stanford-parser-3.6.0-models.jar

stanford-parser-3.3.1.zip

lutung.zip,javalutung的mandrill api客户端-一个java mandrill api连接器

Stanford parser

Stanford parser 句法分析器及可视化.如何使用。首先你的电脑需要安装jdk。

stanford parser

About A natural language parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as "phrases") and which words are the subject or object of a verb. Probabilistic parsers use knowledge of language gained from hand-parsed sentences to try to produce the most likely analysis of new sentences. These statistical parsers still make some mistakes, but commonly work rather well. Their development was one of the biggest breakthroughs in natural language processing in the 1990s. You can try out our parser online. This package is a Java implementation of probabilistic natural language parsers, both highly optimized PCFG and lexicalized dependency parsers, and a lexicalized PCFG parser. The original version of this parser was mainly written by Dan Klein, with support code and linguistic grammar development by Christopher Manning. Extensive additional work (internationalization and language-specific modeling, flexible input/output, grammar compaction, lattice parsing, k-best parsing, typed dependencies output, user support, etc.) has been done by Roger Levy, Christopher Manning, Teg Grenager, Galen Andrew, Marie-Catherine de Marneffe, Bill MacCartney, Anna Rafferty, Spence Green, Huihsin Tseng, Pi-Chuan Chang, Wolfgang Maier, and Jenny Finkel. The lexicalized probabilistic parser implements a factored product model, with separate PCFG phrase structure and lexical dependency experts, whose preferences are combined by efficient exact inference, using an A* algorithm. Or the software can be used simply as an accurate unlexicalized stochastic context-free grammar parser. Either of these yields a good performance statistical parsing system. A GUI is provided for viewing the phrase structure tree output of the parser. As well as providing an English parser, the parser can be and has been adapted to work with other languages. A Chinese parser based on the Chinese Treebank, a German parser based on the Negra corpus and Arabic parsers based on the Penn Arabic Treebank are also included. The parser has also been used for other languages, such as Italian, Bulgarian, and Portuguese. The parser provides Stanford Dependencies output as well as phrase structure trees. Typed dependencies are otherwise known grammatical relations. This style of output is available only for English and Chinese. For more details, please refer to the Stanford Dependencies webpage. The current version of the parser requires Java 6 (JDK1.6) or later. (You can also download an old version of the parser, version 1.4, which runs under JDK 1.4, or version 2.0 which runs under JDK 1.5, but those distributions are no longer supported.) The parser also requires a reasonable amount of memory (at least 100MB to run as a PCFG parser on sentences up to 40 words in length; typically around 500MB of memory to be able to parse similarly long typical-of-newswire sentences using the factored model). The parser is available for download, licensed under the GNU General Public License (v2 or later). Source is included. The package includes components for command-line invocation, a Java parsing GUI, and a Java API. The parser code is dual licensed (in a similar manner to MySQL, etc.). Open source licensing is under the full GPL, which allows many free uses. For distributors of proprietary software, commercial licensing with a ready-to-sign agreement is available. If you don't need a commercial license, but would like to support maintenance of these tools, we welcome gift funding. The download is a 54 MB zipped file (mainly consisting of included grammar data files). If you unpack the zip file, you should have everything needed. Simple scripts are included to invoke the parser on a Unix or Windows system. For another system, you merely need to similarly configure the classpath.

stanford-parser

这是一个斯坦福的文本分词系统，可以生成语法树。这是一个斯坦福的文本分词系统，可以生成语法树