基于转移的依存句法分析，tdp

1.训练数据

格式大致如下, 其中每行代表一个sentence和对应的Actions，两者用|||分隔，其中Actions包括三种：Shift、REDUCE_R和REDUCE_L，分别代表移入、右规约、左规约，其中sentence和Actions之间的序列长度对应关系为len(Actions) = 2 * len(sentence) - 1 ：

Bell , based in Los Angeles , makes and distributes electronic , computer and building products . ||| SHIFT SHIFT REDUCE_R SHIFT SHIFT SHIFT SHIFT REDUCE_L REDUCE_R REDUCE_R REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_L SHIFT REDUCE_R SHIFT REDUCE_R SHIFT SHIFT REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_L REDUCE_R SHIFT REDUCE_R
`` Apparently the commission did not really believe in this ideal . '' ||| SHIFT SHIFT SHIFT SHIFT REDUCE_L SHIFT SHIFT SHIFT SHIFT REDUCE_L REDUCE_L REDUCE_L REDUCE_L REDUCE_L REDUCE_L SHIFT SHIFT SHIFT REDUCE_L REDUCE_R REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_R

2.使用示例

训练

from lightnlp.sp import TDP

tdp_model = TDP()

train_path = '/home/lightsmile/Projects/NLP/DeepDependencyParsingProblemSet/data/train.sample.txt'
dev_path = '/home/lightsmile/Projects/NLP/DeepDependencyParsingProblemSet/data/dev.txt'
vec_path = '/home/lightsmile/NLP/embedding/english/glove.6B.100d.txt'

tdp_model.train(train_path, dev_path=dev_path, vectors_path=vec_path,save_path='./tdp_saves')

测试

tdp_model.load('./tdp_saves')
tdp_model.test(dev_path)

预测

from pprint import pprint
pprint(tdp_model.predict('Investors who want to change the required timing should write their representatives '
                         'in Congress , he added . '))

预测结果如下：

{DepGraphEdge(head=(',', 14), modifier=('he', 15)),
 DepGraphEdge(head=('<ROOT>', -1), modifier=('Investors', 0)),
 DepGraphEdge(head=('Congress', 13), modifier=(',', 14)),
 DepGraphEdge(head=('Investors', 0), modifier=('who', 1)),
 DepGraphEdge(head=('he', 15), modifier=('added', 16)),
 DepGraphEdge(head=('in', 12), modifier=('Congress', 13)),
 DepGraphEdge(head=('representatives', 11), modifier=('in', 12)),
 DepGraphEdge(head=('required', 6), modifier=('timing', 7)),
 DepGraphEdge(head=('should', 8), modifier=('their', 10)),
 DepGraphEdge(head=('the', 5), modifier=('change', 4)),
 DepGraphEdge(head=('the', 5), modifier=('required', 6)),
 DepGraphEdge(head=('their', 10), modifier=('representatives', 11)),
 DepGraphEdge(head=('their', 10), modifier=('write', 9)),
 DepGraphEdge(head=('timing', 7), modifier=('should', 8)),
 DepGraphEdge(head=('to', 3), modifier=('the', 5)),
 DepGraphEdge(head=('want', 2), modifier=('to', 3)),
 DepGraphEdge(head=('who', 1), modifier=('want', 2))}

返回的格式类型为set，其中DepGraphEdge为命名元组，包含head和modifier两元素，这两元素都为(word, position)元组