Sentiment analysis

With the advent of the Internet, we are seeing more sophisticated techniques being developed to support e-Iearning. The rapid developme nt of Web-based learning and new concepts like virtual classrooms, virtual laboratories and virtual universities introduces many new issues to be addressed. On the technical side, we need to develop effective e-technologies for supporting distance education. On the learning and management side, we need to consider issues such as new style of learning and different system set-u p requirements. Finally, the issue of standardization of e-Iearning systems should also be considered. In this special issue, our focus will be on the technical side, although other issues related to knowledge and data engineering for e-Iearning may also be considered. Topics: In this special issue, we call for original papers describing novel knowledge and data engineering techniques that support e-Iearning. Preference will be given to papers that include an evaluation of users' experience in using the proposed methods. Areas of interests include, but are not limited to: • Semantic Web technology for e-Iearning • Data modeling (eg., XML) for efficient management of course materials • Searching and indexing techniques to suppo rt effective course notes retrieval • User-centric e-Iearning systems and user interaction management • Profiling techniques to support grading and learning recommendation • Data and knowledge base suppo rt for pervasive e-Iearning • Course material analysis and understanding • Automatic generation of questions and answers • Collaborative communities for e-Iearning

Knowledge and Data Engineering for e-Learning Special Issue of IEEE Transactions on Knowledge and Data Engineering

An action is performed in a spoken dialog system in response to a user's spoken utterance. A policy which maps belief states of user intent to actions is retrieved or created. A belief state is determined based on the spoken utterance, and an action is selected based on the determined belief state and the policy. The action is performed, and in one embodiment, involves requesting clarification of the spoken utterance from the user. Creating a policy may involve simulating user inputs and spoken dialog system interactions, and modifying policy parameters iteratively until a policy threshold is satisfied. In one embodiment, a belief state is determined by converting the spoken utterance into text, assigning the text to one or more dialog slots associated with nodes in a probabilistic ontology tree (POT), and determining a joint probability based on probability distribution tables in the POT and on the dialog slot assignments.

Belief tracking and action selection in spoken dialog systems

A blog emotion corpus for emotional expression analysis in Chinese

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of groundbreaking applications in chemistry, neurology, electronics, or communication networks, among others. At the current stage of research, however, the efficient processing of GNNs is still an open challenge for several reasons. Besides of their novelty, GNNs are hard to compute due to their dependence on the input graph, their combination of dense and very sparse operations, or the need to scale to huge graphs in some applications. In this context, this paper aims to make two main contributions. On the one hand, a review of the field of GNNs is presented from the perspective of computing. This includes a brief tutorial on the GNN fundamentals, an overview of the evolution of the field in the last decade, and a summary of operations carried out in the multiple phases of different GNN algorithm variants. On the other hand, an in-depth analysis of current software and hardware acceleration schemes is provided, from which a hardware-software, graph-aware, and communication-centric vision for GNN accelerators is distilled.

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

A lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relations from the procedural text has been actively explored. Prior work mostly has focused on finding relationships among verb-noun pairs or clustering of extracted pairs. In this paper, we investigate the problem of learning individual procedure-specific relationships (e.g. is method of, is alternative of, or is subtask of) among sentences. To identify the relationships, we propose an end-to-end neural network architecture, which can selectively learn important procedure-specific relationships. Using this approach, we could construct a how-to knowledge base from the largest procedure sharing-community, wiki-how.com. The evaluation of our approach shows that it outperforms the existing entity relationship extraction algorithms.

Learning Procedures from Text: Codifying How-to Procedures in Deep Neural Networks

Node classification is an important problem in relational machine learning. However, in scenarios where graph edges represent interactions among the entities (e.g., over time), the majority of current methods either summarize the interaction information into link weights or aggregate the links to produce a static graph. In this paper, we propose a neural network architecture that jointly captures both temporal and static interaction patterns, which we call Temporal-Static-Graph-Net (TSGNet). Our key insight is that leveraging both a static neighbor encoder, which can learn aggregate neighbor patterns, and a graph neural network-based recurrent unit, which can capture complex interaction patterns, improve the performance of node classification. In our experiments on node classification tasks, TSGNet produces significant gains compared to state-of-the-art methods—reducing classification error up to 24% and an average of 10% compared to the best competitor on four real-world networks and one synthetic dataset.

/pdf/exploiting-interaction-links-for-node-classification-with-2p2h2ooxki.pdf

Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks.

As an effort to detect the mood of a blog, regardless of the length and writing style, we propose a hybrid approach to detecting blog text's mood, which incorporates commonsense knowledge obtained from the general public (ConceptNet) and the Affective Norms English Words (ANEW) list. Our approach picks up blog text's unique features and compute simple statistics such as term frequency, n-gram, and point-wise mutual information (PMI) for the SVM classification method. In addition, to catch mood transitions in a given blog text, we developed a paragraph-level segmentation based on a mood flow analysis using a revised version of the GuessMood operation of ConceptNet and an ANEW-based affective sensing module. For evaluation, a mood corpus comprised of real blog texts has been built semi-automatically. Our experiments using the corpus show meaningful results for 4 mood types: happy, sad, angry, and fear.

http://www.hogunpark.com/papers/PRICAI2006_LNAI4099.pdf

A hybrid mood classification approach for blog text

Provided are a system capable of providing a highlight of a real-time event using message streams of social media and a highlight service providing method using the same. The disclosed highlight providing system includes: a hot event session detection unit detecting a hot event session where a hot event occurs using message streams of social media; a keyword detection unit detecting keywords related with the hot event from the hot event session detected by the hot event session detection unit using a predetermined algorithm; a hot event generating unit generating the hot event using a semantic relation between the keywords detected by the keyword detection unit; and a highlight providing unit providing a highlight of the social media along with a description in a natural language based on the hot event generated by the hot event generating unit.

Highlight providing system and method based on hot topic event detection

In this paper, we propose a real-time panoramic video streaming system with overlaid interface concept for social media. The proposed system provides real-time panorama images for smart displays such as smart TVs, smart phones, and tablet PCs. Panorama images are collected at sporting events via panorama cameras. Contents thus collected are sent to servers and then provided to smart devices via live sports video streams. Users select a panorama camera and choose their viewing angle and zooming factor for the selected panorama camera. The proposed system provides immersive and realistic views of live sporting events on users' displays. Furthermore, an overlaid panoramic interface concept is proposed for immersive live baseball watching combined with tightly integrated social media experience.

http://hogunpark.com/papers/panorama-overlay-dseo-2014.pdf

Hogun Park

Papers

Learning Procedures from Text: Codifying How-to Procedures in Deep Neural Networks

Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks.

A hybrid mood classification approach for blog text

Highlight providing system and method based on hot topic event detection

Real-time panoramic video streaming system with overlaid interface concept for social media