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1.
Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data. 相似文献
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[目的/意义]大规模在线开放课程论坛具有丰富的用户评论数据。从大量未区分的评论数据中，自动识别出知识密度较高的探索型对话并挖掘其潜在价值，对于改善教师教学质量以及提高学生知识水平具有重要影响。[方法/过程]首先利用GloVe方法训练词向量，加强对文本语义的理解，然后利用卷积神经网络自动学习文本特征，提出一种基于深度学习的探索型对话自动识别模型，并在学堂在线平台《心理学概论》课程论坛标注数据集上进行实证与对比研究。[结果/结论]实验结果显示，利用GloVe方法预训练词向量以及在训练过程中不断对词向量进行学习修正能够提高模型效果。该模型识别探索型对话的F1值为0.94，相较于传统的朴素贝叶斯方法（0.88）、逻辑斯谛回归方法（0.89）、决策树方法（0.88）以及随机森林方法（0.88）取得较大提升，具有较高的实用性和较低的学习成本。 相似文献
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[目的/意义]针对在线医疗社区问答文本复杂程度高、结构化程度低的特点,结合卷积神经网络（CNN）和双向长短记忆神经网络（BiLSTM）两种深度学习模型以及条件随机场（CRF）模型,提出一套适用于在线医疗问答文本的实体识别方法并进行验证。 [过程/方法] 将问答文本进行清洗和BIO标注后,分别用CNN和BiLSTM进行字级别的特征抽取,将两种模型抽取到的特征进行融合,后放入CRF中训练出实体预测模型,再将问答文本放入训练好的模型中得到最终的实体识别结果。[结果/结论]在所选取的乳腺癌医疗社区问答文本数据集上,所提出的方法结果优于其他模型,且识别准确率达到92.3%,召回率达到89.3%,F值达到90.8%。 相似文献
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《Information processing & management》2022,59(1):102755
General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consider general user preferences. Recently, better performance improvement is reported by combining these two types of recommenders. However, most of the previous works typically treat each item separately and assume that each user–item interaction in a sequence is independent. This may be a too simplistic assumption, since there may be a particular purpose behind buying the successive item in a sequence. In fact, a user makes a decision through two sequential processes, i.e., start shopping with a particular intention and then select a specific item which satisfies her/his preferences under this intention. Moreover, different users usually have different purposes and preferences, and the same user may have various intentions. Thus, different users may click on the same items with an attention on a different purpose. Therefore, a user’s behavior pattern is not completely exploited in most of the current methods and they neglect the distinction between users’ purposes and their preferences. To alleviate those problems, we propose a novel method named, CAN, which takes both users’ purposes and preferences into account for the next-item recommendation. We propose to use Purpose-Specific Attention Unit (PSAU) in order to discriminately learn the representations of user purpose and preference. The experimental results on real-world datasets demonstrate the advantages of our approach over the state-of-the-art methods. 相似文献
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Neelakshi Sarma Sanasam Ranbir Singh Diganta Goswami 《Information processing & management》2019,56(1):151-166
With the explosion of multilingual content on Web, particularly in social media platforms, identification of languages present in the text is becoming an important task for various applications. While automatic language identification (ALI) in social media text is considered to be a non-trivial task due to the presence of slang words, misspellings, creative spellings and special elements such as hashtags, user mentions etc., ALI in multilingual environment becomes even more challenging task. In a highly multilingual society, code-mixing without affecting the underlying language sense has become a natural phenomenon. In such a dynamic environment, conversational text alone often fails to identify the underlying languages present in the text. This paper proposes various methods of exploiting social conversational features for enhancing ALI performance. Although social conversational features for ALI have been explored previously using methods like probabilistic language modeling, these models often fail to address issues related to code-mixing, phonetic typing, out-of-vocabulary etc. which are prevalent in a highly multilingual environment. This paper differs in the way the social conversational features are used to propose text refinement strategies that are suitable for ALI in highly multilingual environment. The contributions in this paper therefore includes the following. First, this paper analyzes the characteristics of various social conversational features by exploiting language usage patterns. Second, various methods of text refinement suitable for language identification are proposed. Third, the effects of the proposed refinement methods are investigated using various sentence level language identification frameworks. From various experimental observations over three conversational datasets collected from Facebook, Youtube and Twitter social media platforms, it is evident that our proposed method of ALI using social conversational features outperforms the baseline counterparts. 相似文献
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Is it possible to identify crime suspects by their mobile phone call records? Can the spatial-temporal movements of individuals linked to convicted criminals help to identify those who facilitate crime? Might we leverage the usage of mobile phones, such as incoming and outgoing call numbers, coordinates, call duration and frequency of calls, in a specific time window on either side of a crime to provide a focus for the location and period under investigation? Might the call data records of convicted criminals' social networks serve to distinguish criminals from non-criminals? To address these questions, we used heterogeneous call data records dataset by tapping into the power of social network analysis and the advancements in graph convolutional networks. In collaboration with the Punjab Police and Punjab Information Technology Board, these techniques were useful in identifying convicted individuals. The approaches employed are useful in identifying crime suspects and facilitators to support smart policing in the fight against the country's increasing crime rates. Last but not least, the applied methods are highly desirable to complement high-cost video-based smart city surveillance platforms in developing countries. 相似文献
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李宗凡 《湖南广播电视大学学报》2003,(3):83-84
本在分析卷积交织技术的基础上，提出了一种斜线交织技术，并用计算机模拟了乘积码与该技术相结合后对纠错性能的明显提高。 相似文献
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《Information processing & management》2023,60(4):103350
Knowledge graphs are sizeable graph-structured knowledge with both abstract and concrete concepts in the form of entities and relations. Recently, convolutional neural networks have achieved outstanding results for more expressive representations of knowledge graphs. However, existing deep learning-based models exploit semantic information from single-level feature interaction, potentially limiting expressiveness. We propose a knowledge graph embedding model with an attention-based high-low level features interaction convolutional network called ConvHLE to alleviate this issue. This model effectively harvests richer semantic information and generates more expressive representations. Concretely, the multilayer convolutional neural network is utilized to fuse high-low level features. Then, features in fused feature maps interact with other informative neighbors through the criss-cross attention mechanism, which expands the receptive fields and boosts the quality of interactions. Finally, a plausibility score function is proposed for the evaluation of our model. The performance of ConvHLE is experimentally investigated on six benchmark datasets with individual characteristics. Extensive experimental results prove that ConvHLE learns more expressive and discriminative feature representations and has outperformed other state-of-the-art baselines over most metrics when addressing link prediction tasks. Comparing MRR and Hits@1 on FB15K-237, our model outperforms the baseline ConvE by 13.5% and 16.0%, respectively. 相似文献
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Opinion mining in a multilingual and multi-domain environment as YouTube requires models to be robust across domains as well as languages, and not to rely on linguistic resources (e.g. syntactic parsers, POS-taggers, pre-defined dictionaries) which are not always available in many languages. In this work, we i) proposed a convolutional N-gram BiLSTM (CoNBiLSTM) word embedding which represents a word with semantic and contextual information in short and long distance periods; ii) applied CoNBiLSTM word embedding for predicting the type of a comment, its polarity sentiment (positive, neutral or negative) and whether the sentiment is directed toward the product or video; iii) evaluated the efficiency of our model on the SenTube dataset, which contains comments from two domains (i.e. automobile, tablet) and two languages (i.e. English, Italian). According to the experimental results, CoNBiLSTM generally outperforms the approach using SVM with shallow syntactic structures (STRUCT) – the current state-of-the-art sentiment analysis on the SenTube dataset. In addition, our model achieves more robustness across domains than the STRUCT (e.g. 7.47% of the difference in performance between the two domains for our model vs. 18.8% for the STRUCT) 相似文献
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《Information processing & management》2022,59(3):102930
DNS tunneling is a typical attack adopted by cyber-criminals to compromise victims’ devices, steal sensitive data, or perform fraudulent actions against third parties without their knowledge. The fraudulent traffic is encapsulated into DNS queries to evade intrusion detection. Unfortunately, traditional defense systems based on Deep Packet Inspection cannot always detect such traffic. As a result, DNS tunneling is one problem that has worried the cybersecurity community over the past decade.In this paper, we propose a robust and reliable Deep Learning-based DNS tunneling detection approach to mine valuable insight from DNS query payloads. More precisely, several features are first extracted by the DNS flow, and then they are arranged as bi-dimensional images. A Convolutional Neural Network is used to automatically and adaptively learn spatial hierarchies of features to be used in a fully connected neural network for traffic classification. The proposed approach may result in an extremely interesting task in predictive security approaches to attack detection.The effectiveness of the proposal is evaluated in several experiments using a real-world traffic dataset. The obtained results show that our approach achieves 99.99% of accuracy and performs better than state-of-the-art solutions. 相似文献