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ASV Toolbox is a modular collection of tools for the exploration of written language data. They work either on word lists or text and solve several linguistic classification and clustering tasks. The topics covered contain language detection, POS-tagging, base form reduction, named entity recognition, and terminology extraction. On a more abstract level, the algorithms deal with various kinds of word similarity, using pattern based and statistical approaches. The collection can be used to work on large real world data sets as well as for studying the underlying algorithms. The ASV Toolbox can work on plain text files and connect to a MySQL database. While it is especially designed to work with corpora of the Leipzig Corpora Collection, it can easily be adapted to other sources.

Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes. For more information, see: Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.

Spring LDAP is a Java library for simplifying LDAP operations, based on the pattern of Spring's JdbcTemplate. The framework relieves the user of common chores, such as looking up and closing contexts, looping through results, encoding/decoding values and filters, and more. The LdapTemplate class encapsulates all the plumbing work involved in traditional LDAP programming, such as creating a DirContext, looping through NamingEnumerations, handling exceptions and cleaning up resources. This leaves the programmer to handle the important stuff - where to find data (DNs and Filters) and what do do with it (map to and from domain objects, bind, modify, unbind, etc.), in the same way that JdbcTemplate relieves the programmer of all but the actual SQL and how the data maps to the domain model. In addition to this, Spring LDAP provides transaction support, a pooling library, exception translation from NamingExceptions to a mirrored unchecked Exception hierarchy, as well as several utilities for working with filters, LDAP paths and Attributes.

Xerces2 is the next generation of high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces introduces the Xerces Native Interface (XNI), a complete framework for building parser components and configurations that is extremely modular and easy to program. The Apache Xerces2 parser is the reference implementation of XNI but other parser components, configurations, and parsers can be written using the Xerces Native Interface. For complete design and implementation documents, refer to the XNI Manual. Xerces2 is a fully conforming XML Schema 1.0 processor. A partial experimental implementation of the XML Schema 1.1 Structures and Datatypes Working Drafts (December 2009) and an experimental implementation of the XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010) are provided for evaluation. For more information, refer to the XML Schema page. Xerces2 also provides a complete implementation of the Document Object Model Level 3 Core and Load/Save W3C Recommendations and provides a complete implementation of the XML Inclusions (XInclude) W3C Recommendation. It also provides support for OASIS XML Catalogs v1.1. Xerces2 is able to parse documents written according to the XML 1.1 Recommendation, except that it does not yet provide an option to enable normalization checking as described in section 2.13 of this specification. It also handles namespaces according to the XML Namespaces 1.1 Recommendation, and will correctly serialize XML 1.1 documents if the DOM level 3 load/save APIs are in use.

This is the brand new, re-written from scratch plugin for ANTLR v3. Previous valiant efforts all suffered from being unable to modify the ANTLR Tool itself to provide support not just for Maven oriented things but any other tool that might wish to invoke ANTLR without resorting to the command line interface. Rather than try to shoe-horn new code into the existing Mojo (in fact I think that by incorporating a patch supplied by someone I ended up with tow versions of the Mojo, I elected to rewrite everything from scratch, including the documentation, so that we might end up with a perfect Mojo that can do everything that ANTLR v3 supports such as imported grammar processing, proper support for library directories and locating token files from generated sources, and so on. In the end I decided to also change the the ANTLR Tool.java code so that it would be the provider of all the things that a build tool needs, rather than delegating things to 5 different tools. So, things like dependencies, dependency sorting, option tracking, generating sources and so on are all folded back in to ANTLR's Tool.java code, where they belong, and they now provide a public interface to anyone that might want to interface with them. One other goal of this rewrite was to completely document the whole thing to death. Hence even this pom has more comments than funcitonal elements, in case I get run over by a bus or fall off a cliff while skiing. Jim Idle - March 2009

Xerces2 is the next generation of high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces introduces the Xerces Native Interface (XNI), a complete framework for building parser components and configurations that is extremely modular and easy to program. The Apache Xerces2 parser is the reference implementation of XNI but other parser components, configurations, and parsers can be written using the Xerces Native Interface. For complete design and implementation documents, refer to the XNI Manual. Xerces2 is a fully conforming XML Schema 1.0 processor. A partial experimental implementation of the XML Schema 1.1 Structures and Datatypes Working Drafts (December 2009) and an experimental implementation of the XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010) are provided for evaluation. For more information, refer to the XML Schema page. Xerces2 also provides a complete implementation of the Document Object Model Level 3 Core and Load/Save W3C Recommendations and provides a complete implementation of the XML Inclusions (XInclude) W3C Recommendation. It also provides support for OASIS XML Catalogs v1.1. Xerces2 is able to parse documents written according to the XML 1.1 Recommendation, except that it does not yet provide an option to enable normalization checking as described in section 2.13 of this specification. It also handles namespaces according to the XML Namespaces 1.1 Recommendation, and will correctly serialize XML 1.1 documents if the DOM level 3 load/save APIs are in use.

Xerces2 provides high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces continues to build upon the Xerces Native Interface (XNI), a complete framework for building parser components and configurations that is extremely modular and easy to program. The Apache Xerces2 parser is the reference implementation of XNI but other parser components, configurations, and parsers can be written using the Xerces Native Interface. For complete design and implementation documents, refer to the XNI Manual. Xerces2 provides fully conforming XML Schema 1.0 and 1.1 processors. An experimental implementation of the "XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010)" is also provided for evaluation. For more information, refer to the XML Schema page. Xerces2 also provides a complete implementation of the Document Object Model Level 3 Core and Load/Save W3C Recommendations and provides a complete implementation of the XML Inclusions (XInclude) W3C Recommendation. It also provides support for OASIS XML Catalogs v1.1. Xerces2 is able to parse documents written according to the XML 1.1 Recommendation, except that it does not yet provide an option to enable normalization checking as described in section 2.13 of this specification. It also handles namespaces according to the XML Namespaces 1.1 Recommendation, and will correctly serialize XML 1.1 documents if the DOM level 3 load/save APIs are in use.

Xerces2 is the next generation of high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces introduces the Xerces Native Interface (XNI), a complete framework for building parser components and configurations that is extremely modular and easy to program. The Apache Xerces2 parser is the reference implementation of XNI but other parser components, configurations, and parsers can be written using the Xerces Native Interface. For complete design and implementation documents, refer to the XNI Manual. Xerces2 is a fully conforming XML Schema 1.0 processor. A partial experimental implementation of the XML Schema 1.1 Structures and Datatypes Working Drafts (December 2009) and an experimental implementation of the XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010) are provided for evaluation. For more information, refer to the XML Schema page. Xerces2 also provides a complete implementation of the Document Object Model Level 3 Core and Load/Save W3C Recommendations and provides a complete implementation of the XML Inclusions (XInclude) W3C Recommendation. It also provides support for OASIS XML Catalogs v1.1. Xerces2 is able to parse documents written according to the XML 1.1 Recommendation, except that it does not yet provide an option to enable normalization checking as described in section 2.13 of this specification. It also handles namespaces according to the XML Namespaces 1.1 Recommendation, and will correctly serialize XML 1.1 documents if the DOM level 3 load/save APIs are in use.

GroupDocs.Metadata for Java is a class library to process metadata associated with various Document, Image, CAD, Audio, Video and Archive formats. Key Features: - Covers most popular metadata standards: XMP, EXIF, IPTC, Image Resource Blocks, ID3, document properties - Covers most popular document formats: Microsoft Word, Microsoft Excel, Microsoft PowerPoint, PDF, Microsoft OneNote, Microsoft Visio, Open Document Format - Covers most popular image formats: BMP, GIF, DjVu, JPEG, PNG, TIFF, PSD, WebP, WMF, EMF, DICOM - Covers most popular email formats: Outlook Message, Email Message - Covers most popular audio formats: Mp3, WAV - Covers most popular video formats: Avi, Mov - Create, modify and remove metadata associated with supported document and image formats with a few lines of code - Manage EXIF metadata in Jpeg and TIFF formats - Manage XMP metadata in image and PDF formats - Manage Image Resource blocks in image formats - Manage audio metadata: ID3 tag (ID3v1, ID3v2), Lyrics3 tag, APE - Utilities to inspect and clean hidden metadata in document formats - Utilities to Search and Compare all metadata - Utilities to Export metadata to Excel/Csv - Metadata cleaner utility - MIME type detection - Read track changes. Accept or reject track changes - Read EXIF maker-notes: Sony, Nikon, Canon, Panasonic For more details on the library, please visit GroupDocs website at: https://products.groupdocs.com/metadata/Java Note: The library comes up with some limitations in the evaluation mode. In order to test full features of GroupDocs.Metadata for Java library, please request a free 30-day temporary license.