Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval

Li, Ang; Du, Junping; Kou, Feifei; Xue, Zhe; Xu, Xin; Xu, Mingying; Jiang, Yang

doi:10.48550/arxiv.2203.08615

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…After obtaining the gating information, the current input x t is spliced with the reset gate, then the output of the currently hidden node ht is activated by the tanh activation function. The calculation formula is shown in formula (3), where W is the weight parameter of the hidden layer.…”

Section: Input Outputmentioning

confidence: 99%

“…It is not difficult to see that the Internet has been deeply integrated into people's daily life. More and more people choose to release their emotions on social networks [1][2][3][4]. Therefore, how to use of Internet public opinion tendency to guide actual production and life has become a very important application prospect of text sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

Wang¹,

Du²,

Shao³

et al. 2022

Preprint

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With the development of online travel services, it has great application prospects to timely mine users' evaluation emotions for travel services and use them as indicators to guide the improvement of online travel service quality. In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon. SCCL model aims at the lack of consideration of local features and emotional semantic features of the text in the language model that can efficiently extract text context features like BERT and GRU. Then make the following improvements to their shortcomings. On the one hand, based on BERT-BiGRU, the capsule network is introduced to extract local features while retaining good context features. On the other hand, the sentiment lexicon is introduced to extract the emotional sequence of the text to provide richer emotional semantic features for the model. To enhance the universality of the sentiment lexicon, the improved SO-PMI algorithm based on TF-IDF is used to expand the lexicon, so that the lexicon can also perform well in the field of online travel reviews.

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Section: Input Outputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

Wang¹,

Du²,

Shao³

et al. 2022

Preprint

Self Cite

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show abstract

“…The purpose of word embedding is to learn the vector representation of a word in its context [1]. The learned word vectors can be used in subsequent natural language processing tasks, such as semantic similarity calculating [2,3,4] and sentiment analysis [5,6,7]. The quality of word vectors learned by word embedding method can directly influence the performance of these tasks.…”

Section: Introductionmentioning

confidence: 99%

Chinese Word Sense Embedding with SememeWSD and Synonym Set

Zhou¹,

Du²,

Xue³

et al. 2022

Preprint

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Word embedding is a fundamental natural language processing task which can learn feature of words. However, most word embedding methods assign only one vector to a word, even if polysemous words have multi-senses. To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet. We use the SememeWSD model, an unsupervised word sense disambiguation model based on OpenHowNet, to do word sense disambiguation and annotate the polysemous word with sense id. Then, we obtain top 10 synonyms of the word sense from OpenHowNet and calculate the average vector of synonyms as the vector of the word sense. In experiments, We evaluate the SWSDS model on semantic similarity calculation with Gensim's wmdistance method. It achieves improvement of accuracy. We also examine the SememeWSD model on different BERT models to find the more effective model.

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“…Cross-media target retrieval has a wide range of application scenarios [3,4]. Text or voice with dialogue question answering system has been widely used in mobile terminals and PC operating systems, as an important way of humancomputer interaction, for example, Siri of Apple, Cortana of Microsoft, and Alexa of Amazon.…”

Section: Introductionmentioning

confidence: 99%

“…In the helmet detection process, computer vision is used to automatically detect whether a worker at a construction site is wearing a helmet, including body detection, helmet detection, and matching. When a mismatch is detected between the spatial position of the human body and the helmet, a safety alert is issued [4,5].…”

Section: Introductionmentioning

confidence: 99%

Construction of Safety Early Warning Model for Construction of Engineering Based on Convolution Neural Network

Zhao

2022

Computational Intelligence and Neuroscience

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In recent years, China’s engineering construction management level has been greatly improved, but compared with other industries, the construction industry still has low production efficiency, serious waste, and low level of information problems, and especially in the process of engineering management practice, schedule delay has become the focus of engineering management problems. With the continuous development of science and technology, computer and information technology have been continuously applied in engineering, among which deep neural network (DNN) technology, lean management, information visualization technology, and other technologies have become the hot spot of industry research, and the application of these emerging technologies to improve the level of project schedule control has become an urgent demand of the industry. Therefore, on the basis of deep learning, this paper analyzes the principle and application of object detection and feature extraction constructed by neural network and combines text feature extraction and image feature extraction methods. This application provides a new idea for the development of the construction industry.

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Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval

Cited by 5 publications

References 33 publications

Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

Chinese Word Sense Embedding with SememeWSD and Synonym Set

Construction of Safety Early Warning Model for Construction of Engineering Based on Convolution Neural Network

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