Question

如何从wordnet生成更一般，更不一般和等价的关系？

RitaWordnet中的wordnet相似度给出了一个数字，如-1.0,0.222或1.0，但如何得出更一般，更不一致的词之间的关系？哪个工具适合那个？请帮帮我

我打印后得到java.lang.NullPointerException “全名是”

package wordnet;

import rita.wordnet.RiWordnet;

public class Main {
    public static void main(String[] args) {
        try {
            // Would pass in a PApplet normally, but we don't need to here
            RiWordnet wordnet = new RiWordnet();
            wordnet.setWordnetHome("/usr/share/wordnet/dict");
            // Demo finding parts of speech
            String word = "first name";
            System.out.println("\nFinding parts of speech for " + word + ".");
            String[] partsofspeech = wordnet.getPos(word);
            for (int i = 0; i < partsofspeech.length; i++) {
                System.out.println(partsofspeech[i]);
            }

            //word = "eat";
            String pos = wordnet.getBestPos(word);
            System.out.println("\n\nDefinitions for " + word + ":");
            // Get an array of glosses for a word
            String[] glosses = wordnet.getAllGlosses(word, pos);
            // Display all definitions
            for (int i = 0; i < glosses.length; i++) {
                System.out.println(glosses[i]);
            }

            // Demo finding a list of related words (synonyms)
            //word = "first name";
            String[] poss = wordnet.getPos(word);
            for (int j = 0; j < poss.length; j++) {
                System.out.println("\n\nSynonyms for " + word + " (pos: " + poss[j] + ")");
                String[] synonyms = wordnet.getAllSynonyms(word, poss[j], 10);
                for (int i = 0; i < synonyms.length; i++) {
                    System.out.println(synonyms[i]);
                }
            }

            // Demo finding a list of related words
            // X is Hypernym of Y if every Y is of type X
            // Hyponym is the inverse
            //word = "nurse";
            pos = wordnet.getBestPos(word);
            System.out.println("\n\nHyponyms for " + word + ":");
            String[] hyponyms = wordnet.getAllHyponyms(word, pos);
            //System.out.println(hyponyms.length);
            //if(hyponyms!=null)
            for (int i = 0; i < hyponyms.length; i++) {


                System.out.println(hyponyms[i]);
            }

            System.out.println("\n\nHypernyms for " + word + ":");
            String[] hypernyms = wordnet.getAllHypernyms(word, pos);
            //if(hypernyms!=null)
            for (int i = 0; i < hypernyms.length; i++) {
                System.out.println(hypernyms[i]);
            }

               System.out.println("\n\nHolonyms for " + word + ":");

            String[] holonyms = wordnet.getAllHolonyms(word, pos);
            //if(holonyms!=null)
            for (int i = 0; i < holonyms.length; i++) {
                System.out.println(holonyms[i]);
            }

              System.out.println("\n\nmeronyms for " + word + ":");
            String[] meronyms = wordnet.getAllMeronyms(word, pos);
            if(meronyms!=null)
            for (int i = 0; i < meronyms.length; i++) {
                System.out.println(meronyms[i]);
            }
              System.out.println("\n\nAntonym for " + word + ":");
            String[] antonyms = wordnet.getAllAntonyms(word, pos);
            if(antonyms!=null)
            for (int i = 0; i < antonyms.length; i++) {
                System.out.println(antonyms[i]);
            }


            String start = "cameras";
            String end = "digital cameras";
            pos = wordnet.getBestPos(start);

            // Wordnet can find relationships between words
            System.out.println("\n\nRelationship between: " + start + " and " + end);
            float dist = wordnet.getDistance(start, end, pos);
            String[] parents = wordnet.getCommonParents(start, end, pos);
            System.out.println(start + " and " + end + " are related by a distance of: " + dist);

            // These words have common parents (hyponyms in this case)
            System.out.println("Common parents: ");
            if (parents != null) {
                for (int i = 0; i < parents.length; i++) {
                    System.out.println(parents[i]);
                }
            }

            //wordnet.
            // System.out.println("\n\nHypernym Tree for " + start);
            // int[] ids = wordnet.getSenseIds(start,wordnet.NOUN);
            // wordnet.printHypernymTree(ids[0]);
        } catch (Exception e) {
            e.printStackTrace();
        }
     }
  }

Answer 1

Rita wordnet确实提供api用于查找上位词（更一般），下位词（不太通用）和同义词。请查看以下页面了解详细信息： -

http://www.rednoise.org/rita/wordnet/documentation/index.htm

了解所有这些术语（上位词等），查看wordnet的维基百科页面。

Answer 2

您可以尝试自己解析数据库。这不会那么难。 1）找到以下文件中的单词：index.noun，index.verb，index.adj和index.noun，2）提取其synset（“感官”）的id，并为每个synset转到data.noun ，data.verb，data.adj或data.noun并提取其上位词或下位词的同义词ID。然后在这些synset ID中搜索同义词和光泽。如果你使用正则表达式，这是相当容易的。

数据库（例如index.verb）可以在Wordnet的某个目录中找到，您可以从here下载。如果您使用的是Linux，那么还有一个很好的命令行程序可以帮助您完成工作，但是如果您想将它集成到Java代码中，我恐怕您必须自己完成所有的解析。您可能还会发现this link很有趣。希望这会有所帮助：）

PS：你也可以试试NLTK（用Python编写）

wordnet关系

2 个答案: