Download JAR files tagged by york with all dependencies

OPDS 1.2 (New York Public Library extensions)

Library providing Concrete ingesters and utilities for the Annotated New York Times corpus.

Replaces missing numeric values using Expectation Maximization with a multivariate normal model. Described in " Schafer, J.L. Analysis of Incomplete Multivariate Data, New York: Chapman and Hall, 1997."

Implements Grading. The base classifiers are "graded". For more information, see A.K. Seewald, J. Fuernkranz: An Evaluation of Grading Classifiers. In: Advances in Intelligent Data Analysis: 4th International Conference, Berlin/Heidelberg/New York/Tokyo, 115-124, 2001.

This API determines the holidays for a given year, country/name and eventually state/region. The holiday data is stored in XML files (one for each country) and will be read from the classpath. You can provide your own holiday calendar XML file or use any of the provided ones. Currently, there are over 70 countries supported like the following: * United States * Germany * France * India * Australia * and so on Besides, those there will be more special calendars like currently existing NYSE calendar (New York Stock Exchange).

This API determines the holidays for a given year, country/name and eventually state/region. The holiday data is stored in XML files (one for each country) and will be read from the classpath. You can provide your own holiday calendar XML file or use any of the provided ones. Currently there are 63 countries supported like the following: United States, most european countries, Russia, India, Australia. Besides those there will be more special calendars like currently existing NYSE calendar (New York Stock Exchange).

Information extraction is the process of identifying specified classes of entities, relations, and events in natural language text – creating structured data from unstructured input. JET, the Java Extraction Toolkit, developed at New York University over the past fifteen years, provides a rich set of tools for research and education in information extraction from English text. These include standard language processing tools such as a tokenizer, sentence segmenter, part-of-speech tagger, name tagger, regular-expression pattern matcher, and dependency parser. Also provided are relation and event extractors based on the specifications of the U.S. Government's ACE [Automatic Content Extraction] program. The program is provided under an Apache 2.0 license.

POJava DateTime is a simple, light-weight Java-based API for parsing and manipulating dates. It parses dates from most languages and formats out of the box without having to specify which format is expected. Defaults such as time zones, and whether to interpret an internationally ambiguous date like "03/06/2014" as DMY order or MDY order are inferred by system time zone and locale and stored in a default config object that can be replaced or overridden. Multiple languages for month names are supported without any additional configuration needed. The net effect the default parser for a server in Paris would have a different automatic configuration from a server in New York. Throw a random local date at either, and it'll parse it as expected. If your server supports customers from multiple locales and time zones, then each can be specified when parsing a date/time to resolve any ambiguities.

The boilerpipe library provides algorithms to detect and remove the surplus "clutter" (boilerplate, templates) around the main textual content of a web page. The library already provides specific strategies for common tasks (for example: news article extraction) and may also be easily extended for individual problem settings. Extracting content is very fast (milliseconds), just needs the input document (no global or site-level information required) and is usually quite accurate. Boilerpipe is a Java library written by Christian Kohlschütter. It is released under the Apache License 2.0. The algorithms used by the library are based on (and extending) some concepts of the paper "Boilerplate Detection using Shallow Text Features" by Christian Kohlschütter et al., presented at WSDM 2010 -- The Third ACM International Conference on Web Search and Data Mining New York City, NY USA.