NLP之Stanford Parser

本文介绍了句法分析的基本概念及其应用,重点探讨了Stanford Parser的使用方法及解析结果的理解,包括Constituency Parser和Dependency Parser的区别。

Parser

主要有以下几个问题:

  • Parser是什么?
  • 如何使用Stanford Parser
  • Parser把一个句子转变为树状结构,那么这棵树具体的是怎么样,怎么操作这棵树?也就是说,怎么把这棵树用在LSTM上。这里面又包含了许多问题。

一、Definition

我只知道Parser是句法分析。
专业一点的说:
句法分析判断输入的单词序列(一般为句子)的构成是否合乎给定的语法,并通过构造句法树来确定句子的结构以及各层次句法成分之间的关系,即确定一个句子中的哪些词构成一个短语,哪些词是动词的主语或宾语等问题。

二、Usage of Stanford Parser

1 . download

2 . 解压之后的目录如下:

image

3 . 执行

# mydata.txt是我的语料,输出在result.txt
./lexparser.sh mydata.txt > result.txt

4.结果如图所示:
mydata.txt中只有一句话My dog also likes eating sausage.

下面是My dog also likes eating sausage.的两种Parser的结果:

parser result

三、Tree

一开始这个结果看不明白。第二种还好,先理解了;第一种是要通过入栈出栈来实现的。

第一种是Constituency Parser,第二种是Dependency Parser。

下面是我手绘的图:

Constituency Parser:

这个我感觉应该是理解错了,这个S是啥意思呢???

constituency

Dependency Parser

dependency

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.
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