MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-based agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG, on the other hand, achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc

Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition

We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.Comment: 4 page

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Efficient Correlated Topic Modeling with Topic Embedding

Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.Comment: KDD 2017 oral. The first two authors contributed equall

The effectiveness of laceback ligatures during initial orthodontic alignment: a systematic review and meta-analysis

Lacebacks may be used to limit unwanted incisor proclination during initial orthodontic alignment; however, their use has not met with universal approval. This systematic review aims to appraise the evidence in relation to the effectiveness of lacebacks in controlling incisor position during initial alignment. Electronic database searches of published literature (MEDLINE via Ovid, Cochrane Central Register of Controlled Trials, LILACS, and IBECS) and unpublished literature were performed. Search terms used included randomized controlled trial, controlled clinical trial, random allocation, double blind method, orthodontics, and laceback. Data were extracted using custom forms. Risk of bias assessment was made using the Cochrane Collaboration risk of bias tool. The quality of the evidence was also assessed using GRADE. Mean differences in incisor inclination and antero-posterior changes in incisor and molar position during alignment were calculated. Two studies involving 97 participants were found to be at low risk of bias and were included in the quantitative synthesis. The random effects meta-analysis demonstrated that the use of lacebacks was associated with 0.5 mm greater posterior movement of the incisors during alignment; this finding was of limited clinical importance and statistically non-significant [95 per cent confidence interval (CI): −1.25, 0.25, P = 0.19]. Little difference (0.46 mm) was also found between laceback and non-laceback groups with regards to mesial molar movement (95 per cent CI: −0.33, 1.24, P = 0.26). According to the GRADE assessment, the overall quality of evidence relating to the use of lacebacks was high. There is no evidence to support the use of lacebacks for the control of the sagittal position of the incisors during initial orthodontic alignmen