Examples with NT com.hankcs.hanlp.corpus.tag.NT used on opensource projects

Example 1 with NT

use of com.hankcs.hanlp.corpus.tag.NT in project HanLP by hankcs.

the class NTDictionary method loadDat.

private EnumItem<NT>[] loadDat(String path) {
    byte[] bytes = IOUtil.readBytes(path);
    if (bytes == null)
        return null;
    NT[] values = NT.values();
    int index = 0;
    int size = ByteUtil.bytesHighFirstToInt(bytes, index);
    index += 4;
    EnumItem<NT>[] valueArray = new EnumItem[size];
    for (int i = 0; i < size; ++i) {
        int currentSize = ByteUtil.bytesHighFirstToInt(bytes, index);
        index += 4;
        EnumItem<NT> item = new EnumItem<NT>();
        for (int j = 0; j < currentSize; ++j) {
            NT tag = values[ByteUtil.bytesHighFirstToInt(bytes, index)];
            index += 4;
            int frequency = ByteUtil.bytesHighFirstToInt(bytes, index);
            index += 4;
            item.labelMap.put(tag, frequency);
        }
        valueArray[i] = item;
    }
    return valueArray;
}

Example 2 with NT

use of com.hankcs.hanlp.corpus.tag.NT in project HanLP by hankcs.

the class OrganizationRecognition method roleTag.

public static List<EnumItem<NT>> roleTag(List<Vertex> vertexList, WordNet wordNetAll) {
    List<EnumItem<NT>> tagList = new LinkedList<EnumItem<NT>>();
    //        int line = 0;
    for (Vertex vertex : vertexList) {
        // 构成更长的
        Nature nature = vertex.guessNature();
        switch(nature) {
            case nrf:
                {
                    if (vertex.getAttribute().totalFrequency <= 1000) {
                        tagList.add(new EnumItem<NT>(NT.F, 1000));
                    } else
                        break;
                }
                continue;
            case ni:
            case nic:
            case nis:
            case nit:
                {
                    EnumItem<NT> ntEnumItem = new EnumItem<NT>(NT.K, 1000);
                    ntEnumItem.addLabel(NT.D, 1000);
                    tagList.add(ntEnumItem);
                }
                continue;
            case m:
                {
                    EnumItem<NT> ntEnumItem = new EnumItem<NT>(NT.M, 1000);
                    tagList.add(ntEnumItem);
                }
                continue;
        }
        // 此处用等效词，更加精准
        EnumItem<NT> NTEnumItem = OrganizationDictionary.dictionary.get(vertex.word);
        if (NTEnumItem == null) {
            NTEnumItem = new EnumItem<NT>(NT.Z, OrganizationDictionary.transformMatrixDictionary.getTotalFrequency(NT.Z));
        }
        tagList.add(NTEnumItem);
    //            line += vertex.realWord.length();
    }
    return tagList;
}

Example 3 with NT

use of com.hankcs.hanlp.corpus.tag.NT in project HanLP by hankcs.

the class OrganizationRecognition method Recognition.

public static boolean Recognition(List<Vertex> pWordSegResult, WordNet wordNetOptimum, WordNet wordNetAll) {
    List<EnumItem<NT>> roleTagList = roleTag(pWordSegResult, wordNetAll);
    if (HanLP.Config.DEBUG) {
        StringBuilder sbLog = new StringBuilder();
        Iterator<Vertex> iterator = pWordSegResult.iterator();
        for (EnumItem<NT> NTEnumItem : roleTagList) {
            sbLog.append('[');
            sbLog.append(iterator.next().realWord);
            sbLog.append(' ');
            sbLog.append(NTEnumItem);
            sbLog.append(']');
        }
        System.out.printf("机构名角色观察：%s\n", sbLog.toString());
    }
    List<NT> NTList = viterbiExCompute(roleTagList);
    if (HanLP.Config.DEBUG) {
        StringBuilder sbLog = new StringBuilder();
        Iterator<Vertex> iterator = pWordSegResult.iterator();
        sbLog.append('[');
        for (NT NT : NTList) {
            sbLog.append(iterator.next().realWord);
            sbLog.append('/');
            sbLog.append(NT);
            sbLog.append(" ,");
        }
        if (sbLog.length() > 1)
            sbLog.delete(sbLog.length() - 2, sbLog.length());
        sbLog.append(']');
        System.out.printf("机构名角色标注：%s\n", sbLog.toString());
    }
    OrganizationDictionary.parsePattern(NTList, pWordSegResult, wordNetOptimum, wordNetAll);
    return true;
}

Example 4 with NT

use of com.hankcs.hanlp.corpus.tag.NT in project HanLP by hankcs.

the class OrganizationDictionary method parsePattern.

/**
     * 模式匹配
     *
     * @param ntList         确定的标注序列
     * @param vertexList     原始的未加角色标注的序列
     * @param wordNetOptimum 待优化的图
     * @param wordNetAll
     */
public static void parsePattern(List<NT> ntList, List<Vertex> vertexList, final WordNet wordNetOptimum, final WordNet wordNetAll) {
    //        ListIterator<Vertex> listIterator = vertexList.listIterator();
    StringBuilder sbPattern = new StringBuilder(ntList.size());
    for (NT nt : ntList) {
        sbPattern.append(nt.toString());
    }
    String pattern = sbPattern.toString();
    final Vertex[] wordArray = vertexList.toArray(new Vertex[0]);
    trie.parseText(pattern, new AhoCorasickDoubleArrayTrie.IHit<String>() {

        @Override
        public void hit(int begin, int end, String keyword) {
            StringBuilder sbName = new StringBuilder();
            for (int i = begin; i < end; ++i) {
                sbName.append(wordArray[i].realWord);
            }
            String name = sbName.toString();
            // 对一些bad case做出调整
            if (isBadCase(name))
                return;
            // 正式算它是一个名字
            if (HanLP.Config.DEBUG) {
                System.out.printf("识别出机构名：%s %s\n", name, keyword);
            }
            int offset = 0;
            for (int i = 0; i < begin; ++i) {
                offset += wordArray[i].realWord.length();
            }
            wordNetOptimum.insert(offset, new Vertex(Predefine.TAG_GROUP, name, ATTRIBUTE, WORD_ID), wordNetAll);
        }
    });
}