Improving Recall of Regular Expressions for Information Extraction

Learning or writing regular expressions to identify instances of a specific
concept within text documents with a high precision and recall is challenging.
It is relatively easy to improve the precision of an initial regular expression
by identifying false positives covered and tweaking the expression to avoid the
false positives. However, modifying the expression to improve recall is difficult
since false negatives can only be identified by manually analyzing all documents,
in the absence of any tools to identify the missing instances. We focus on partially
automating the discovery of missing instances by soliciting minimal user
feedback. We present a technique to identify good generalizations of a regular
expression that have improved recall while retaining high precision. We empirically
demonstrate the effectiveness of the proposed technique as compared to
existing methods and show results for a variety of tasks such as identification of
dates, phone numbers, product names, and course numbers on real world datasets

Antiepileptika bei Frauen im gebärfähigen Alter und in der Schwangerschaft : Vergleich der Fachinformationen in Deutschland und der Schweiz mit dem aktuellen Wissensstand [Antiepileptics in women of childbearing age and during pregnancy : Comparison of specialized information with the current state of knowledge in Germany and Switzerland].

BACKGROUND: Healthcare professionals regularly read the summary of product characteristics (SmPC) as one of the various sources of information on the risks of drug use in women of childbearing age and during pregnancy. The aim of this article is to present an overview of the teratogenic potential of various antiepileptic drugs and to compare these data with the information provided by the SmPCs. METHODS: A literature search on the teratogenic risks of 19 antiepileptic agents was conducted and the results were compared with the information on the use in women of childbearing age and during pregnancy provided by the SmPCs of 38 commercial products available in Switzerland and Germany. RESULTS: The teratogenic risk is discussed in all available SmPCs. Quantification of the risk for birth defects and the numbers of documented pregnancies are mostly missing. Reproductive safety information in SmPCs showed poor concordance with risk levels reported in the literature. Recommendations concerning the need to monitor plasma levels and possibly perform dose adjustments during pregnancy to prevent treatment failure were missing in five Swiss and two German SmPCs. DISCUSSION: The information regarding use in women of childbearing age and during pregnancy provided by the SmPCs is heterogeneous and poorly reflects the current state of knowledge. Regular updates of SmPCs are warranted in order for these documents to be of reliable use for health care professionals.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Phylogenomics of eukaryotes: Impact of missing data on large alignments

Resolving the relationships between Metazoa and other eukaryotic groups as well as between metazoan phyla is central to the understanding of the origin and evolution of animals. The current view is based on limited data sets, either a single gene with many species (e.g., ribosomal RNA) or many genes but with only a few species. Because a reliable phylogenetic inference simultaneously requires numerous genes and numerous species, we assembled a very large data set containing 129 orthologous proteins (similar to30,000 aligned amino acid positions) for 36 eukaryotic species. Included in the alignments are data from the choanoflagellate Monosiga ovata, obtained through the sequencing of about 1,000 cDNAs. We provide conclusive support for choanoflagellates as the closest relative of animals and for fungi as the second closest. The monophyly of Plantae and chromalveolates was recovered but without strong statistical support. Within animals, in contrast to the monophyly of Coelomata observed in several recent large-scale analyses, we recovered a paraphyletic Coelamata, with nematodes and platyhelminths nested within. To include a diverse sample of organisms, data from EST projects were used for several species, resulting in a large amount of missing data in our alignment (about 25%). By using different approaches, we verify that the inferred phylogeny is not sensitive to these missing data. Therefore, this large data set provides a reliable phylogenetic framework for studying eukaryotic and animal evolution and will be easily extendable when large amounts of sequence information become available from a broader taxonomic range.

Missing out? Challenges to hearing the views of all children on the barriers and supports to learning

Children's views are essential to enabling schools to fulfil their duties under the Special Educational Needs and Disability Act 2001 and create inclusive learning environments. Arguably children are the best source of information about the ways in which schools support their learning and what barriers they encounter. Accessing this requires a deeper level of reflection than simply asking what children find difficult. It is also a challenge to ensure that the views of all children contribute including those who find communication difficult. Development work in five schools is drawn on to analyse the ways in which teachers used suggestions for three interview activities. The data reveals the strengths and limitations of different ways of supporting the communication process.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.

Missing data mechanisms and their implications on the analysis of categorical data

We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.

Designing information systems which manage or avoid privacy incidents

In this paper, we consider an information system (IS) to be a set of technologies together with a set of rules about those technologies. An IS is considered to be prone to a privacy incident if it does not fully protect the private information of a user or if a dishonest user can take advantage of the privacy protection offered by the IS. This work identifies the potential privacy incidents that may occur in an IS, and proposes a framework, the MAPI Framework (Manage or Avoid Privacy Incidents), which designs IS to manage or avoid privacy incidents. The MAPI Framework can also be used for evaluating IS by identifying the missing or inappropriate technologies which may lead to privacy incidents.

Corporate entrepreneurship and innovation part 1 : the missing link

Purpose – To examine the literature on corporate entrepreneurship and innovation and to develop a combined definition of these two terms. Moreover, the literature is used to construct a holistic model that seeks to explain the links between corporate entrepreneurial activity and the innovation process. Design/methodology/approach – A number of published works on entrepreneurship and innovation are critiqued. The findings from this literature review are used to develop a framework illustrating the relationships between the corporate entrepreneur and the innovation process. Findings – The paper presents a combined definition of corporate entrepreneurship and innovation and, from the literature review, concludes that previous models on entrepreneurship and innovation are fragmented because there is little exploration on the relationships and dynamics between these two factors. A framework of corporate entrepreneurship and innovation is constructed by synthesising the information gathered from previous literature. This model shows that there are missing links between the entrepreneur and the innovation process. The paper discusses three factors that may explain both the dynamics and the relationships between the entrepreneur and the innovation process. These are entrepreneurial attitudes, vision and actions. Originality/value – This paper fulfils an identified gap in the literature, namely the lack of investigation into the links between the corporate entrepreneur and the innovation process, and suggests three factors that could be used to explain this gap. Part 2 of this paper will present a new holistic model of corporate entrepreneurship and innovation that illustrates the relationships between these two areas in more detail.

Consistency and stability in aggregation operators : an application to missing data problems

In this work we analyze the key issue of the relationship that should hold between the operators in a family {An} of aggregation operators in order to understand they properly define a consistent whole. Here we extend some of the ideas about stability of a family of aggregation operators into a more general framework, formally defining the notions of i – L and j – R strict stability for families of aggregation operators. The notion of strict stability of order k is introduced as well. Finally, we also present an application of the strict stability conditions to deal with missing data problems in an information aggregation process. For this analysis, we have focused in the weighted mean family and the quasi-arithmetic weighted means families.

The missing evidence: a systematic review of patients' experiences of adverse events in health care

PURPOSE: Preventable patient harm due to adverse events (AEs) is a significant health problem today facing contemporary health care. Knowledge of patients' experiences of AEs is critical to improving health care safety and quality. A systematic review of studies of patients' experiences of AEs was conducted to report their experiences, knowledge gaps and any challenges encountered when capturing patient experience data. DATA SOURCES: Key words, synonyms and subject headings were used to search eight electronic databases from January 2000 to February 2015, in addition to hand-searching of reference lists and relevant journals. STUDY SELECTION: Titles and abstracts of publications were screened by two reviewers and checked by a third. Full-text articles were screened against the eligibility criteria. DATA EXTRACTION: Data on design, methods and key findings were extracted and collated. RESULTS: Thirty-three publications demonstrated patients identifying a range of problems in their care; most commonly identified were medication errors, communication and coordination of care problems. Patients' income, education, health burden and marital status influence likelihood of reporting. Patients report distress after an AE, often exacerbated by receiving inadequate information about the cause. Investigating patients' experiences is hampered by the lack of large representative patient samples, data over sufficient time periods and varying definitions of an AE. CONCLUSION: Despite the emergence of policy initiatives to enhance patient engagement, few studies report patients' experiences of AEs. This information must be routinely captured and utilized to develop effective, patient-centred and system-wide policies to minimize and manage AEs.

The missing link: using the NBER recession indicator to construct coincident and leading indices economic activity

We use the information content in the decisions of the NBER Business Cycle Dating Committee to construct coincident and leading indices of economic activity for the United States. We identify the coincident index by assuming that the coincident variables have a common cycle with the unobserved state of the economy, and that the NBER business cycle dates signify the turning points in the unobserved state. This model allows us to estimate our coincident index as a linear combination of the coincident series. We establish that our index performs better than other currently popular coincident indices of economic activity.

The missing link: using the NBER recession indicator to construct coincident and leading indices economic activity

We use the information content in the decisions of the NBER Business Cycle Dating Committee to construct coincident and leading indices of economic activity for the United States. We identify the coincident index by assuming that the coincident variables have a common cycle with the unobserved state of the economy, and that the NBER business cycle dates signify the turning points in the unobserved state. This model allows us to estimate our coincident index as a linear combination of the coincident series. We establish that our index performs better than other currently popular coincident indices of economic activity.

The missing link: using the NBER recession indicator to construct coincident and leading indices economic activity

We use the information content in the decisions of the NBER Business Cycle Dating Committee to construct coincident and leading indices of economic activity for the United States. We identify the coincident index by assuming that the coincident variables have a common cycle with the unobserved state of the economy, and that the NBER business cycle dates signify the turning points in the unobserved state. This model allows us to estimate our coincident index as a linear combination of the coincident series. We compare the performance of our index with other currently popular coincident indices of economic activity.