A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

Multilingual Animacy Classification by Sparse Logistic Regression

This paper presents results from three experiments on automatic animacy classification in Japanese and English. We present experiments that focus on solutions to the problem of reliably classifying a large set of infrequent items using a small number of automatically extracted features. We labeled a set of Japanese nouns as ±animate on the basis of reliable, surface-obvious morphological features, producing an accurately but sparsely labeled data set. To classify these nouns, and to achieve good generalization to other nouns for which we do not have labels, we used feature vectors based on frequency counts of verbargument relations that abstract away from item identity and into class-wide distributional tendencies of the feature set. Grouping items into suffix-based equivalence classes prior to classification increased data coverage and improved classification accuracy. For the items that occur at least once with our feature set, we obtained 95% classification accuracy. We used loanwords to transfer automatically acquired labels from English to classify items that are zerofrequency in the Japanese data set, giving increased precision on inanimate items and increased recall on animate items

Health economics of targeted intraoperative radiotherapy (TARGIT-IORT) for early breast cancer: a cost-effectiveness analysis in the United Kingdom

Objective: The clinical effectiveness of targeted intraoperative radiotherapy (TARGIT-IORT) has been confirmed in the randomised TARGIT-A (targeted intraoperative radiotherapy-alone) trial to be similar to a several weeks’ course of whole-breast external-beam radiation therapy (EBRT) in patients with early breast cancer. This study aims to determine the cost effectiveness of TARGIT-IORT to inform policy decisions about its wider implementation. Setting TARGIT-A randomised clinical trial (ISRCTN34086741) which compared TARGIT with traditional EBRT and found similar breast cancer control, particularly when TARGIT was given simultaneously with lumpectomy. Methods: Cost-utility analysis using decision analytic modelling by a Markov model. A cost-effectiveness Markov model was developed using TreeAge Pro V.2015. The decision analytic model compared two strategies of radiotherapy for breast cancer in a hypothetical cohort of patients with early breast cancer based on the published health state transition probability data from the TARGIT-A trial. Analysis was performed for UK setting and National Health Service (NHS) healthcare payer’s perspective using NHS cost data and treatment outcomes were simulated for both strategies for a time horizon of 10 years. Model health state utilities were drawn from the published literature. Future costs and effects were discounted at the rate of 3.5%. To address uncertainty, one-way and probabilistic sensitivity analyses were performed. Main outcome measures: Quality-adjusted life-years (QALYs). Results: In the base case analysis, TARGIT-IORT was a highly cost-effective strategy yielding health gain at a lower cost than its comparator EBRT. Discounted TARGITIORT and EBRT costs for the time horizon of 10 years were £12 455 and £13 280, respectively. TARGIT-IORT gained 0.18 incremental QALY as the discounted QALYs gained by TARGIT-IORT were 8.15 and by EBRT were 7.97 showing TARGIT-IORT as a dominant strategy over EBRT. Model outputs were robust to one-way and probabilistic sensitivity analyses. Conclusions: TARGIT-IORT is a dominant strategy over EBRT, being less costly and producing higher QALY gain

Towards Effective Tutorial Feedback for Explanation Questions: A Dataset and Baselines

This paper proposes a new shared task on grading student answers with the goal of enabling well-targeted and flexible feedback in a tutorial dialogue setting

Storageless and caching Tier-2 models in the UK context

Operational and other pressures have lead to WLCG experiments moving increasingly to a stratified model for Tier-2 resources, where ``fat" Tier-2s (``T2Ds") and ``thin" Tier-2s (``T2Cs") provide different levels of service. In the UK, this distinction is also encouraged by the terms of the current GridPP5 funding model. In anticipation of this, testing has been performed on the implications, and potential implementation, of such a distinction in our resources. In particular, this presentation presents the results of testing of storage T2Cs, where the ``thin" nature is expressed by the site having either no local data storage, or only a thin caching layer; data is streamed or copied from a ``nearby" T2D when needed by jobs. In OSG, this model has been adopted successfully for CMS AAA sites; but the network topology and capacity in the USA is significantly different to that in the UK (and much of Europe). We present the result of several operational tests: the in-production University College London (UCL) site, which runs ATLAS workloads using storage at the Queen Mary University of London (QMUL) site; the Oxford site, which has had scaling tests performed against T2Ds in various locations in the UK (to test network effects); and the Durham site, which has been testing the specific ATLAS caching solution of ``Rucio Cache" integration with ARC's caching layer

Using Subcategorization to Resolve Verb Class Ambiguity

Levin's (1993) taxonomy of verbs and their classes is a widely used resource for lexical semantics. In her framework, some verbs, such as give exhibit no class ambiguity. But other verbs, such as write, can inhabit more than one class. In some of these am- biguous cases the appropriate class for a particular token of a verb is immediately obvious from inspection of the surrounding context, In others it is not, and an application which wants to recover this infor- mation will be forced to rely on some more or less elaborate process of inference. We present a simple statistical model of verb class ambiguity and show how it can be used to carry out such inference

Stochastic text generation

Introduction: generation and understanding Natural Language Generation (NLG) research aims at systems which produce coherent natural language text from an underlying representation of knowledge. Systems must produce language|single sentences or more complex discourses|which (i) faithfully represents the relevant knowledge, and also (ii) does this in a naturalsounding way. These have been termed the delity and uency goals, respectively (Ward 1993). The uency goal leads to important dierences between research in NLG and that in Natural Language Understanding (NLU). An NLU system has to recover meaning representations from input strings of text or speech. Whether or not a given string sounds natural, or elegant, or forceful is immaterial. What matters is that an NLU system should be able to extract some meaning, and that the meaning should correspond as closely as possible to that intended by the string's speaker or writer. At one level, NLG can be characterised as the inv