The Resource Description Framework (RDF) is a general-purpose language for representing information in the Web.
This document defines a textual syntax for RDF called Turtle that allows RDF graphs to be completely written in a compact and natural text form, with abbreviations for common usage patterns and datatypes. Turtle provides levels of compatibility with the existing N-Triples and Notation 3 formats as well as the triple pattern syntax of the SPARQL W3C Proposed Recommendation.
This document specifies a language that is in common usage under the name "Turtle". It is intended to be compatible with, and a subset of, Notation 3.
This document is an introduction to the JavaScript Programming Language for professional programmers. It is a small language, so if you are familiar with other languages, then this won't be too demanding.
JavaScript is not Java. They are two very different languages. JavaScript is not a subset of Java. It is not interpreted Java. (Java is interpreted Java!) JavaScript shares C-family syntax with Java, but at a deeper level it shows greater similarity to the languages Scheme and Self. It is a small language, but it is also a suprisingly powerful and expressive language.You should take a look at it. You will find that it is not a toy language, but a full programming language with many distinctive properties.
CSL provides an easy-to-use but feature-rich XML language to describe bibliographic and citation formatting. It has been developed alongside CiteProc. Analogous to BibTeX .bst files or the binary equivalents in proprietary applications like Endnote, CSL is open, international-ready, and designed on a solid foundation that yields a language that is easy-to-use, while able to flexibly-but-reliably format bibliographies and citations for a wide variety of fields.
Finding important information in unstructured text
From Language and Information Technologies
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A vast majority of the information we deal with in everyday life consists of raw, unstructured text, where the most important facts or concepts are not always readily available, but hidden in the myriad of details that accompany them. To handle and digest the sheer amount of information we are exposed to in this information age, more sophisticated procedures are required to unveil the important parts of a text, and to allow us to process more information in less time. The goal of this project is to develop robust and accurate techniques to automatically extract important information from unstructured text, in the form of keyphrases (keyphrase extraction) or entire sentences (extractive summarization).
Funded by Google
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Graph-based NLP
From Language and Information Technologies
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The goal of this research project is to investigate efficient graph-based representations of text, and explore the application of ranking models based on such graph structures to natural language processing tasks. We bring together methods from computational linguistics and graph-theory, and combine them into a suite of innovative approaches that will improve and ultimately solve difficult problems in natural language processing. Specifically, we are currently working on the application of graph centrality algorithms to problems such as word sense disambiguation, text summarization and keyword extraction.
Find language and speech technology experts all over the world,
-- and be found if you are an expert yourself!
ELSNET has just taken over the responsibility for the joint ELSNET / STN directory of experts in the field of language and speech processing and related areas. This directory is intended to provide direct access to the top experts in these fields. At this moment the directory includes 1202 experts from 64 countries all over the world, both from the academic and from the industrial community.