it allows users to continuously update critical information in the fast-changing disaster environment. This paper presents a novel sequence to sequence based framework for forecasting people's needs during disasters using social media and weather data. It consists of two Long Short-Term Memory (LSTM) models, one of which encodes input sequences of weather information and the other plays as a conditional decoder that decodes the encoded vector and forecasts the survivors' needs. Case studies using data collected during Hurricane Sandy in 2012, Hurricane Harvey and Hurricane Irma in 2017 demonstrate that the proposed approach outperformed the statistical language model n-gram, LSTM generative model, and convolutional neural network (CNN) based model. This research indicates its great promise for enhancing disaster management such as evacuation planning and commodity delivery.

关键词： Disaster relief needs forecasting concern flow LSTM hurricane events sequence to sequence model

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Deep Trajectory Recovery with Fine-Grained Calibration using Kalman Filter

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第3期33卷 921-934页

作者： Wang, Jingyuan Wu, Ning Lu, Xinxi Zhao, Wayne Xin Feng, Kai Beihang Univ Sch Comp Sci & Engn Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn MOE Engn Res Ctr Adv Comp Applicat Technol Beijing 100191 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Renmin Univ China Sch Informat Haidian 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

With the development of location-acquisition technologies, there are a huge number of mobile trajectories generated and accumulated in a variety of domains. However, due to the constraints of device and environment, many trajectories are recorded at low sampling rate, which increases the uncertainty between two consecutive sampled points in the trajectories. Our task is to recover a high-sampled trajectory based on the irregular low-sampled trajectory in free space, i.e., without road network information. There are two major problems with traditional solutions. First, many of these methods rely on heuristic search algorithms or simple probabilistic models. They cannot well capture complex sequential dependencies or global data correlations. Second, for reducing the predictive complexity of the unconstrained numerical coordinates, most of the previous studies have adopted a common preprocessing strategy by mapping the space into discrete units. As a side effect, using discrete units is likely to bring noise or inaccurate information. Hence, a principled post-calibration step is required to produce accurate results, which has been seldom studied by existing methods. To address the above difficulties, we propose a novel Deep Hybrid Trajectory Recovery model, named DHTR. Our recovery model extends the classic sequence-to-sequence generation framework by implementing a subsequence-to-sequence recovery model tailored for the current task, named subseq2seq. In order to effectively capture spatiotemporal correlations, we adopt both spatial and temporal attentions for enhancing the model performance. With the attention mechanisms, our model is able to characterize long-range correlations among trajectory points. Furthermore, we integrate the subseq2seq with a calibration component of Kalman filter (KF) for reducing the predictive uncertainty. At each timestep, the noisy predictions from the subseq2seq component will be fed into the KF component for calibration, and then the

关键词： Trajectory Computational modeling Task analysis Roads Data models Correlation Calibration Trajectory recovery sequence to sequence model spatiotemporal attention kalman filter

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Corpus-Based Translation Automation of Adaptable Corpus Translation Module 5

Corpus-Based Translation Automation of Adaptable Corpus Tran...

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5th International Conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Lutskiv, Andriy Lutsyshyn, Roman Ternopil Ivan Puluj Natl Tech Univ Ruska 56 UA-46001 Ternopol Ukraine

Thesis deals with the translation module development which automates corpus-based translation. The translation module is a part of an adaptable corpus tool and is implemented as a separate microservice. This translation module provides for the linguist and translator the ability to translate texts with conveying their style. The translation module is domain-specific oriented which allows to convey text style better than public cloud translation services. In this research religious and historical texts were analyzed. Neural machine translation method was justified and used. sequence to sequence Transformer model as a neural network model was chosen. All stages of text processing by the Transformer model which based on the Multi-Head Attention mechanism were analyzed. Software libraries and toolkits for the sequence to sequence Transformer model were analyzed and chosen. Based on chosen software libraries implemented own Transformer model implementation. Developed model comprises text preprocessing and neural network model implementation. Cost-efficient computer system which comprises hardware and software components for neural network model training was configured. Based on heuristic approach by carrying out computational experiments neural network model hyper-parameters were chosen and justified. Loss function, learning rate, perplexity and BLEU as a key model training criterion were analyzed and applied. Training and test samples of text data sets were prepared. Training and test data sets comprise language pairs of Ukrainian text fragments and their English equivalents. Configured neural network model was trained and tested. Automatic assessment approach of trained model which based on semantic closeness was suggested and tested.

关键词： Natural language processing neural machine translation corpus-based translation Transformer model sequence to sequence model deep learning machine learning adaptable corpus tool Computer-Aided Translation

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Review Response Generation in E-Commerce Platforms with External Product Information 19

Review Response Generation in E-Commerce Platforms with Exte...

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World Wide Web Conference (WWW)

作者： Zhao, Lujun Song, Kaisong Sun, Changlong Zhang, Qi Huang, Xuanjing Liu, Xiaozhong Fudan Univ Shanghai Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Indiana Univ Bloomington IN 47405 USA Alibaba Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450366748

"User reviews" are becoming an essential component of e-commerce. When buyers write a negative or doubting review, ideally, the sellers need to quickly give a response to minimize the potential impact. When the number of reviews is growing at a frightening speed, there is an urgent need to build a response writing assistant for customer service providers. In order to generate high-quality responses, the algorithm needs to consume and understand the information from both the original review and the target product. The classical sequence-to-sequence (Seq2Seq) methods can hardly satisfy this requirement. In this study, we propose a novel deep neural network model based on the Seq2Seq framework for the review response generation task in e-commerce platforms, which can incorporate product information by a gated multi-source attention mechanism and a copy mechanism. Moreover, we employ a reinforcement learning technique to reduce the exposure bias problem. To evaluate the proposed model, we constructed a large-scale dataset from a popular e-commerce website, which contains product information. Empirical studies on both automatic evaluation metrics and human annotations showthat the proposed model can generate informative and diverse responses, significantly outperforming state-of-the-art text generation models.

关键词： Review Response Generation Neural Network sequence to sequence model Gated Multi-Source Attention Mechanism Reinforcement Learning

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Automated Smartification of Notices to Airmen 7

Automated Smartification of Notices to Airmen

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7th Swiss Conference on Data Science (SDS)

作者： Bravin, Marc Pfaffli, Daniel Mazumder, Sita Pouly, Marc Univ Lucerne Appl Sci & Arts Sch IT Rotkreuz Switzerland

ISBN: (纸本)9781728171777

Notices to airmen (NOTAMs) are text messages targeted to pilots to make them aware of short term events and obstacles. Typical examples are military actions in airspaces or closed runways. NOTAMs are written in a strongly compressed format and vocabulary, making the information hard to access for non-trained readers. Smart NOTAM is a commercial service offered by Skyguide that filters irrelevant messages for a specific route or period. Additionally, it converts the messages into a standardized phraseology and removes irrelevant information. Having access to thousands of NOTAMs together with their smartified version, a transformer model was trained and evaluated to automate the currently manual smartification process. NOTAMs contain a large number of factual details such as coordinates or radio frequencies, making the decoding process vulnerable to the inaccurate generation of such details. Therefore, the model was extended by a pointer-network that allows copying words directly from the source text. It achieves a BLEU score above 86% on unseen test data. Visual inspection of fails reveals many ground truth messages written in other languages than English or containing factual details added from other sources.

关键词： notice to airmen machine learning natural language processing sequence to sequence model transformer pointer-generator

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Arabic Optical Character Recognition Using Attention Based Encoder-Decoder Architecture 2

Arabic Optical Character Recognition Using Attention Based E...

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3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Sobhi, Mohamed Hifny, Yasser Elkaffas, Saleh Mesbah Arab Acad Sci Technol & Maritime Transport Coll Comp & IT Kerdasa Egypt Univ Helwan Cairo Egypt

ISBN: (纸本)9781450389266

Optical character recognition (OCR) systems are used to convert scanned documents into text. Arabic OCR is an active area of research where high accuracy is demanding. This paper focuses on building a model for converting images that contain Arabic text into their corresponding text using a deep learning approach. This model does not require any knowledge of the underlying language and it is simply trained end-to-end on the KAFD dataset. It combines several standard neural components from vision and natural language processing. Features are extracted from images using Convolutional Neural Networks (CNNs) where the features are arranged in a grid. Each row is then encoded using a Recurrent Neural Networks (RNNs). An RNN decoder with a visual attention mechanism is used to generate the output text. Our preliminary experiments show that the presented approach is effective. The overall obtained accuracy is 89.82%. However, the individual results for some fonts are higher than this score.

关键词： sequence to sequence model Encoder-Decoder Architecture Arabic OCR Convolutional Neural Networks (CNNs) Recurrent Neural Network (RNN) Attention Mechanism Long Short-Term Memory (LSTM)

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