The UTAustin team participated in two main tasks this year the Cold Start Slot Filling (CSSF) task and the Slot-Filler Validation/Ensembling task, which was divided into the filtering and ensembling subtasks. Our system uses stacking to ensemble multiple systems for the KBP slot filling task, as described in our ACL 2015 paper. We expand the stacking approach by allowing the classifier to also ...

In the last decade, climate change has become one of the major environmental issues. The reduction of greenhouse gas emission as the main contributor in climate change is now more and more critical. Therefore, precise and reliable emission estimation models are necessary to help evaluate the impacts of future projects, strategies, and policies. This increasing pressure has made the emission mod...

Online environments offer a major advantage that data can be accessed freely. At the same time however, they present us with an issue of trust: how much any data from online sites can be trusted. Trust and Reputation Systems (TRS), developed to address this issue of trust on network, quantify reliability in terms of semantics and derive a trustnetwork from a targeted online data. The performanc...

Tian Qiu , Zi-Ke Zhang , and Guang Chen 1 1 School of Information Engineering, Nanchang Hangkong University, Nanchang, 330063, P.R. China 2 Institute of Information Economy, Hangzhou Normal University Hangzhou 310036, P. R. China 3 Web Sciences Center, University of Electronic Science and Technology of China Chengdu 610054, P.R. China 4 Beijing Computational Science Research Center, Beijing 100...

In this paper, Collaborative Profile Space analysis, a novel approach based on HMM, is proposed to tackle the “new item” cold start problem in music recommendation task. By calculating the probability of generating a particular song from a set of trained HMMs, coordinates in the Collaborative Profile Space is defined, and used in the classifiers. With self-collected dataset and evaluation strat...

Since the rise of collaborative tagging systems on the web, the tag recommendation task – suggesting suitable tags to users of such systems while they add resources to their collection – has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previ...

In this paper we discuss our approach to the task of Cold-Start Knowledge Base Population and the challenges associated with it. We describe our knowledge base system Lorify and each of the components necessary to populate it from unstructured text. The pivotal component for building a large-scale knowledge base is scalable cross-document coreference. We address this with a novel clustering alg...

In this demo, we showcase a set up wizard designed to bypass the cold start problem that often affects recommendation systems in the event domain. We have developed a mobile application for tourists, RelEVENT, which allows them to quickly and non-intrusively set up preferences and/or interests related to events. This will directly affect the degree to which they can receive personalized recomme...

Combining social network information with collaborative filtering recommendation algorithms has helped to alleviate some drawbacks of collaborative filtering, for example, the cold start problem, and has increased the accuracy of recommendations. However, the user coverage of recommendation for social-based recommendation is low as there is often insufficient data about explicit social relation...

In this work, we apply a clustering technique to integrate the contents of items into the item-based collaborative filtering framework. The group rating information that is obtained from the clustering result provides a way to introduce content information into collaborative recommendation and solves the cold start problem. Extensive experiments have been conducted on MovieLens data to analyze ...