If you use Entity Framework in Visual Studio 2008 and .NET 3.5, this is the book you want. Programming Entity Framework, 1st Edition offers experienced developers a thorough introduction to Microsoft’s core framework for modeling and interacting with data in .NET applications. This hands-on tour provides a deep understanding of Entity Framework’s architecture and APIs, and explains how to use the framework in a variety of applications built with Visual Studio 2008 and .NET 3.5.
Entity Framework 4.0 Recipes provides an exhaustive collection of ready-to-use code solutions for Entity Framework, Microsoft’s vision for the future of data access. Entity Framework is a model-centric data access platform with an ocean of new concepts and patterns for developers to learn. With this book, you will learn the core concepts of Entity Framework through a broad range of clear and concise solutions to everyday data access tasks.
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Get a thorough introduction to ADO.NET Entity Framework 4 -- Microsoft's core framework for modeling and interacting with data in .NET applications. The second edition of this acclaimed guide provides a hands-on tour of the framework latest version in Visual Studio 2010 and .NET Framework 4. Not only will you learn how to use EF4 in a variety of applications, you'll also gain a deep understanding of its architecture and APIs.
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Getting the DbContext API into Your Project Looking at Some Highlights of the DbContext API Reducing and Simplifying Ways to Work with a Set Retrieving an Entity Using ID with DbSet.Find Avoiding Trolling Around the Guts of Entity Framework Working with the BreakAway Model Getting the Sample Solution Getting DbContext from an EDMX Model Ensuring DbContext Instances
We present work on the automatic generation of short indicative-informative abstracts of scientific and technical articles. The indicative part of the abstract identifies the topics of the document while the informative part of the abstract elaborate some topics according to the reader's interest by motivating the topics, describing entities and defining concepts. We have defined our method of automatic abstracting by studying a corpus professional abstracts. The method also considers the reader's interest as essential in the process of abstracting. ...
Julia Lerman is the leading independent authority on the Entity Framework and has been using and teaching the technology since its inception in 2006. She is well known in the .NET community as a Microsoft MVP, ASPInsider, and INETA Speaker. Julia is a frequent presenter at technical conferences around the world and writes articles for many well-known technical publications including the Data Points column in MSDN Magazine.
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.
Distributional similarity is a classic technique for entity set expansion, where the system is given a set of seed entities of a particular class, and is asked to expand the set using a corpus to obtain more entities of the same class as represented by the seeds. This paper shows that a machine learning model called positive and unlabeled learning (PU learning) can model the set expansion problem better. Based on the test results of 10 corpora, we show that a PU learning technique outperformed distributional similarity significantly. ...
Turkish is an agglutinative language with complex morphological structures, therefore using only word forms is not enough for many computational tasks. In this paper we analyze the effect of morphology in a Named Entity Recognition system for Turkish. We start with the standard word-level representation and incrementally explore the effect of capturing syntactic and contextual properties of tokens. Furthermore, we also explore a new representation in which roots and morphological features are represented as separate tokens instead of representing only words as tokens. ...
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.
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.
We extend the original entity-based coherence model (Barzilay and Lapata, 2008) by learning from more ﬁne-grained coherence preferences in training data. We associate multiple ranks with the set of permutations originating from the same source document, as opposed to the original pairwise rankings. We also study the eﬀect of the permutations used in training, and the eﬀect of the coreference component used in entity extraction.
There are lexical, syntactic, semantic and discourse variations amongst the languages used in various biomedical subdomains. It is important to recognise such differences and understand that biomedical tools that work well on some subdomains may not work as well on others. We report here on the semantic variations that occur in the sublanguages of two biomedical subdomains, i.e. cell biology and pharmacology, at the level of named entity information.
It is often claimed that Named Entity recognition systems need extensive gazetteers--lists of names of people, organisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named Entity recognition systems. We report on a Named Entity recognition system which combines rule-based grammars with statistical (maximum entropy) models. We report on the system's performance with gazetteers of different types and different sizes, using test material from the MUC-7 competition. ...
One goal of natural language generation is to produce coherent text that presents information in a logical order. In this paper, we show that topological ﬁelds, which model high-level clausal structure, are an important component of local coherence in German. First, we show in a sentence ordering experiment that topological ﬁeld information improves the entity grid model of Barzilay and Lapata (2008) more than grammatical role and simple clausal order information do, particularly when manual annotations of this information are not available. ...
We propose methods for estimating the probability that an entity from an entity database is associated with a web search query. Association is modeled using a query entity click graph, blending general query click logs with vertical query click logs. Smoothing techniques are proposed to address the inherent data sparsity in such graphs, including interpolation using a query synonymy model. A large-scale empirical analysis of the smoothing techniques, over a 2-year click graph collected from a commercial search engine, shows signiﬁcant reductions in modeling error. ...
We use search engine results to address a particularly difﬁcult cross-domain language processing task, the adaptation of named entity recognition (NER) from news text to web queries. The key novelty of the method is that we submit a token with context to a search engine and use similar contexts in the search results as additional information for correctly classifying the token. We achieve strong gains in NER performance on news, in-domain and out-of-domain, and on web queries.