Stanford CoreNLP使用需要注意的一點

1、Stanford CoreNLP maven依賴，jdk依賴1.8

<dependency>
    <groupId>edu.stanford.nlp</groupId>
    <artifactId>stanford-corenlp</artifactId>
    <version>3.6.0</version>
</dependency>
<dependency>
    <groupId>edu.stanford.nlp</groupId>
    <artifactId>stanford-corenlp</artifactId>
    <version>3.6.0</version>
    <classifier>models</classifier>
</dependency>
<dependency>
    <groupId>edu.stanford.nlp</groupId>
    <artifactId>stanford-corenlp</artifactId>
    <version>3.6.0</version>
    <classifier>models-chinese</classifier>
</dependency>

2、Stanford CoreNLP分詞、分句、詞性標注、命名實體識別、語法分析本身支持很多，但是全部

使用會導致性能很差，比如我們實際使用中需要使用ner, parse, mention, coref可以先不適用。

annotators = segment, ssplit, pos, lemma,ner, parse, mention, coref
因為涉及復雜解析時時間復雜度很高

連接

https://stackoverflow.com/questions/29543274/stanford-nlp-annotate-text-is-very-slow

1down vote

Is the text a single long sentence? The runtime of the parser is O(n^3) with respect to the length of the sentence, which gets quite slow on sentences longer than ~40 words. If you remove the "parse, dcoref, regexner" annotators, does it speed up? And, does it then slow down again if you re-add "parse"?

3、程序的配置使用還是很方便的，可以在自己開發中借鑒使用。