Search results for: K. Tharini

Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

Abstract:

Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

Procedia PDF Downloads 683

Authors: V. K. Stalin, M. Kirithika, K. Shanmugam, K. Tharini

Abstract:

Nanotechnology has been widely used in many applications such as medical, electronics, robotics and also in geotechnical engineering area through stabilization of bore holes, grouting etc. In this paper, an attempt is made for understanding the influence of nano copper slag (1%, 2% & 3%) on the index, compaction and UCC strength properties of natural soil (CH type) with and without lime stabilization for immediate and 7 days curing period. Results indicated that upto 1% of Nano copper slag, there is an increment in UC strength of virgin soil and lime stabilised soil. Beyond 1% nano copper slag, there is a steep reduction in UC strength and increase of plasticity both in lime stabilised soil and virgin soil. The effect of lime is found to show more influence on large surface area of nano copper slag in natural soil. For both immediate and curing effect, with 1% of Nano copper slag, the maximum unconfined compressive strength was 38% and 106% higher than that of the virgin soil strength.

Keywords: lime, nano copper slag, SEM, XRD, stabilisation

Procedia PDF Downloads 379

Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

Abstract:

In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: evolving learning, knowledge extraction, knowledge graph, text mining

Procedia PDF Downloads 433