Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201

Towards the semantic interpretation of personal health messages from social media

Recent attempts have been made to utilise social media platforms, such as Twitter, to provide early warning and monitoring of health threats in populations (i.e. Internet biosurveillance). It has been shown in the literature that a system based on keyword matching that exploits social media messages could report flu surveillance well ahead of the Centers of Disease Control and Prevention (CDC). However, we argue that a simple keyword matching may not capture semantic interpretation of social media messages that would enable healthcare experts or machines to extract and leverage medical knowledge from social media messages. In this paper, we motivate and describe a new task that aims to tackle this technology gap by extracting semantic interpretation of medical terms mentioned in social media messages, which are typically written in layman’s language. Achieving such a task would enable an automatic integration between the data about direct patient experiences extracted from social media and existing knowledge from clinical databases, which leads to advances in the use of community health experiences in healthcare services.The authors gratefully acknowledge funding from the EPSRC (grant number EP/M005089/1)This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2811271.281127

Financial Characteristics of Companies Audited by Large Audit Firms

Purpose “ The purpose of this paper is to examine how financial characteristics associated with the choice of a big audit firm with further investigation on the agency costs of free cash flows.Design/methodology/approach “ The sample used for this work includes industrial listed companies from Germany and France. To test our hypothesis, we used a number of logit models, extending the standard model selection audit firm, to include the variables of interest. Following previous work, our dependent dummy variable is Big4 or non-Big4.Findings “ We observed that most independent variables in the German companies show similar results to previous work, but we did not have the same results for the French industry. Moreover, our findings suggest that the total debt and dividends can be an important reason for determining the choice of a large audit firm, reducing agency costs of free cash flows.Research limitations/implications “ This study has some limitations on the measurements of the cost of the audit fees and also generates opportunities for additional searching.Originality/value “ The paper provides only one aspect to explain the relationship between the problems of agency costs of free cash flow and influence in choosing a large auditing firm, which stems from investors\u27 demand for higher quality audits