The Simple View of borrowing and code-switching

Over the past decade, extensive research has explored collocations with high mutual information (MI) scores. However, second-language learners often struggle to incorporate these collocations into their writing due to a lack of awareness and limited practice opportunities. To address this challenge, the present article presents a 10-week writing course that integrates various activities using COCA, an online corpus tool, to enhance students’ awareness and use of collocations with high MI scores. By analyzing student essays and their reflections on class activities, the article examines the benefits and challenges of using COCA for the instruction of collocations with high MI scores in the English language classroom. Pedagogical recommendations are provided to refine instructional practices.

Cluster labelling is a technique which provides useful information about the cluster to the end users. In this paper, we propose a novel approach which is the follow-up of our previous work. Our earlier approach generates clusters of web documents by using a modified apriori approach which is more efficient and faster than the traditional apriori approach. To label the clusters, the proposed approach used an effective feature selection technique which selects the top features of a cluster. Rather than labelling the cluster with 'bag of words', a concept driven mechanism has been developed which uses the Wikipedia that takes the top features of a cluster as input to generate the possible candidate labels. Mutual information (MI) score technique has been used for ranking the candidate labels and then the topmost candidates are considered as potential labels of a cluster. Experimental results on two benchmark datasets demonstrate the efficiency of our approach.

Data Research, Innovation and Virtual Environments Unit (DRIVE), NIHR Great Ormond Street Hospital BRCDiagnostic testing has an important role in modern medical practice. Despite the benefit that laboratory tests provide,...

Many surrogate-based motion models (SMMs), proposed to guide motion management in radiotherapy, are constructed by correlating motion of an external surrogate and internal anatomy during CT-simulation. Changes in this correlation define model break down. We validate a methodology that incorporates fluoroscopic (FL) images acquired during treatment for SMM construction and update. Under a prospective IRB, 4DCT scans, VisionRT (VRT) surfaces, and orthogonal FLs were collected from five lung cancer patients. VRT surfaces and two FL time-series were acquired pre- and post-treatment. A simulated annealing optimization scheme was used to estimate optimal lung deformations by maximizing the mutual information (MI) between digitally reconstructed radiographs (DRRs) of the SMM-estimated 3D images and FLs. Our SMM used partial-least-regression and was trained using the optimal deformations and VRT surfaces from the first breathing-cycle. SMM performance was evaluated using the MI score between reference FLs and the corresponding SMM or phase-assigned 4DCT DRRs. The Hausdorff distance for contoured landmarks was used to evaluate target position estimation error. For four out of five patients, two principal components approximated lung surface deformations with submillimeter accuracy. Analysis of the MI score between more than 4000 pairs of FL and DRR demonstrated that our model led to more similarity between the FL and DRR images compared to 4DCT and DRR images from a model based on an a priori correlation model. Our SMM consistently displayed lower mean and 95th percentile Hausdorff distances. For one patient, 95th percentile Hausdorff distance was reduced by 11 mm. Patient-averaged reductions in mean and 95th percentile Hausdorff distances were 3.6 mm and 7 mm for right-lung, and 3.1 mm and 4 mm for left-lung targets. FL data were used to evaluate model performance and investigate the feasibility of model update. Despite variability in breathing, use of post-treatment FL preserved model fidelity and consistently outperformed 4DCT for position estimation.

What are multiword units? There are four major kinds of multiword units: (1) A multiword unit can be a group of words that commonly occur together, like ‘take a chance’; (2) it can be a group of words where the meaning of the phrase is not obvious from the meaning of the parts, as with ‘by and large’ or ‘be taken in’ (be tricked); (3) it can simply refer to all the combinations of a particular word or type of word and its accompanying words whether they are highly frequent, strongly associated, or not; and (4) it can refer to word groups that are intuitively seen as being formulaic sequences, that is, items stored as single choices (see Durrant and Schmitt, 2009: 159, for an excellent discussion of these four types). These different kinds of multiword units are based on a variety of criteria – frequency of co-occurrence, compositionality (do the parts make the whole?), form and storage, and so it is not too surprising that there is a large and growing list of terms to cover multiword units. Wray (2000) lists around 50 terms including composites, conventionalised forms, idiomatic phrases, routine formulae, phrasal expressions, and stock utterances, just to describe formulaic sequences, and there are at least as many again to describe multiword units more generally (lexical bundles, collocations, phrasal units, multiword units). The large number of terms reflects the different purposes for looking at multiword units, and the fragmented nature of research in this area. Walker (2011) suggests that the definition of what is a multiword unit affects what appears in learners' dictionaries. A major issue in research on multiword units is the need to set clear criteria and, where possible, develop standard terminology to describe the different types of multiword units.

“Formulaic language” is a term that has come into use, particularly in the United Kingdom, in the last two decades. Its subject domain, multiword linguistic units, has been under investigation for at least the last century, with early work on multiword units by Jan Baudouin de Courtenay in Russia and Charles Bally in Switzerland. These beginnings have given rise to strong Continental research traditions in a branch of philology/linguistics termed “phraseology” (see the separate Oxford Bibliographies article in Linguistics “Idiom and Phraseology.” The term “formulaic language” encompasses two empirical domains. The first is a component of linguistic knowledge. Views differ on the extent of this domain. For some scholars it includes all multiword sequences that are held in long-term memory, including song lyrics, advertising jingles, play scripts, proverbs, and compound words. A more conservative view limits the set of formulaic sequences to vocabulary items that have grammatical structure—in other words, phrasal lexical items. The linguistic units of formulaic language are often termed “formulaic sequences,” but many other terms are used both inside the formulaic language research tradition and outside it. The second empirical domain is the use of multiword expressions in speech and writing—that is, an aspect of language use. Investigation in the first domain leads, among other things, to lexicographic and vocabulary acquisition research. Investigation in the second domain leads, for example, to studies of the use of technical phrases in air traffic control speech. Major research questions in these two domains are the identification of formulaic sequences, their place in theories of language, their acquisition in first and second language learning, their function in speech production and perception, and their function in creating native-like speech and writing.

With regional socio-economic development, the gap between water use demand and available water resources in arid and humid semi-arid areas becomes increasingly serious. In this study, the size of regional water use in the Guanzhong region and Shiyang river basin in northwest China are analyzed to identify important factors affecting it, with the aim of providing better and optimal development planning for the region. Information entropy is used to measure and characterize the diversity of regional water use. Agricultural development and meteorological factors are found to be the main issues affecting regional water use in both regions. A multiple-linear regression (MLR) model was built by applying correlation coefficient (R) and mutual information (MI) scores in the process. Results show that the low value of information entropy of water use in the Shiyang river basin is due to the high proportion of agriculture water use. Using input factors chosen by MI score was found to be the best model to simulate the change of regional water use in both regions. A method using an MLR model together with MI is shown to be able to quantify the relationship between the influencing factors and water use diversity with limited available data.

Minutiae based fingerprint matching algorithms are wildly used in fingerprint identification and verification applications. However, they may suffer from spurious matches because they do not use the rich local image information. In this paper, we extend minutiae based methods to incorporate such local image information. Our method uses local mutual information, a proven similarity measure in various applications, to improve the matching rate. The overall minutiae distribution pattern between two fingerprints is represented by the initial minutiae matching result, while the mutual information measures the similarity between neighborhoods of matched minutiae, thus enhancing the final matching decision. FVC2002 DB1 and DB3 databases are used to test the proposed approach. Experimental result shows the improvement when combining minutiae matching scores with mutual information scores.

Image classification methods based on text utilize terms extracted from image annotations (image caption, image-related article, etc.) to achieve classification. For images involving different anatomical structures (chest, spine, etc.), however, the precision of pure textual classification often suffers from highly complex text contents (e.g. text terms extracted out of two MR abdomen images may be quite different from each other: terms from one image may concerns gastroenteritis while the other contains terms involving hysteromyoma). This paper tackles the anatomy image classification problem using a hybrid approach. First, a mutual information (MI) based filter is applied to select a set of terms with top MI scores for each anatomical class and help reduce the noise existing in the raw text. Second, local features extracted from the images are transformed as visual descriptors. Last, a hybrid scheme on the results from the textual and visual methods is applied to achieved further improvement of the classification results. Experiments show that this hybrid scheme improves the results over the sole textual or visual method on different anatomical class settings.

Background Investment in pandemic preparedness is a long-term gamble, with the return on investment coming at an unknown point in the future. Many countries have chosen to stockpile key resources, and the number of pandemic economic evaluations has risen sharply since 2009. We assess the importance of uncertainty in time-to-pandemic (and associated discounting) in pandemic economic evaluation, a factor frequently neglected in the literature to-date. Methods We use a probability tree model and Monte Carlo parameter sampling to consider the cost effectiveness of antiviral stockpiling in Cambodia under parameter uncertainty. Mean elasticity and mutual information (MI) are used to assess the importance of time-to-pandemic compared with other parameters. We also consider the sensitivity to choice of sampling distribution used to model time-to-pandemic uncertainty. Results Time-to-pandemic and discount rate are the primary drivers of sensitivity and uncertainty in pandemic cost effectiveness models. Base case cost effectiveness of antiviral stockpiling ranged between is US$112 and US$3599 per DALY averted using historical pandemic intervals for time-to-pandemic. The mean elasticities for time-to-pandemic and discount rate were greater than all other parameters. Similarly, the MI scores for time to pandemic and discount rate were greater than other parameters. Time-to-pandemic and discount rate were key drivers of uncertainty in cost-effectiveness results regardless of time-to-pandemic sampling distribution choice. Conclusions Time-to-pandemic assumptions can “substantially” affect cost-effectiveness results and, in our model, is a greater contributor to uncertainty in cost-effectiveness results than any other parameter. We strongly recommend that cost-effectiveness models include probabilistic analysis of time-to-pandemic uncertainty.

The aim of this chapter is a step-by-step guide on how to infer gene networks from gene expression profiles. The definition of a gene network is given in Subheading 1, where the different types of networks are discussed. The chapter then guides the readers through a data-gathering process in order to build a compendium of gene expression profiles from a public repository. Gene expression profiles are then discretized and a statistical relationship between genes, called mutual information (MI), is computed. Gene pairs with insignificant MI scores are then discarded by applying one of the described pruning steps. The retained relationships are then used to build up a Boolean adjacency matrix used as input for a clustering algorithm to divide the network into modules (or communities). The gene network can then be used as a hypothesis generator for discovering gene function and analyzing gene signatures. Some case studies are presented, and an online web-tool called Netview is described.

Corpus-based approaches and discourse analysis in relation to reduplication and repetition

We consider the task of discovering functional dependencies in data for target attributes of interest. To solve it, we have to answer two questions: How do we quantify the dependency in a model-agnostic and interpretable way as well as reliably against sample size and dimensionality biases? How can we efficiently discover the exact or alpha -approximate top-k dependencies? We address the first question by adopting information-theoretic notions. Specifically, we consider the mutual information score, for which we propose a reliable estimator that enables robust optimization in high-dimensional data. To address the second question, we then systematically explore the algorithmic implications of using this measure for optimization. We show the problem is NP-hard and justify worst-case exponential-time as well as heuristic search methods. We propose two bounding functions for the estimator, which we use as pruning criteria in branch-and-bound search to efficiently mine dependencies with approximation guarantees. Empirical evaluation shows that the derived estimator has desirable statistical properties, the bounding functions lead to effective exact and greedy search algorithms, and when combined, qualitative experiments show the framework indeed discovers highly informative dependencies.

Low dimensional representation of multivariate data using unsupervised feature extraction is combined with a hybrid feature selection method to improve classification performance of recognition tasks. The proposed hybrid feature selector is applied to the union of feature subspaces selected by Fisher criterion and feature-class mutual information (MI). It scores each feature as a linear weighted sum of its interclass MI and Fisher criterion score. Proposed method efficiently selects features with higher class discrimination in comparison to feature-class MI, Fisher criterion or unsupervised selection using variance; thus, resulting in much improved recognition performance. In addition, the paper also highlights the use of MI between a feature and class as a computationally economical and optimal feature selector for statistically independent features.

Buddhism seems to be the fastest growing religion in the West both in terms of converts and of previously non-religious individuals who seek to practice various meditation techniques. What is it like to be a Buddhist in the West? How does one experience the world, how does one describe it, what world-views does one have? Does one’s experience change significantly and permanently after embracing Buddha’s teachings or is it just a temporary persona one puts on? I tried to answer these questions with the help of phenomenography and Corpus linguistics analysis. Phenomenography, a pragmatic method for doing qualitative research, aims at investigating different ways people think about a phenomenon. Its data analysis is always whole group oriented; patterns which emerge from the data are collected into categories of description [1]. Corpus linguistics, a study of large bodies of real written texts, is an empirically-based approach with the focus on the meaning; by observing the occurrence of lexical units (words, multi-word units, phrases etc.) in context we find out how they are typically used [2]. I investigated texts that 16 Buddhists (experimental group) and 16 non-Buddhists (control group) from different European countries produced when answering a questionnaire I designed for this occasion, the Questionnaire of Life Situations. Its main point is to describe feelings, thoughts, attitudes etc. about important aspects of life as vividly and as detailedly as possible. Very briefly, Buddhists seem to be a lot more compassionate and tolerant than general population; their way of thinking about the world is much more holistic and all-inclusive, to the point of overgeneralizing; they turned out to be more positive and optimistic as well as more self-assure, peaceful and calm. However, analysis of the correspondence with Buddhists via online forums and mails shed a sceptical light on the results as I have encountered quite some ill-tempered and defensive behaviour. Based on this disparity and the fact their answers were extremely imbued with main Buddha’s teachings I argue that Buddhist participants exhibited strong socially desirable behaviour and exaggerated in a sense that they used the questionnaire to promote Buddhism and teach me its virtues. The study has some disadvantages – age bias and lack of criteria for defining non-Buddhist group to say the least. But since this is a novel combination of approaches and my first exploration of this much unexplored interdisciplinary area of psychology of religion, world-view studies, and cultural issues in cognitive science, I am very satisfied with the result. In the future, it would be interesting to expand the study by getting more participants, enriching the questionnaire, and investigating different groups. !!References [1] F. Marton and S. Booth, Learning and awareness. Mahwah, N.J.: L. E. Associates, 1997. [2] W. Teubert, ‘My version of corpus linguistics’, International Journal of Corpus Linguistics, vol. 10, no. 1, pp. 1–13, 2005.