Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.Comment: Accepted at DTMBIO workshop, CIKM 2017. To appear in BMC Bioinformatics. Pls cite that versio

Learning in Networks: a survey

This paper presents a survey of research on learning with a special focus on the structure of interaction between individual entities. The structure is formally modelled as a network: the nodes of the network are individuals while the arcs admit a variety of interpretations (ranging from information channels to social and economic ties). I first examine the nature of learning about optimal actions for a given network architecture. I then discuss learning about optimal links and actions in evolving networks.

Interaction Processes in Collaborative Learning Networks: A Social Interdependence Perspective

Information systems and communication tools such as online discussions forums are increasingly replacing traditional instructor-led learning methods with collaborative learning networks. Collaborative learning networks emphasize the distributed nature of learning and community-based sharing of knowledge, where people connect and collectively contribute knowledge to a learning community. However, the value realized through collaborative learning depends on social interaction processes that take place among members of a learning network. The aim of this paper is to present our ongoing research on social interaction processes, their determinants, and their effects on individual and group learning performance. We investigate the role of different social interaction processes in collaborative learning networks, where students’ learning is derived from (instead of with) the learning community. As a result, we aim to offer theoretical insights into how collaborative learning networks enhance the learning outcomes of both the individual and group

Learning in Networks: a survey

This paper presents a survey of research on learning with a special focus on the structure of interaction between individual entities. The structure is formally modelled as a network: the nodes of the network are individuals while the arcs admit a variety of interpretations (ranging from information channels to social and economic ties). I first examine the nature of learning about optimal actions for a given network architecture. I then discuss learning about optimal links and actions in evolving networks

An evaluation of social learning networks: a qualitative perspective

Affordances offered through ubiquitous nature of Web 2.0 technologies and social media have progressively become universal constituents of our lives. Presently our students have seen the escalation in use of multimedia in their studies. With technological advances in telecommunication technologies, students have become accustomed to instant, global communications modes. Educational institutions have progressively adapted more innovative pedagogical approaches in their provision. Web 2.0 has fundamentally altered communication methods between people around the world. Access to information, dissemination, sharing and creation of new digitised content are powerful tools that ease social media adaptation in everyone’s life. Over the last decade multimedia authoring tools have become more useful for content generation. The price and expertise to use these authoring tools has decreased, therefore offering opportunity for educators to broaden their experimental horizons with these technologies. With the advent of Web 2.0, access to information, dissemination, sharing and creation of new digitised content are powerful tools that ease social media adaptation in student’s life. Universities have reported reforms in the use of Education 2.0, while Web 2.0 is finding its momentums in further education and schools. Since the advent of Web 2.0 many educational institutions have reported remarkable positive influences in students learning behaviours. Research studies have illustrated association between students improved communication and collaboration linked to improved motivation hence more on going academic performance. Social learning networks represent a more diverse mechanism than a content delivery platform. The potential to release both students and instructors creative talents, ease of content creation and collaboratively sharing teaching and learning resources has enabled educational institutions to explore the strategic benefits of social learning networks. Recent studies indicate that these digital elements when aligned with the best practices of multimedia design become powerful learning agents. This study is aimed at highlighting the importance of social learning networks in education from a qualitative perspective. A series of recent studies at higher and further education has provided guidelines for the improved use of social media in e-learning. This paper’s findings will introduce qualitative verdicts for a framework adaptation of social learning networks in e-learning

Employing Topological Data Analysis On Social Networks Data To Improve Information Diffusion

For the past decade, the number of users on social networks has grown tremendously from thousands in 2004 to billions by the end of 2015. On social networks, users create and propagate billions of pieces of information every day. The data can be in many forms (such as text, images, or videos). Due to the massive usage of social networks and availability of data, the field of social network analysis and mining has attracted many researchers from academia and industry to analyze social network data and explore various research opportunities (including information diffusion and influence measurement). Information diffusion is defined as the way that information is spread on social networks; this can occur due to social influence. Influence is the ability affect others without direct commands. Influence on social networks can be observed through social interactions between users (such as retweet on Twitter, like on Instagram, or favorite on Flickr). In order to improve information diffusion, we measure the influence of users on social networks to predict influential users. The ability to predict the popularity of posts can improve information diffusion as well; posts become popular when they diffuse on social networks. However, measuring influence and predicting posts popularity can be challenging due to unstructured, big, noisy data. Therefore, social network mining and analysis techniques are essential for extracting meaningful information about influential users and popular posts. For measuring the influence of users, we proposed a novel influence measurement that integrates both users’ structural locations and characteristics on social networks, which then can be used to predict influential users on social networks. centrality analysis techniques are adapted to identify the users’ structural locations. Centrality is used to identify the most important nodes within a graph; social networks can be represented as graphs (where nodes represent users and edges represent interactions between users), and centrality analysis can be adopted. The second part of the work focuses on predicting the popularity of images on social networks over time. The effect of social context, image content and early popularity on image popularity using machine learning algorithms are analyzed. A new approach for image content is developed to represent the semantics of an image using its captions, called keyword vector. This approach is based on Word2vec (an unsupervised two-layer neural network that generates distributed numerical vectors to represent words in the vector space to detect similarity) and k-means (a popular clustering algorithm). However, machine learning algorithms do not address issues arising from the nature of social network data, noise and high dimensionality in data. Therefore, topological data analysis is adopted. It is a noble approach to extract meaningful information from high-dimensional data and is robust to noise. It is based on topology, which aims to study the geometric shape of data. In this thesis, we explore the feasibility of topological data analysis for mining social network data by addressing the problem of image popularity. The proposed techniques are employed to datasets crawled from real-world social networks to examine the performance of each approach. The results for predicting the influential users outperforms existing measurements in terms of correlation. As for predicting the popularity of images on social networks, the results indicate that the proposed features provides a promising opportunity and exceeds the related work in terms of accuracy. Further exploration of these research topics can be used for a variety of real-world applications (including improving viral marketing, public awareness, political standings and charity work)

Empirical evaluation of different feature representations for social circles detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_4Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. We propose in this paper an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks. We compare our results with several different baselines.This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), fundedby the US Army Research Office (ARO).Alonso, J.; Paredes Palacios, R.; Rosso, P. (2015). Empirical evaluation of different feature representations for social circles detection. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 31-38. https://doi.org/10.1007/978-3-319-19390-8_4S3138Buhmann, J., Kühnel, H.: Vector quantization with complexity costs. IEEE Trans. Inf. Theor. 39(4), 1133–1145 (1993)Dey, K., Bandyopadhyay, S.: An empirical investigation of like-mindedness of topically related social communities on microblogging platforms. In: International Conference on Natural Languages (2013)Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)Frank, M., Streich, A.P., Basin, D., Buhmann, J.M.: Multi-assignment clustering for boolean data. J. Mach. Learn. Res. 13(1), 459–489 (2012)Kaggle: Learning social circles in networks. http://www.kaggle.com/c/learning-social-circlesMcAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. Adv. Neural Inf. Process. Syst. 25, 539–547 (2012)McAuley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 4 (2014)Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)Pathak, N., DeLong, C., Banerjee, A., Erickson, K.: Social topic models for community extraction. In: The 2nd SNA-KDD Workshop (2008)Porter, M.A., Onnela, J.P., Mucha, P.J.: Communities in networks. Not. Amer. Math. Soc. 56(9), 1082–1097 (2009)Rose, K., Gurewitz, E., Fox, G.C.: Vector quantization by deterministic annealing. IEEE Transactions on Information Theory 38(4), 1249–1257 (1992)Sachan, M., Contractor, D., Faruquie, T.A., Subramaniam, L.V.: Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 331–340 (2012)Streich, A.P., Frank, M., Basin, D., Buhmann, J.M.: Multi-assignment clustering for Boolean data. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 969–976 (2009)Vaidya, J., Atluri, V., Guo, Q.: The role mining problem: finding a minimal descriptive set of roles. In: Proceedings of the 12th ACM Symposium on Access Control Models and Technologies, pp. 175–184 (2007)Zhou, D., Councill, I., Zha, H., Giles, C.L.: Discovering temporal communities from social network documents. In: Seventh IEEE International Conference on Data Mining, PP. 745–750 (2007