Context-Based Quotation Recommendation

While composing a new document, anything from a news article to an email or essay, authors often utilize direct quotes from a variety of sources. Although an author may know what point they would like to make, selecting an appropriate quote for the specific context may be time-consuming and difficult. We therefore propose a novel context-aware quote recommendation system which utilizes the content an author has already written to generate a ranked list of quotable paragraphs and spans of tokens from a given source document. We approach quote recommendation as a variant of open-domain question answering and adapt the state-of-the-art BERT-based methods from open-QA to our task. We conduct experiments on a collection of speech transcripts and associated news articles, evaluating models' paragraph ranking and span prediction performances. Our experiments confirm the strong performance of BERT-based methods on this task, which outperform bag-of-words and neural ranking baselines by more than 30% relative across all ranking metrics. Qualitative analyses show the difficulty of the paragraph and span recommendation tasks and confirm the quotability of the best BERT model's predictions, even if they are not the true selected quotes from the original news articles.Comment: 12 pages, 3 figure

Evaluation of range of motion restriction within the hip joint

In Total Hip Arthroplasty, determining the impingement free range of motion requirement is a complex task. This is because in the native hip, motion is restricted by both impingement as well as soft tissue restraint. The aim of this study is to determine a range of motion benchmark which can identify motions which are at risk from impingement and those which are constrained due to soft tissue. Two experimental methodologies were used to determine motions which were limited by impingement and those motions which were limited by both impingement and soft tissue restraint. By comparing these two experimental results, motions which were limited by impingement were able to be separated from those motions which were limited by soft tissue restraint. The results show motions in extension as well as flexion combined with adduction are limited by soft tissue restraint. Motions in flexion, flexion combined with abduction and adduction are at risk from osseous impingement. Consequently, these motions represent where the maximum likely damage will occur in femoroacetabular impingement or at most risk of prosthetic impingement in Total Hip Arthroplasty

C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users’ satisfaction