Data naming

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  • When you’re setting color properties in a data access page, you normally enter a hexa- decimal value that represents the red-green-blue (RGB) color value that your browser can understand. However, data access pages must run in Microsoft Internet Explorer, and Internet Explorer also understands a wide variety of color names that you can enter instead. The following table lists those color names and their corresponding hexadecimal color code values.

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  • B 1: Khởi động máy vào log on Admin , vào thư mục C: tạo thư mục (Dulieu) và các file h1.txt , h2.txt ; sau đó vào D: tạo thư mục Backup B2: Start → Programs → Accessories → Systems Tools → Backup → tại cửa sổ Welcome → bỏ dấu chọn ô Always start in wizard mode → Click chọn Advance Mode → tại cửa sổ Backup Utility chọn tab Backup → click vào dấu “+” tại C: và đánh dấu chọn vào ô Dulieu → tại cửa sổ Backup media or file name chọn tab Browse … →...

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  • (BQ) The Survey Data Reference Manual is organized alphabetically, making it easy to find an individual entry if you know the name of a command. This overview organizes and presents the commands conceptually, that is, according to the similarities in the functions they perform.

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  • This book will introduce you to one of the most important extensions to PHP that are available, starting with PHP version 5.0—the PHP Data Objects, commonly known as PDO. PHP grew in to a very popular web programming language due to its simplicity and ease of use. One of the key factors of this growing success is the built-in possibility to access many popular relational database management systems (RDBMS), such as MySQL, PostgreSQL, and SQLite, to name just a few.

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  • [ Team LiB ] Recipe 1.7 Connecting to a Named Instance of SQL Server or Microsoft Data Engine (MSDE) Problem You want to connect to a named instance of a SQL Server or Microsoft Data Engine (MSDE). Solution You need to understand what a SQL Server or MSDE named instance is and how to connect to one.

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  • [ Team LiB ] Recipe 2.16 Mapping Table and Column Names Between the Data Source and DataSet Problem You want to control the names assigned to tables and columns when you fill a DataSet using a DataAdapter.

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  • Modifying Data Using a Strongly Typed DataSet In Chapter 10, you saw how to create and use a strongly typed DataSet class named MyDataSet. You can use objects of this class to represent the Customers table and rows from that table

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  • [ Team LiB ] Recipe 3.6 Combining Data in Tables from Heterogeneous Data Sources Problem You want to create a report that is based on data from tables in more than one data source. Solution Use ad-hoc connector names in SQL statements.

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  • Data design is an essential part of the application development cycle. By analogy, building an application is like building a house. Having the right tools is important, but we need a solid foundation: the data structure. However, producing a good data structure can be a daunting challenge; the quest for a perfect data structure can lead us to new territories where many methods are available. Which one is the best? How can we keep our focus on the goal to achieve, without losing our time?

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  • Naming Methods: Overview Oracle Net provides five naming methods: Host naming: Enables users in a TCP/IP environment to resolve names through their existing name resolution service Local naming: Locates network addresses by using information configured and stored on each individual client's tnsnames.

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  • JDBC has simplified database access in Java applications, but a few nagging wrinkles remain—namely, persisting Java objects to relational databases. With this book, you’ll learn how the Spring Framework makes that job incredibly easy with dependency injection, template classes, and object-relational-mapping (ORM). Through sample code, you’ll discover how Spring streamlines the use of JDBC and ORM tools such as Hibernate, the Java Persistence API (JPA), and Java Data Objects (JDO).

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  • Building an accurate Named Entity Recognition (NER) system for languages with complex morphology is a challenging task. In this paper, we present research that explores the feature space using both gold and bootstrapped noisy features to build an improved highly accurate Arabic NER system.

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  • This paper presents a method that assists in maintaining a rule-based named-entity recognition and classification system. The underlying idea is to use a separate system, constructed with the use of machine learning, to monitor the performance of the rule-based system. The training data for the second system is generated with the use of the rule-based system, thus avoiding the need for manual tagging. The disagreement of the two systems acts as a signal for updating the rule-based system.

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  • In this paper we deal with Named Entity Recognition (NER) on transcriptions of French broadcast data. Two aspects make the task more difficult with respect to previous NER tasks: i) named entities annotated used in this work have a tree structure, thus the task cannot be tackled as a sequence labelling task; ii) the data used are more noisy than data used for previous NER tasks. We approach the task in two steps, involving Conditional Random Fields and Probabilistic Context-Free Grammars, integrated in a single parsing algorithm.

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  • The named entity disambiguation task is to resolve the many-to-many correspondence between ambiguous names and the unique realworld entity. This task can be modeled as a classification problem, provided that positive and negative examples are available for learning binary classifiers. High-quality senseannotated data, however, are hard to be obtained in streaming environments, since the training corpus would have to be constantly updated in order to accomodate the fresh data coming on the stream. ...

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  • This paper presents an original approach to semi-supervised learning of personal name ethnicity from typed graphs of morphophonemic features and first/last-name co-occurrence statistics. We frame this as a general solution to an inference problem over typed graphs where the edges represent labeled relations between features that are parameterized by the edge types. We propose a framework for parameter estimation on different constructions of typed graphs for this problem using a gradient-free optimization method based on grid search.

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  • We report an empirical study on the role of syntactic features in building a semisupervised named entity (NE) tagger. Our study addresses two questions: What types of syntactic features are suitable for extracting potential NEs to train a classifier in a semi-supervised setting? How good is the resulting NE classifier on testing instances dissimilar from its training data? Our study shows that constituency and dependency parsing constraints are both suitable features to extract NEs and train the classifier. ...

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  • This paper describes how a machinelearning named entity recognizer (NER) on upper case text can be improved by using a mixed case NER and some unlabeled text. The mixed case NER can be used to tag some unlabeled mixed case text, which are then used as additional training material for the upper case NER. We show that this approach reduces the performance gap between the mixed case NER and the upper case NER substantially, by 39% for MUC-6 and 22% for MUC-7 named entity test data. Our method is thus useful in improving the accuracy of NERs on upper...

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  • This work studies Named Entity Recognition (NER) for Catalan without making use of annotated resources of this language. The approach presented is based on machine learning techniques and exploits Spanish resources, either by first training models for Spanish and then translating them into Catalan, or by directly training bilingual models. The resulting models are retrained on unlabelled Catalan data using bootstrapping techniques. Exhaustive experimentation has been conducted on real data, showing competitive results for the obtained NER systems. ...

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  • We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall.

    pdf12p bunthai_1 06-05-2013 21 2   Download

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