python - 使用 StanfordParser 从已解析的句子中获取类型依赖项

Question

使用 NLTK 的 StanfordParser，我可以解析这样一个句子:

os.environ['STANFORD_PARSER'] = 'C:\jars' 
os.environ['STANFORD_MODELS'] = 'C:\jars'  
os.environ['JAVAHOME'] ='C:\ProgramData\Oracle\Java\javapath' 
parser = stanford.StanfordParser(model_path="C:\jars\englishPCFG.ser.gz")
sentences = parser.parse(("bring me a red ball",)) 
for sentence in sentences:
    sentence    

结果是:

Tree('ROOT', [Tree('S', [Tree('VP', [Tree('VB', ['Bring']),
Tree('NP', [Tree('DT', ['a']), Tree('NN', ['red'])]), Tree('NP',
[Tree('NN', ['ball'])])]), Tree('.', ['.'])])])

除了上图之外，我如何使用 Stanford 解析器获取类型化依赖项？像这样的东西:

1. root(ROOT-0, bring-1)
2. iobj(bring-1, me-2)
3. det(ball-5, a-3)
4. amod(ball-5, red-4)
5. dobj(bring-1, ball-5)

Answer 1

NLTK 的 StanfordParser 模块(目前)没有将树包装到 Stanford Dependencies 转换代码。你可以使用我的图书馆 PyStanfordDependencies ，它包装了依赖转换器。

如果 nltk_tree 是问题代码片段中的 sentence，那么这行得通:

#!/usr/bin/python3
import StanfordDependencies

# Use str() to convert the NLTK tree to Penn Treebank format
penn_treebank_tree = str(nltk_tree) 

sd = StanfordDependencies.get_instance(jar_filename='point to Stanford Parser JAR file')
converted_tree = sd.convert_tree(penn_treebank_tree)

# Print Typed Dependencies
for node in converted_tree:
    print('{}({}-{},{}-{})'.format(
            node.deprel,
            converted_tree[node.head - 1].form if node.head != 0 else 'ROOT',
            node.head,
            node.form,
            node.index))