The Annotated We
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The Annotated We
Author: Vladimir Wozniuk
language: en
Publisher: Bloomsbury Publishing PLC
Release Date: 2015-05-12
The AnnotatedWe represents the first fully annotated translation of Evgeny Zamiatin’s classic novel in English. Generally recognized as the first modern anti-utopian novel, Zamiatin’s We has puzzled scholars and critics alike, for it is both serious and playful, full of games. Long considered to be enigmatic, it stands out as unique among his works, and its importance is beyond doubt, for it not only holds the distinction of being the first work of its kind, but is also widely believed to have provided thematic elements for the two most famous dystopian works of the twentieth century, Aldous Huxley's Brave New World and George Orwell's Nineteen Eighty-Four. This new English translation employs language and syntax that mirror the precision and economy of Zamiatin’s Russian in his“poem in prose.” The commentary that accompanies the text sheds light on Zamiatin’s use of language as well as on the broad array of allusions that mark it, while at the same time suggesting many previously unacknowledged sources for the novel’s playfulness.
Natural Language Annotation for Machine Learning
Author: James Pustejovsky
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2012-10-11
Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started. Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project. Define a clear annotation goal before collecting your dataset (corpus) Learn tools for analyzing the linguistic content of your corpus Build a model and specification for your annotation project Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework Create a gold standard corpus that can be used to train and test ML algorithms Select the ML algorithms that will process your annotated data Evaluate the test results and revise your annotation task Learn how to use lightweight software for annotating texts and adjudicating the annotations This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.
Reasoning Techniques for the Web of Data
Linked Data publishing has brought about a novel “Web of Data”: a wealth of diverse, interlinked, structured data published on the Web. These Linked Datasets are described using the Semantic Web standards and are openly available to all, produced by governments, businesses, communities and academia alike. However, the heterogeneity of such data – in terms of how resources are described and identified – poses major challenges to potential consumers. Herein, we examine use cases for pragmatic, lightweight reasoning techniques that leverage Web vocabularies (described in RDFS and OWL) to better integrate large scale, diverse, Linked Data corpora. We take a test corpus of 1.1 billion RDF statements collected from 4 million RDF Web documents and analyse the use of RDFS and OWL therein. We then detail and evaluate scalable and distributed techniques for applying rule-based materialisation to translate data between different vocabularies, and to resolve coreferent resources that talk about the same thing. We show how such techniques can be made robust in the face of noisy and often impudent Web data. We also examine a use case for incorporating a PagerRank-style algorithm to rank the trustworthiness of facts produced by reasoning, subsequently using those ranks to fix formal contradictions in the data. All of our methods are validated against our real world, large scale, open domain, Linked Data evaluation corpus.