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The semantic mapping of the English language is the key to making Natural Language Processing (NLP) effective. Cognition's unique Semantic Map, which it built over the past 24 years, is the most comprehensive and complete map of the English language available today. It can be used in support of the Semantic Web for semantic search, search tools, business analytics, machine translation, document search, context search, and much more.

This paper addresses the issue of text normalization, an important yet often overlooked problem in natural language processing. By text normalization, we mean converting 'informally inputted' text into the canonical form, by eliminating 'noises' in the text and detecting paragraph and sentence boundaries in the text.

Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. UIMA is a framework and SDK for developing such applications. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at. UIMA enables such an application to be decomposed into components, for example "language identification" -> "language specific segmentation" -> "sentence boundary detection" -> "entity detection (person/place names etc.)". Each component must implement interfaces defined by the framework and must provide self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages. UIMA additionally provides capabilities to wrap components as network services, and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.

The EU IST integrated project Semantic Knowledge Technologies (SEKT) developed and exploited semantic knowledge technologies. Core to the SEKT project has been the creation of synergies by combining the three core research areas ontology management, machine learning and natural language processing.

GATE is... * the Eclipse of Natural Language Engineering, the Lucene of Information Extraction, a leading toolkit for Text Mining * used worldwide by thousands of scientists, companies, teachers and students * comprised of an architecture, a free open source framework (or SDK) and graphical development environment * used for all sorts of language processing tasks, including Information Extraction in many languages * funded by the EPSRC, BBSRC, AHRC, the EU and commercial users * 100% Java reference implementation of ISO TC37/SC4 and used with XCES in the ANC * 10 years old in 2005, used in many research projects and compatible with IBM's UIMA * based on MVC, mobile code, continuous integration, and test-driven development, with code hosted on SourceForge

The Lemur Toolkit is a open-source toolkit designed to facilitate research in language modeling and information retrieval. Lemur supports a wide range of industrial and research language applications such as ad-hoc retrieval, site-search, and text mining. The toolkit supports indexing of large-scale text databases, the construction of simple language models for documents, queries, or subcollections, and the implementation of retrieval systems based on language models as well as a variety of other retrieval models. The system is written in the C and C++ languages, and is designed as a research system to run under Unix operating systems, although it can also run under Windows.

Swingly is a new type of semantic search engine designed to help you find answers to questions - wherever they can be found on the Internet.

Starting wtih Volume 35, Issue 1, Computational Linguistics is an open access journal, freely available to all online readers. There will no longer be a print edition.