Response Retrieval in Information-seeking Conversations

The increasing popularity of mobile Internet has led to several crucial changes in the way that people use search engines compared with traditional Web search on desktops. On one hand, there is limited output bandwidth with the small screen sizes of most mobile devices. Mobile Internet users prefer direct answers on the search engine result page (SERP). On the other hand, voice-based / text-based conversational interfaces are becoming increasing popular as shown in the wide adoption of intelligent assistant services and devices such as Amazon Echo, Microsoft Cortana and Google Assistant around the world. These important changes have triggered several new challenges that search engines have had to adapt to in order to better satisfy the information needs of mobile Internet users. In this dissertation, we investigate several aspects of single-turn answer retrieval and multi-turn information-seeking conversations to handle the new challenges of search on the mobile Internet. We start from the research on single-turn answer retrieval and analyze the weaknesses of existing deep learning architectures for answer ranking. Then we propose an attention based neural matching model with a value-shared weighting scheme and attention mechanism to improve existing deep neural answer ranking models. Our proposed model achieves state-of-the-art performance for answer sentence retrieval compared with both feature engineering based methods and other neural models. Then we move on to study response retrieval in multi-turn information-seeking conversations beyond single-turn interactions. Much research on response selection in conversation systems is modeling the matching patterns between user input message (either with context or not) and response candidates, which ignores external knowledge beyond the dialog utterances. We propose a learning framework on top of deep neural matching networks that leverages external knowledge with pseudo-relevance feedback and QA correspondence knowledge distillation for response retrieval. We also study how to integrate user intent modeling into neural ranking models to improve response retrieval performance. Finally, hybrid models of response retrieval and generation are investigated in order to combine the merits of these two different paradigms of conversation models. Our goal is to develop effective learning models for answer retrieval and information-seeking conversations, in order to improve the effectiveness and user experience when accessing information with a touch screen interface or a conversational interface, as commonly adopted by millions of mobile Internet devices

Linking named entities to Wikipedia

Natural language is fraught with problems of ambiguity, including name reference. A name in text can refer to multiple entities just as an entity can be known by different names. This thesis examines how a mention in text can be linked to an external knowledge base (KB), in our case, Wikipedia. The named entity linking (NEL) task requires systems to identify the KB entry, or Wikipedia article, that a mention refers to; or, if the KB does not contain the correct entry, return NIL. Entity linking systems can be complex and we present a framework for analysing their different components, which we use to analyse three seminal systems which are evaluated on a common dataset and we show the importance of precise search for linking. The Text Analysis Conference (TAC) is a major venue for NEL research. We report on our submissions to the entity linking shared task in 2010, 2011 and 2012. The information required to disambiguate entities is often found in the text, close to the mention. We explore apposition, a common way for authors to provide information about entities. We model syntactic and semantic restrictions with a joint model that achieves state-of-the-art apposition extraction performance. We generalise from apposition to examine local descriptions specified close to the mention. We add local description to our state-of-the-art linker by using patterns to extract the descriptions and matching against this restricted context. Not only does this make for a more precise match, we are also able to model failure to match. Local descriptions help disambiguate entities, further improving our state-of-the-art linker. The work in this thesis seeks to link textual entity mentions to knowledge bases. Linking is important for any task where external world knowledge is used and resolving ambiguity is fundamental to advancing research into these problems

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations

Grammar and Corpora 2016

In recent years, the availability of large annotated corpora, together with a new interest in the empirical foundation and validation of linguistic theory and description, has sparked a surge of novel work using corpus methods to study the grammar of natural languages. This volume presents recent developments and advances, firstly, in corpus-oriented grammar research with a special focus on Germanic, Slavic, and Romance languages and, secondly, in corpus linguistic methodology as well as the application of corpus methods to grammar-related fields. The volume results from the sixth international conference Grammar and Corpora (GaC 2016), which took place at the Institute for the German Language (IDS) in Mannheim, Germany, in November 2016.Die Verfügbarkeit großer annotierter und durchsuchbarer Korpora, verbunden mit einem neuerwachten Interesse an der empirischen Grundlegung und Validierung linguistischer Theorie und Beschreibung hat in letzter Zeit zu einer regelrechten Welle interessanter Arbeiten zur Grammatik natürlicher Sprachen geführt. Dieser Band präsentiert zum einen neuere Entwicklungen in der korpusorientierten Forschung zur Grammatik germanischer, romanischer und slawischer Sprachen und zum anderen innovative Ansätze in der einschlägigen korpuslinguistischen Methodologie, die auch Anwendung im Umfeld der Grammatik finden. Der Band fasst die Beiträge der sechsten internationalen Konferenz Grammar and Corpora zusammen, die im November 2016 am Institut für Deutsche Sprache (IDS) in Mannheim stattfand

Drug development progress in duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a severe, progressive, and incurable X-linked disorder caused by mutations in the dystrophin gene. Patients with DMD have an absence of functional dystrophin protein, which results in chronic damage of muscle fibers during contraction, thus leading to deterioration of muscle quality and loss of muscle mass over time. Although there is currently no cure for DMD, improvements in treatment care and management could delay disease progression and improve quality of life, thereby prolonging life expectancy for these patients. Furthermore, active research efforts are ongoing to develop therapeutic strategies that target dystrophin deficiency, such as gene replacement therapies, exon skipping, and readthrough therapy, as well as strategies that target secondary pathology of DMD, such as novel anti-inflammatory compounds, myostatin inhibitors, and cardioprotective compounds. Furthermore, longitudinal modeling approaches have been used to characterize the progression of MRI and functional endpoints for predictive purposes to inform Go/No Go decisions in drug development. This review showcases approved drugs or drug candidates along their development paths and also provides information on primary endpoints and enrollment size of Ph2/3 and Ph3 trials in the DMD space