Data extraction

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  • Current research directions are looking at Data Mining (DM) and Knowledge Management (KM) as complementary and interrelated felds, aimed at supporting, with algorithms and tools, the lifecycle of knowledge, including its discovery, formalization, retrieval, reuse, and update. While DM focuses on the extraction of patterns, information, and ultimately knowledge from data (Giudici, 2003; Fayyad et al.

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  • Exam 70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012 Objective 1. Design anD impLement a Data WarehOuse 1.1 Design and implement dimensions. 1.2 Design and implement fact tables. 2. extract anD transfOrm Data 2.1 Define connection managers. chapter Chapter 1 Chapter 2 Chapter 1 Chapter 2 Chapter 3 Chapter 4 2.2 Design data flow. Chapter 9 Chapter 3 Chapter 5 Chapter 7 Chapter 10 Chapter 13 Chapter 18 Chapter 19 2.3 Implement data flow. Chapter 20 Chapter 3 Chapter 5 Chapter 7 Chapter 13 Chapter 18 2.4 Manage SSIS package execution. 2.5 Implement script tasks in SSIS. 3.

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  • Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like....

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  • [ Team LiB ] Recipe 8.6 Using XPath to Query Data in a DataSet Problem You need to use an XPath expression to extract certain rows from a DataSet. Solution Use SelectSingleNode( ) or SelectNodes( ). The sample code contains two event handlers: Form.

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  • Shiitake mushroom contains several therapeutic actions such as antioxidant and antimicrobial properties, carried by the diversity of its components. In the present work, extracts from shiitake mushroom were obtained using different extraction techniques: high-pressure operations and low-pressure methods. The high-pressure technique was applied to obtain shiitake extracts using pure CO2 and CO2 with co-solvent in pressures up to 30 MPa.

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  • This book starts by setting a clear foundation for what Core Data is and how it works and then takes you step-by-step through how to extract the results you need from this powerful framework. You’ll learn what the components of Core Data are and how they interact, how to design your data model, how to filter your results, how to tune performance, how to migrate your data across data model versions, and many other topics around and between these that will separate your apps from the crowd.

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  • Data warehouses usually have some missing values due to unavailable data that affect the number and the quality of the generated rules. The missing values could affect the coverage percentage and number of reduces generated from a specific data set. Missing values lead to the difficulty of extracting useful information from data set. Association rule algorithms typically only identify patterns that occur in the original form throughout the database.

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  • These days it seems like everyone is collecting data. But all of that data is just raw information -- to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless. Author Philipp Janert teaches you how to think about data: how to effectively approach data analysis problems, and how to extract all of the available information from your data.

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  • Annotating training data for event extraction is tedious and labor-intensive. Most current event extraction tasks rely on hundreds of annotated documents, but this is often not enough. In this paper, we present a novel self-training strategy, which uses Information Retrieval (IR) to collect a cluster of related documents as the resource for bootstrapping.

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  • This book offers practical recipes to solve a variety of common problems that users have with extracting Access data and performing calculations on it. Whether you use Access 2007 or an earlier version, this book will teach you new methods to query data, different ways to move data in and out of Access, how to calculate answers to financial and investment issues, how to jump beyond SQL by manipulating data with VBA, and more.

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  • Information-extraction (IE) systems seek to distill semantic relations from naturallanguage text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the unbounded number of relations found on the Web. But how well can these open systems perform? This paper presents WOE, an open IE system which improves dramatically on TextRunner’s precision and recall. ...

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  • Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors.

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  • The applicability of many current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.

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  • Data Mining: Classification and Prediction presents about Classification with decision trees; Artificial Neural Networks; Algorithm for decision tree induction; Attribute Selection Measure; Extracting Classification Rules from Trees .

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  • Machine learning approaches have been developed to address relation extraction, which is the task of extracting semantic relations between entities expressed in text. Supervised approaches are limited in scalability because labeled data is expensive to produce. A particularly attractive approach, called distant supervision (DS), creates labeled data by heuristically aligning entities in text with those in a knowledge base, such as Freebase (Mintz et al., 2009).

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  • Although researchers have conducted extensive studies on relation extraction in the last decade, supervised approaches are still limited because they require large amounts of training data to achieve high performances. To build a relation extractor without significant annotation effort, we can exploit cross-lingual annotation projection, which leverages parallel corpora as external resources for supervision.

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  • Probabilistic latent topic models have recently enjoyed much success in extracting and analyzing latent topics in text in an unsupervised way. One common deficiency of existing topic models, though, is that they would not work well for extracting cross-lingual latent topics simply because words in different languages generally do not co-occur with each other. In this paper, we propose a way to incorporate a bilingual dictionary into a probabilistic topic model so that we can apply topic models to extract shared latent topics in text data of different languages. ...

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  • We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for selecting clusters at an appropriate level of granularity. When training on different sizes of data, our semi-supervised approach consistently outperformed a state-of-the-art supervised baseline system.

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  • Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web’s natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors.

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  • Standard algorithms for template-based information extraction (IE) require predefined template schemas, and often labeled data, to learn to extract their slot fillers (e.g., an embassy is the Target of a Bombing template). This paper describes an approach to template-based IE that removes this requirement and performs extraction without knowing the template structure in advance.

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