Eclipse下使用Stanford CoreNLP的方法

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源碼：CoreNLP官網。

目前release的CoreNLP version 3.5.0版本僅支援java-1.8及以上版本，因此有時需要為Eclipse添加jdk-1.8配置，配置方法如下：

• 首先，去oracle官網下載java-1.8，下載網址為：java下載，安裝完成後。
• 開啟Eclipse，選擇Window -> Preferences -> Java –> Installed JREs 進行配置：
• 點擊表單右邊的“add”，然後添加一個“Standard VM”（應該是標準虛擬機器的意思），然後點擊“next”；
• 在”JRE HOME”那一行點擊右邊的“Directory…”找到你java 的安裝路徑，比如“C:Program Files/Java/jdk1.8”

這樣你的Eclipse就已經支援jdk-1.8了。

1. 建立java工程，注意編譯環境版本選擇1.8

2. 將官網下載的源碼解壓到工程下，並匯入所需jar包

如匯入stanford-corenlp-3.5.0.jar、stanford-corenlp-3.5.0-javadoc.jar、stanford-corenlp-3.5.0-models.jar、stanford-corenlp-3.5.0-sources.jar、xom.jar等

匯入jar包過程為：項目右擊->Properties->Java Build Path->Libraries，點擊“Add JARs”，在路徑中選取相應的jar包即可。

3. 建立TestCoreNLP類，代碼如下

 1 package Test; 2  3 import java.util.List; 4 import java.util.Map; 5 import java.util.Properties; 6  7 import edu.stanford.nlp.dcoref.CorefChain; 8 import edu.stanford.nlp.dcoref.CorefCoreAnnotations.CorefChainAnnotation; 9 import edu.stanford.nlp.ling.CoreAnnotations.LemmaAnnotation;10 import edu.stanford.nlp.ling.CoreAnnotations.NamedEntityTagAnnotation;11 import edu.stanford.nlp.ling.CoreAnnotations.PartOfSpeechAnnotation;12 import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;13 import edu.stanford.nlp.ling.CoreAnnotations.TextAnnotation;14 import edu.stanford.nlp.ling.CoreAnnotations.TokensAnnotation;15 import edu.stanford.nlp.ling.CoreLabel;16 import edu.stanford.nlp.pipeline.Annotation;17 import edu.stanford.nlp.pipeline.StanfordCoreNLP;18 import edu.stanford.nlp.semgraph.SemanticGraph;19 import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation;20 import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;21 import edu.stanford.nlp.trees.Tree;22 import edu.stanford.nlp.trees.TreeCoreAnnotations.TreeAnnotation;23 import edu.stanford.nlp.util.CoreMap;24 25 public class TestCoreNLP {26     public static void main(String[] args) {27         // creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution28         Properties props = new Properties();29         props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");30         StanfordCoreNLP pipeline = new StanfordCoreNLP(props);31         32         // read some text in the text variable33         String text = "Add your text here:Beijing sings Lenovo";34         35         // create an empty Annotation just with the given text36         Annotation document = new Annotation(text);37         38         // run all Annotators on this text39         pipeline.annotate(document);40         41         // these are all the sentences in this document42         // a CoreMap is essentially a Map that uses class objects as keys and has values with custom types43         List<CoreMap> sentences = document.get(SentencesAnnotation.class);44         45         System.out.println("word\tpos\tlemma\tner");46         for(CoreMap sentence: sentences) {47              // traversing the words in the current sentence48              // a CoreLabel is a CoreMap with additional token-specific methods49             for (CoreLabel token: sentence.get(TokensAnnotation.class)) {50                 // this is the text of the token51                 String word = token.get(TextAnnotation.class);52                 // this is the POS tag of the token53                 String pos = token.get(PartOfSpeechAnnotation.class);54                 // this is the NER label of the token55                 String ne = token.get(NamedEntityTagAnnotation.class);56                 String lemma = token.get(LemmaAnnotation.class);57                 58                 System.out.println(word+"\t"+pos+"\t"+lemma+"\t"+ne);59             }60             // this is the parse tree of the current sentence61             Tree tree = sentence.get(TreeAnnotation.class);62             63             // this is the Stanford dependency graph of the current sentence64             SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);65         }66         // This is the coreference link graph67         // Each chain stores a set of mentions that link to each other,68         // along with a method for getting the most representative mention69         // Both sentence and token offsets start at 1!70         Map<Integer, CorefChain> graph = document.get(CorefChainAnnotation.class);71     }72 }

PS：該代碼的思想是將text字串交給Stanford CoreNLP處理，StanfordCoreNLP的各個組件（annotator）按“tokenize（分詞）, ssplit（斷句）, pos（詞性標註）, lemma（詞元化）, ner（具名實體識別）, parse（文法分析）, dcoref（同義字分辨）”順序進行處理。

處理完後List<CoreMap> sentences = document.get(SentencesAnnotation.class);中包含了所有分析結果，遍曆即可獲知結果。

這裡簡單的將單詞、詞性、詞元、是否實體列印出來。其餘的用法參見官網（如sentiment、parse、relation等）。

4. 執行結果：

Eclipse下使用Stanford CoreNLP的方法