Search results for: Xudong Luo

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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Xudong Luo"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Xudong Luo</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> A Survey of the Applications of Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pingping%20Lin">Pingping Lin</a>, <a href="https://publications.waset.org/search?q=Xudong%20Luo"> Xudong Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural language often conveys emotions of speakers. Therefore, sentiment analysis on what people say is prevalent in the field of natural language process and has great application value in many practical problems. Thus, to help people understand its application value, in this paper, we survey various applications of sentiment analysis, including the ones in online business and offline business as well as other types of its applications. In particular, we give some application examples in intelligent customer service systems in China. Besides, we compare the applications of sentiment analysis on Twitter, Weibo, Taobao and Facebook, and discuss some challenges. Finally, we point out the challenges faced in the applications of sentiment analysis and the work that is worth being studied in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Natural%20language%20processing" title="Natural language processing">Natural language processing</a>, <a href="https://publications.waset.org/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/search?q=application" title=" application"> application</a>, <a href="https://publications.waset.org/search?q=online%20comments." title=" online comments."> online comments.</a> </p> <a href="https://publications.waset.org/10011530/a-survey-of-the-applications-of-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011530/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011530/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011530/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011530/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011530/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011530/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011530/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011530/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011530/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011530/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">953</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Fine-Grained Sentiment Analysis: Recent Progress</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jie%20Liu">Jie Liu</a>, <a href="https://publications.waset.org/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/search?q=Pingping%20Lin"> Pingping Lin</a>, <a href="https://publications.waset.org/search?q=Yifan%20Fan"> Yifan Fan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people鈥檚 opinions or sentiments for better decision-making. So, sentiment analysis, especially the fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, ma-chine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/search?q=fine-grained" title=" fine-grained"> fine-grained</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/10012408/fine-grained-sentiment-analysis-recent-progress" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012408/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012408/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012408/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012408/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012408/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012408/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012408/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012408/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012408/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012408/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2397</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> On Dialogue Systems Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yifan%20Fan">Yifan Fan</a>, <a href="https://publications.waset.org/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/search?q=Pingping%20Lin"> Pingping Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dialogue%20management" title="Dialogue management">Dialogue management</a>, <a href="https://publications.waset.org/search?q=response%20generation" title=" response generation"> response generation</a>, <a href="https://publications.waset.org/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/search?q=evaluation." title=" evaluation."> evaluation.</a> </p> <a href="https://publications.waset.org/10011653/on-dialogue-systems-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011653/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011653/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011653/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011653/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011653/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011653/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011653/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011653/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011653/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011653/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011653.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">787</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Personal Information Classification Based on Deep Learning in Automatic Form Filling System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Shunzuo%20Wu">Shunzuo Wu</a>, <a href="https://publications.waset.org/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/search?q=Yuanxiu%20Liao"> Yuanxiu Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Personal%20information" title="Personal information">Personal information</a>, <a href="https://publications.waset.org/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/search?q=auto%20fill" title=" auto fill"> auto fill</a>, <a href="https://publications.waset.org/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/search?q=document%20analysis." title=" document analysis."> document analysis.</a> </p> <a href="https://publications.waset.org/10011599/personal-information-classification-based-on-deep-learning-in-automatic-form-filling-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011599/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011599/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011599/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011599/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011599/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011599/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011599/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011599/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011599/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011599/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011599.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">861</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> A Survey of Response Generation of Dialogue Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yifan%20Fan">Yifan Fan</a>, <a href="https://publications.waset.org/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/search?q=Pingping%20Lin"> Pingping Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Retrieval" title="Retrieval">Retrieval</a>, <a href="https://publications.waset.org/search?q=generative" title=" generative"> generative</a>, <a href="https://publications.waset.org/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/search?q=response%0D%0Ageneration" title=" response generation"> response generation</a>, <a href="https://publications.waset.org/search?q=knowledge." title=" knowledge."> knowledge.</a> </p> <a href="https://publications.waset.org/10011629/a-survey-of-response-generation-of-dialogue-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011629/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011629/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011629/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011629/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011629/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011629/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011629/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011629/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011629/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011629/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011629.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1204</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> A Survey of Sentiment Analysis Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pingping%20Lin">Pingping Lin</a>, <a href="https://publications.waset.org/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/search?q=Yifan%20Fan"> Yifan Fan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Natural%20language%20processing" title="Natural language processing">Natural language processing</a>, <a href="https://publications.waset.org/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/search?q=document%20analysis" title=" document analysis"> document analysis</a>, <a href="https://publications.waset.org/search?q=multimodal%20sentiment%20analysis" title=" multimodal sentiment analysis"> multimodal sentiment analysis</a>, <a href="https://publications.waset.org/search?q=deep%20learning." title=" deep learning."> deep learning.</a> </p> <a href="https://publications.waset.org/10011630/a-survey-of-sentiment-analysis-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a 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