Showing papers by "Sarvnaz Karimi published in 2014"

Pinpointing Locational Focus in Microblogs

Abstract: Extracting the geographical location that a tweet is about is crucial for many important applications ranging from disaster management to recommendation systems. We address the problem of finding the locational focus of tweets that is geographically identifiable on a map. Because of the short, noisy nature of tweets and inherent ambiguity of locations, tweet text alone cannot provide sufficient information for disambiguating the location mentions and inferring the actual location focus being referred to in a tweet. Therefore, we present a novel algorithm that identifies all location mentions from three information sources---tweet text, hashtags, and user profile---and then uses a gazetteer database to infer the most probable locational focus of a tweet. Our novel algorithm has the ability to infer a locational focus that may not be explicitly mentioned in the tweet and determine its most appropriate granularity, e.g., city or country.

Evaluation of text-processing algorithms for adverse drug event extraction from social media

Abstract: The discovery of suspected adverse drug reactions is no longer restricted to mining reports that pharmaceutical companies and health professionals send to regulators for possible safety signals. Patient forums and other social media are being studied for additional sources of information to assist in expediting adverse reaction discovery. Extracting information on drugs, adverse drug reactions, diseases and symptoms, or patient demographics from such media is an essential step of this process, but it is not straightforward. While most studies in this area use a lexicon-based information extraction methodology, they do not explicitly evaluate the impact of text-processing steps on their final results. We experimentally quantify the value of the most popular techniques to establish whether or not they benefit the information extraction process.

Overview of the 2014 ALTA Shared Task: Identifying Expressions of Locations in Tweets

Abstract: This year was the fifth in the ALTA series of shared tasks. The topic of the 2014 ALTA shared task was to identify location information in tweets. As in past competitions, we used Kaggle in Class as the framework for submission, evaluation and communication with the participants. In this paper we describe the details of the shared task, evaluation method, and results of the participating systems.

A Study of Querying Behaviour of Expert and Non-expert Users of Biomedical Search Systems

Abstract: The amount of biomedical literature, and the popularity of health-related searches, are both growing rapidly. While most biomedical search systems offer a range of advanced features, there is limited understanding of user preferences, and how searcher expertise relates to the use and perception of different search features in this domain. Through a controlled user study where both medical experts and non-medical participants were asked to carry-out informational searches in a task-based environment, we seek to understand how querying behaviour differs, both in the formulation of query strings, and in the use of advanced querying features. Our results suggest that preferences vary substantially between these groups of users, and that biomedical search systems need to offer a range of tools in order to effectively support both types of searchers.

Differences between social media and regulatory databases in adverse drug reaction discovery

Abstract: Information extraction from social media for a variety of applications, such as collecting people opinion about a product or a political party, has been widely studied and justified. Extracting information for health related applications however is less justified especially because of sensitivity of health issues, difficulty in establishing the value and trust in lay people to judge their health problems. Using social media to discover adverse drug reactions is one of the most controversial topics. It is difficult to establish the causality between an adverse drug reaction and a drug when the context information such as patient condition is missing. We compare official reports of adverse drug reactions with reports on medical forums related to two different drugs to discuss the potential and challenges in this research area.