Google SyntaxNet提供输出 喜欢..
saw VBD ROOT
+-- Alice NNP nsubj
| +-- , , punct
| +-- reading VBG rcmod
| +-- who WP nsubj
| +-- had VBD aux
| +-- been VBN aux
| +-- about IN prep
| +-- SyntaxNet NNP pobj
+-- , , punct
+-- Bob NNP dobj
+-- in IN prep
| +-- hallway NN pobj
| +-- the DT det
+-- yesterday NN tmod
+-- . . punct
我想使用python来读取和解析此输出(String数据)。 并使用'标记的括号表示法打印出来' 喜欢 - >
[saw [Alice [,] [reading [who][had][been][about [SyntaxNet]]]][,][Bob][in [hallway [the]]][yesterday][.]]
你能帮助我吗?
答案 0 :(得分:6)
不是解析树,而是可以使SyntaxNet以conll格式输出所有内容,这样更易于解析。你的句子的conll格式如下:
1 Alice _ NOUN NNP _ 10 nsubj _ _
2 , _ . , _ 1 punct _ _
3 who _ PRON WP _ 6 nsubj _ _
4 had _ VERB VBD _ 6 aux _ _
5 been _ VERB VBN _ 6 aux _ _
6 reading _ VERB VBG _ 1 rcmod _ _
7 about _ ADP IN _ 6 prep _ _
8 SyntaxNet _ NOUN NNP _ 7 pobj _ _
9 , _ . , _ 10 punct _ _
10 saw _ VERB VBD _ 0 ROOT _ _
11 Bob _ NOUN NNP _ 10 dobj _ _
12 in _ ADP IN _ 10 prep _ _
13 the _ DET DT _ 14 det _ _
14 hallway _ NOUN NN _ 12 pobj _ _
15 yesterday _ NOUN NN _ 10 tmod _ _
16 . _ . . _ 10 punct _ _
可以找到每列的含义here。我们现在关注的唯一列是第一个(单词的ID),第二个(单词本身)和第7个(头部,换句话说,父级)。根节点的父节点为0。
要获得conll格式,我们只需要注释掉demo.sh的最后几行(我假设您曾用它来获取输出):
$PARSER_EVAL \
--input=$INPUT_FORMAT \
--output=stdout-conll \
--hidden_layer_sizes=64 \
--arg_prefix=brain_tagger \
--graph_builder=structured \
--task_context=$MODEL_DIR/context.pbtxt \
--model_path=$MODEL_DIR/tagger-params \
--slim_model \
--batch_size=1024 \
--alsologtostderr \
| \
$PARSER_EVAL \
--input=stdin-conll \
--output=stdout-conll \
--hidden_layer_sizes=512,512 \
--arg_prefix=brain_parser \
--graph_builder=structured \
--task_context=$MODEL_DIR/context.pbtxt \
--model_path=$MODEL_DIR/parser-params \
--slim_model \
--batch_size=1024 \
--alsologtostderr #\
# | \
# bazel-bin/syntaxnet/conll2tree \
# --task_context=$MODEL_DIR/context.pbtxt \
# --alsologtostderr
(不要忘记在前一行注释掉反斜杠)
(where I got this trick from, see the comment)
当我自己运行demo.sh时,我得到了很多我不需要的信息。你怎么摆脱我留给你弄清楚(让我知道:))。 我现在将相关部分写入一个文件,所以我可以把它管道到我要编写的python程序中。如果您可以删除信息,您应该能够将demo.sh直接传送到python程序中。
注意:我对python很新,所以请随意改进我的代码。
首先,我们只想从输入中读取conll文件。我喜欢把每个单词都放在一个很好的课堂上。
#!/usr/bin/env python
import sys
# Conll data format:
# http://ilk.uvt.nl/conll/#dataformat
#
# The only parts we need:
# 1: ID
# 2: FORM (The original word)
# 7: HEAD (The ID of its parent)
class Word:
"A class containing the information of a single line from a conll file."
def __init__(self, columns):
self.id = int(columns[0])
self.form = columns[1]
self.head = int(columns[6])
self.children = []
# Read the conll input and put it in a list of words.
words = []
for line in sys.stdin:
# Remove newline character, split on spaces and remove empty columns.
line = filter(None, line.rstrip().split(" "))
words.append(Word(line))
很好,但它还不是树形结构。我们还要做更多的工作。
我可以多次预先查看整个列表以查找每个孩子的每个单词,但这样效率会很低。我通过他们的父母对它们进行排序,然后它应该只是快速查找以获得给定父母的每个孩子。
# Sort the words by their head (parent).
lookup = [[] for _ in range(len(words) + 1)]
for word in words:
lookup[word.head].append(word)
创建树结构:
# Build a tree
def buildTree(head):
"Find the children for the given head in the lookup, recursively"
# Get all the children of this parent.
children = lookup[head]
# Get the children of the children.
for child in children:
child.children = buildTree(child.id)
return children
# Get the root's child. There should only be one child. The function returns an
# array of children so just get the first one.
tree = buildTree(0)[0] # Start with head = 0 (which is the ROOT node)
为了能够以新格式打印树,您可以向Word类添加一些方法重载:
def __str__(self):
if len(self.children) == 0:
return "[" + self.form + "]"
else:
return "[" + self.form + " " + "".join(str(child) for child in self.children) + "]"
def __repr__(self):
return self.__str__()
现在你可以这样做:
print tree
管道就像这样:
cat input.conll | ./my_parser.py
或直接来自syntaxnet:
echo "Alice, who had been reading about SyntaxNet, saw Bob in the hallway yesterday." | syntaxnet/demo.sh | ./my_parser.py