如何使用python nltk的斯坦福大学NER加快NE识别

问题描述

首先，我将文件内容标记为句子，然后在每个句子上调用Stanford NER.但是这个过程确实很慢.我知道是否可以更快地在整个文件内容上调用它，但是我在每个句子上都调用它，因为我想索引NE识别前后的每个句子.

First I tokenize the file content into sentences and then call Stanford NER on each of the sentences. But this process is really slow. I know if I call it on the whole file content if would be faster, but I'm calling it on each sentence as I want to index each sentence before and after NE recognition.

st = NERTagger('stanford-ner/classifiers/english.all.3class.distsim.crf.ser.gz', 'stanford-ner/stanford-ner.jar')
for filename in filelist:
    sentences = sent_tokenize(filecontent) #break file content into sentences
    for j,sent in enumerate(sentences):
        words = word_tokenize(sent) #tokenize sentences into words
        ne_tags = st.tag(words) #get tagged NEs from Stanford NER

这可能是由于每个句子都调用了st.tag()，但是有什么方法可以使其运行得更快?

This is probably due to calling st.tag() for each sentence, but is there any way to make it run faster?

编辑

我想标记句子分开的原因是我想将句子写到文件中(例如句子索引)，以便在以后给定带有ne标记的句子时，我可以获得未处理的句子(我也在这里做词形化

The reason that I want to tag sentences separate is that I want to write sentences to a file (like sentence indexing) so that given the ne tagged sentence at a later stage, i can get the unprocessed sentence (i'm also doing lemmatizing here)

文件格式:

推荐答案

来自 StanfordNERTagger ，有tag_sents()函数，请参见 https://github.com/nltk/nltk/blob/develop/nltk/tag/stanford.py#L68

From StanfordNERTagger, there is the tag_sents() function, see https://github.com/nltk/nltk/blob/develop/nltk/tag/stanford.py#L68

>>> st = NERTagger('stanford-ner/classifiers/english.all.3class.distsim.crf.ser.gz', 'stanford-ner/stanford-ner.jar')
>>> tokenized_sents = [[word_tokenize(sent) for sent in sent_tokenize(filecontent)] for filename in filelist]
>>> st.tag_sents(tokenized_sents)

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