Slot Filling

Slot filling (SF) is the task of automatically extracting facts about particular entities from unstructured text, and populating a knowledge base (KB) with these facts. These structured KBs enable applications such as structured web queries and question answering. SF is typically framed as a query-oriented setting of the related task of relation extraction. Throughout this thesis, we reflect on how SF is a task with many distinct problems. We demonstrate that recall is a major limiter on SF system performance. We contribute an analysis of typical SF recall loss, and find a substantial amount of loss occurs early in the SF pipeline. We confirm that accurate NER and coreference resolution are required for high-recall SF. We measure upper bounds using a naïve graph-based semi-supervised bootstrapping technique, and find that only 39% of results are reachable using a typical feature space. We expect that this graph-based technique will be directly useful for extraction, and this leads us to frame SF as a label propagation task. We focus on a detailed graph representation of the task which reflects the behaviour and assumptions we want to model based on our analysis, including modifying the label propagation process to model multiple types of label interaction. Analysing the graph, we find that a large number of errors occur in very close proximity to training data, and identify that this is of major concern for propagation. While there are some conflicts caused by a lack of sufficient disambiguating context—we explore adding additional contextual features to address this—many of these conflicts are caused by subtle annotation problems. We find that lack of a standard for how explicit expressions of relations must be in text makes consistent annotation difficult. Using a strict definition of explicitness results in 20% of correct annotations being removed from a standard dataset. We contribute several annotation-driven analyses of this problem, exploring the definition of slots and the effect of the lack of a concrete definition of explicitness: annotation schema do not detail how explicit expressions of relations need to be, and there is large scope for disagreement between annotators. Additionally, applications may require relatively strict or relaxed evidence for extractions, but this is not considered in annotation tasks. We demonstrate that annotators frequently disagree on instances, dependent on differences in annotator world knowledge and thresholds on making probabilistic inference. SF is fundamental to enabling many knowledge-based applications, and this work motivates modelling and evaluating SF to better target these tasks

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%

The LatMix summer campaign : submesoscale stirring in the upper ocean

Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 96 (2015): 1257–1279, doi:10.1175/BAMS-D-14-00015.1.Lateral stirring is a basic oceanographic phenomenon affecting the distribution of physical, chemical, and biological fields. Eddy stirring at scales on the order of 100 km (the mesoscale) is fairly well understood and explicitly represented in modern eddy-resolving numerical models of global ocean circulation. The same cannot be said for smaller-scale stirring processes. Here, the authors describe a major oceanographic field experiment aimed at observing and understanding the processes responsible for stirring at scales of 0.1–10 km. Stirring processes of varying intensity were studied in the Sargasso Sea eddy field approximately 250 km southeast of Cape Hatteras. Lateral variability of water-mass properties, the distribution of microscale turbulence, and the evolution of several patches of inert dye were studied with an array of shipboard, autonomous, and airborne instruments. Observations were made at two sites, characterized by weak and moderate background mesoscale straining, to contrast different regimes of lateral stirring. Analyses to date suggest that, in both cases, the lateral dispersion of natural and deliberately released tracers was O(1) m2 s–1 as found elsewhere, which is faster than might be expected from traditional shear dispersion by persistent mesoscale flow and linear internal waves. These findings point to the possible importance of kilometer-scale stirring by submesoscale eddies and nonlinear internal-wave processes or the need to modify the traditional shear-dispersion paradigm to include higher-order effects. A unique aspect of the Scalable Lateral Mixing and Coherent Turbulence (LatMix) field experiment is the combination of direct measurements of dye dispersion with the concurrent multiscale hydrographic and turbulence observations, enabling evaluation of the underlying mechanisms responsible for the observed dispersion at a new level.The bulk of this work was funded under the Scalable Lateral Mixing and Coherent Turbulence Departmental Research Initiative and the Physical Oceanography Program. The dye experiments were supported jointly by the Office of Naval Research and the National Science Foundation Physical Oceanography Program (Grants OCE-0751653 and OCE-0751734).2016-02-0

How are veterans perceived today?

There is always a considerable debate in the UK and the US about the health of military veterans. Data from representative opinion polls and surveys conducted in the last three decades highlight a persistent set of beliefs about military veterans, held by members of UK and US societies. The majority of British and American respondents think that veterans are more likely to experience disability, ill mental-health, unemployment and homelessness than members of the civilian population. Some of these negative perceptions are accurate for US veterans, since, compared to civilians, they are statistically more likely to struggle with physical and mental health issues and be homeless. However, such perceptions are erroneous for the majority of British veterans

'My teeth don't chew on shrapnel': an anthology of poetry by military veterans

This anthology, created by Oxford Brookes Poetry Centre, features poetry written by participants and associates of the Oxford Brookes Veterans' Poetry Workshop, 2019-20. Nine poets are represted here: Jo Young, Tom Laaser, Claire Hughes, Noel Harrower, John Thampi, Jamie Broady, Andrew Fassett, Stewart Hill, and Maggs Vibo. The anthology also includes an introduction by Niall Munro, an explanation of the workshop process by Susie Campbell, an essay about women veteran writers by Jane Potter, and reflections on her research into the perceptions of veterans in the UK and the US by Rita Phillips. Susie Campbell has also provided helpful writing prompts linked to the poems for those readers who are - or would like to be - writers. In addition to the four versions of the anthology, this package of files also includes transcripts of each of the interviews conducted by Dr Niall Munro with ten workshop participants, not all of whom appear in the anthology: Eugene Ratz, John Thampi, J. Robin Whitely, Jo Young (two interviews), Andrew Fassett, Tom Laaser, Stewart Hill, Claire Hughes, Maggs Vibo, and Jamie Broady. The transcriptions were completed by Dr Hester Bradley

Urban Modernity in the Contemporary Gulf

Urban Modernity in the Contemporary Gulf offers a timely and engaging discussion on architectural production in the modernization era in the Arabian Peninsula. Focusing on the 20th century as a starting point, the book explores the display of transnational architectural practices resulting in different notions of locality, cosmopolitanism, and modernity. Contextually, with an eye on the present, the book reflects on the initiatives that recently re-engaged with the once ville moderne which, meanwhile, lost its pivotal function and meaning. A city within a bigger city, the urban fabric produced during the modernization era has the potential to narrate the social growth, East–West dynamics, and citizens’ memories of the recent past. Reading obsolescence as an opportunity, the book looks into this topic from a cross-country perspective. It maps, reads and analyses the notion of modern heritage in relation to the contemporary city and looks beyond physical transformations to embrace cultural practices and strategies of urban re-appropriation. It interrogates the value of modern architecture in the non-West, examining how academic research is expanding the debate on Gulf urbanism, and describes how practices of reuse could foster rethinking neglected areas, also addressing land consumption in the GCC. Presenting a diverse and geographically inclusive authorship, which combines established and up-and-coming researchers in the field, this is an important reference for academics and upper-level students interested in heritage studies, post-colonial urbanism, and architecture in the non-West