将 stanford pos tagger 导入 nltk 时遇到问题

发布于 2024-12-03 12:46:08 字数 472 浏览 2 评论 0原文

这可能是一个非常琐碎的问题。我正在尝试通过 nltk 使用 stanford pos tagger 此处问题是我的 nltk 库不包含 stanford 模块。所以我将其复制到适当的文件夹中并进行编译。现在，当我尝试运行示例时，会检测到模块，但不会检测到模块内的类。谁能告诉我哪里错了？这可能又是非常愚蠢的。

>>> from nltk.tag import stanford 
>>> st = StanfordTagger('bidirection-distsim-wsj-0-18.tagger')

我使用 py_compile 编译 stanford.py 文件。我错过了什么吗

原文

This is probably a very trivial question. I am trying to use the stanford pos tagger through nltk given here The problem is that my nltk lib doesnt contain the stanford module. So I copied the same into the appropriate folder and compiled the same. Now when i try to run an example the module is getting detected but not the class inside the module. Can anyone tell me where I am going wrong?? Again this is probably very dumb.

>>> from nltk.tag import stanford 
>>> st = StanfordTagger('bidirection-distsim-wsj-0-18.tagger')

I used py_compile to compile the stanford.py file. Am i missing something

分享到QQ

分享到微博

如果你对这篇内容有疑问，欢迎到本站社区发帖提问参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。

发布评论

需要登录才能够评论，你可以免费注册一个本站的账号。

魂牵梦绕锁你心扉 2024-12-10 12:46:08

您仅导入 stanford。为了访问 StanfordTagger 您需要使用：（

>>> from nltk.tag.stanford import StanfordTagger

假设 `StanfordTagger 没有进一步嵌套在模块中）或通过以下方式访问它：

>>> st = stanford.StanfordTagger('bidirection-distsim-wsj-0-18.tagger')

You are only importing stanford. In order to access StanfordTagger you need to use either:

>>> from nltk.tag.stanford import StanfordTagger

(assuming that `StanfordTagger is not further nested in a module) or access it by

>>> st = stanford.StanfordTagger('bidirection-distsim-wsj-0-18.tagger')

回复收藏 0 原文

[旋木] 2024-12-10 12:46:08

如果您想使用斯坦福解析器，请使用：

import os
from nltk.parse import stanford
os.environ['STANFORD_PARSER'] = '/folder/with/standford/jars'
os.environ['STANFORD_MODELS'] = '/folder/with/standford/jars'

parser = stanford.StanfordParser(model_path="/location/of/the/englishPCFG.ser.gz")
print parser.raw_batch_parse(("Hello, My name is Melroy.", "What is your name?"))

输出：

[Tree('ROOT', [Tree('S', [Tree('INTJ', [Tree('UH', ['Hello'])]),
树(',', [',']), 树('NP', [树('PRP$', ['我的']), 树('NN',
['名称']）]），树（'VP'，[树（'VBZ'，['是']），树（'ADJP'，[树（'JJ'，
['Melroy'])])]), Tree('.', ['.'])])]), Tree('ROOT', [Tree('SBARQ',
[树（'WHNP'，[树（'WP'，['什么']）]），树（'SQ'，[树（'VBZ'，
['是']), 树('NP', [树('PRP$', ['你的']), 树('NN', ['名称'])])]),
树('.', ['?'])])])]

注1：
在这个例子中，解析器和模型 jar 位于同一文件夹中。

注2：

stanford 解析器的文件名为：stanford-parser.jar
stanford 模型的文件名为：stanford-parser-xxx-models.jar

注3：
englishPCFG.ser.gz 文件可以在 models.jar 文件 (/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz) 中找到。请使用存档管理器“解压缩”models.jar 文件。

If you want to use the Stanford parser, use this:

import os
from nltk.parse import stanford
os.environ['STANFORD_PARSER'] = '/folder/with/standford/jars'
os.environ['STANFORD_MODELS'] = '/folder/with/standford/jars'

parser = stanford.StanfordParser(model_path="/location/of/the/englishPCFG.ser.gz")
print parser.raw_batch_parse(("Hello, My name is Melroy.", "What is your name?"))

Output:

[Tree('ROOT', [Tree('S', [Tree('INTJ', [Tree('UH', ['Hello'])]),
Tree(',', [',']), Tree('NP', [Tree('PRP$', ['My']), Tree('NN',
['name'])]), Tree('VP', [Tree('VBZ', ['is']), Tree('ADJP', [Tree('JJ',
['Melroy'])])]), Tree('.', ['.'])])]), Tree('ROOT', [Tree('SBARQ',
[Tree('WHNP', [Tree('WP', ['What'])]), Tree('SQ', [Tree('VBZ',
['is']), Tree('NP', [Tree('PRP$', ['your']), Tree('NN', ['name'])])]),
Tree('.', ['?'])])])]

Note 1:
In this example both the parser & model jars are in the same folder.

Note 2:

File name of stanford parser is: stanford-parser.jar
File name of stanford models is: stanford-parser-x.x.x-models.jar

Note 3:
The englishPCFG.ser.gz file can be found inside the models.jar file (/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz). Please use come archive manager to 'unzip' the models.jar file.

回复收藏 0 原文

~没有更多了~