使用networkx为每个句子创建图 [英] Creating a graph using networkx for each sentence

问题描述

我想为句子列表中的每个句子创建一个图形.如果句子包含专有名词，则该词将附加到subject_list.如果单词是普通名词，则将单词附加到object_list上，如果单词是动词，则将单词附加到verb_list上. 如何为每个句子分别创建图?

I want to create a graph for each sentence in the sentence_list. If the sentence contains a proper noun then that word is appended to the subject_list. If the word is a common noun then the word is appended to the object_list and if the word is a verb then it is appended to the verb_list. How can I create a graph separately for each sentence?

sentence_list=['Tom drinks milk', 'Jack plays cricket', 'Tim ate rice']

tag_list=[Tom:'NP',drinks:'VF',milk:'NN',plays:'VF',cricket:'NN',ate:'VF',rice:'NN',Tim:'NP', Jack:'NP']
subject_list=[]
object_list=[]
verb_list=[]

newDict = {}
for sent in sentence_list:
   for line in tag_list:
       k,v = line.strip().split(':')
       newDict[k.strip()] = v.strip()
       if v=='NP':
          subject_list.append(k)
          print('SUBJECT:',subject_list)
       if v=='NN':
          object_list.append(k)
          print('OBJECT',object_list)                 
       if v=='VF':
        verb_list.append(k)
        print('VERB',verb_list)
 import networkx as nx
 import matplotlib.pyplot as pl


 labels={}
 graph = nx.Graph()

 for subject in subject_list:
     s=subject.decode('utf-8')
     graph.add_node(s)
     labels[s]=s


 for obj in object_list:
     b=obj.decode('utf-8')
     graph.add_node(b)
     labels[b]=b

 for verb in verb_list:
     v=verb.decode('utf-8')
     graph.add_node(v)
     labels[v]=v

 for s,o,v in subject_list,object_list,verb_list:
     graph.add_edge(subject_list[s],object_list[o])
     graph.add_edge(object_list[o],verb_list[v])


      pos=nx.spring_layout(graph)
 nx.draw_networkx(graph, pos=pos, labels=labels)
 pl.show()

预期输出

推荐答案

sentence_list=['Tom drinks milk', 'Jack plays cricket', 'Tim ate rice']

tag_list={'Tom':'NP','Tim':'NP','Jack':'NP','drinks':'VF','milk':'NN','plays':'VF','cricket':'NN','ate':'VF','rice':'NN'}
tag_list_keys = tag_list.keys()
subject_list=[]
object_list=[]
verb_list=[]

def classify(item):
    if item in tag_list_keys:
        if tag_list[item] == 'NP': subject_list.append(item)
        if tag_list[item] == 'NN': object_list.append(item)
        if tag_list[item] == 'VF': verb_list.append(item)



def extract(item):
    item_split = item.split(' ')
    map(classify, item_split)

map(extract, sentence_list)

print('SUBJECT:',subject_list)
print('OBJECT',object_list)                 
print('VERB',verb_list)

import networkx as nx
import matplotlib.pyplot as plt

G = nx.Graph()
for i in range(3):
    G.add_node(object_list[i])
    G.add_node(verb_list[i])
    G.add_node(subject_list[i])
    G.add_edge(verb_list[i],object_list[i])
    G.add_edge(subject_list[i],verb_list[i])
nx.draw(G, with_labels= True)
plt.show()

希望这对您有用.

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