AI模型NLP

HanLP入门

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摘要: 本文介绍 HanLP 这个 NLP 框架，包括 1.x 和 2.x，Java 和 Python 接口，RESTful API 和 Native API。

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$0 HanLP 项目基本信息

HanLP 面向生产环境的多语种自然语言处理工具包，基于 PyTorch 和 TensorFlow 2.x 双引擎，目标是普及落地最前沿的NLP技术。HanLP 具备功能完善、精度准确、性能高效、语料时新、架构清晰、可自定义的特点。

HanLP 提供 RESTful 和 native 两种 API，分别面向轻量级和海量级两种场景。无论何种API何种语言，HanLP 接口在语义上保持一致，在代码上坚持开源。

轻量级 RESTful API 仅数 KB，适合敏捷开发、移动 APP 等场景。简单易用，无需 GPU 配环境，秒速安装。支持 Python，Go，Java，各个语言的安装和样例看仓库链接。

海量级 native API 依赖 PyTorch、TensorFlow 等深度学习技术，适合专业 NLP 工程师、研究者以及本地海量数据场景。要求 Python 3.6 至 3.9，支持 Windows，推荐 Linux。可以在 CPU 上运行，推荐 GPU/TPU。安装 PyTorch 版：pip install hanlp。HanLP 发布的模型分为多任务和单任务两种，多任务速度快省显存，单任务精度高更灵活。

$1 轻量级 RESTFUL API

$1-1 Python 接口

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pip install hanlp_restful

创建客户端，填入服务器地址和秘钥：

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from hanlp_restful import HanLPClient
HanLP = HanLPClient("https://www.hanlp.com/api", auth=None, language="zh", timeout=10)

调用 parse 接口，传入一篇文章，得到 HanLP 精准的分析结果。

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result = HanLP.parse("2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。阿婆主来到北京立方庭参观自然语义科技公司。")
print(result)

结果如下：

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{
  "tok/fine": [
    ["2021年", "HanLPv2.1", "为", "生产", "环境", "带来", "次", "世代", "最", "先进", "的", "多", "语种", "NLP", "技术", "。"],
    ["阿婆主", "来到", "北京", "立方庭", "参观", "自然", "语义", "科技", "公司", "。"]
  ],
  "tok/coarse": [
    ["2021年", "HanLPv2.1", "为", "生产环境", "带来", "次世代", "最", "先进", "的", "多语种", "NLP", "技术", "。"],
    ["阿婆主", "来到", "北京", "立方庭", "参观", "自然语义科技公司", "。"]
  ],
  "pos/ctb": [
    ["NT", "URL", "P", "NN", "NN", "VV", "M", "NN", "AD", "VA", "DEC", "CD", "NN", "NN", "NN", "PU"],
    ["NN", "VV", "NR", "NR", "VV", "NN", "NN", "NN", "NN", "PU"]
  ],
  "pos/pku": [
    ["t", "nx", "p", "vn", "n", "v", "q", "n", "d", "a", "u", "a", "n", "nx", "n", "w"],
    ["n", "v", "ns", "ns", "v", "n", "n", "n", "n", "w"]
  ],
  "pos/863": [
    ["nt", "w", "p", "v", "n", "v", "q", "nt", "d", "a", "u", "a", "n", "w", "n", "w"],
    ["n", "v", "ns", "n", "v", "n", "n", "n", "n", "w"]
  ],
  "ner/msra": [
    [["2021年", "DATE", 0, 1], ["HanLPv2.1", "ORGANIZATION", 1, 2]],
    [["北京立方庭", "LOCATION", 2, 4], ["自然语义科技公司", "ORGANIZATION", 5, 9]]
  ],
  "ner/pku": [
    [],
    [["北京", "ns", 2, 3], ["立方庭", "ns", 3, 4], ["自然语义科技公司", "nt", 5, 9]]
  ],
  "ner/ontonotes": [
    [["2021年", "DATE", 0, 1], ["次世代", "DATE", 6, 8]],
    [["北京", "FAC", 2, 3], ["立方庭", "LOC", 3, 4], ["自然语义科技公司", "ORG", 5, 9]]
  ],
  "srl": [
    [[["2021年", "ARGM-TMP", 0, 1], ["HanLPv2.1", "ARG0", 1, 2], ["为生产环境", "ARG2", 2, 5], ["带来", "PRED", 5, 6], ["次世代最先进的多语种NLP技术", "ARG1", 6, 15]], [["次世代", "ARGM-TMP", 6, 8], ["最", "ARGM-ADV", 8, 9], ["先进", "PRED", 9, 10], ["NLP技术", "ARG0", 13, 15]]],
    [[["阿婆主", "ARG0", 0, 1], ["来到", "PRED", 1, 2], ["北京立方庭", "ARG1", 2, 4]], [["阿婆主", "ARG0", 0, 1], ["参观", "PRED", 4, 5], ["自然语义科技公司", "ARG1", 5, 9]]]
  ],
  "dep": [
    [[6, "tmod"], [6, "nsubj"], [6, "prep"], [5, "nn"], [3, "pobj"], [0, "root"], [8, "clf"], [10, "dep"], [10, "advmod"], [15, "rcmod"], [10, "cpm"], [13, "nummod"], [15, "nn"], [15, "nn"], [6, "dobj"], [6, "punct"]],
    [[2, "nsubj"], [0, "root"], [4, "nn"], [2, "dobj"], [2, "conj"], [9, "nn"], [9, "nn"], [9, "nn"], [5, "dobj"], [2, "punct"]]
  ],
  "sdp": [
    [[[6, "Time"]], [[6, "Agt"]], [[5, "mPrep"]], [[5, "Desc"]], [[6, "Datv"]], [[0, "Root"]], [[8, "Qp"]], [[15, "TDur"]], [[10, "mDegr"]], [[15, "Desc"]], [[10, "mAux"]], [[13, "Quan"]], [[15, "Desc"]], [[15, "Nmod"]], [[6, "Cont"]], [[6, "mPunc"]]],
    [[[2, "Agt"]], [[4, "mPrep"]], [[4, "Nmod"]], [[2, "Lfin"]], [[2, "ePurp"]], [[7, "Nmod"]], [[9, "Nmod"]], [[9, "Desc"]], [[5, "Cont"]], [[2, "mPunc"]]]
  ],
  "con": [
    ["TOP", [["IP", [["NP", [["NT", ["2021年"]]]], ["NP", [["URL", ["HanLPv2.1"]]]], ["VP", [["PP", [["P", ["为"]], ["NP", [["NN", ["生产"]], ["NN", ["环境"]]]]]], ["VP", [["VV", ["带来"]], ["NP", [["IP", [["VP", [["NP", [["QP", [["CLP", [["M", ["次"]]]]]], ["NP", [["NN", ["世代"]]]]]], ["ADVP", [["AD", ["最"]]]], ["VP", [["VA", ["先进"]]]]]]]], ["DEC", ["的"]], ["NP", [["QP", [["CD", ["多"]]]], ["NP", [["NN", ["语种"]]]]]], ["NP", [["NN", ["NLP"]], ["NN", ["技术"]]]]]]]]]], ["PU", ["。"]]]]]],
    ["TOP", [["IP", [["NP", [["NN", ["阿婆主"]]]], ["VP", [["VP", [["VV", ["来到"]], ["NP", [["NR", ["北京"]], ["NP", [["NR", ["立方庭"]]]]]]]], ["VP", [["VV", ["参观"]], ["NP", [["NN", ["自然"]], ["NN", ["语义"]], ["NN", ["科技"]], ["NN", ["公司"]]]]]]]], ["PU", ["。"]]]]]]
  ]
}

$1-2 Java 接口

使用 Maven

在 pom.xml 中添加依赖。

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<dependency>
    <groupId>com.hankcs.hanlp.restful</groupId>
    <artifactId>hanlp-restful</artifactId>
    <version>0.0.8</version>
</dependency>

创建客户端，填入服务器地址和秘钥：

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HanLPClient HanLP = new HanLPClient("https://www.hanlp.com/api", null, "zh"); // auth不填则匿名，zh中文，mul多语种

返回的是 com.hankcs.hanlp.restful.HanLPClient 对象，我们可以通过文章 Java核心技术1-反射 中写的【打印一个类的全部信息的方法】代码模板来查看类类信息。

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public class com.hankcs.hanlp.restful.HanLPClient
{
  public com.hankcs.hanlp.restful.HanLPClient(java.lang.String, java.lang.String, java.lang.String, int);
  public com.hankcs.hanlp.restful.HanLPClient(java.lang.String, java.lang.String);

  public java.util.Map parse([[Ljava.lang.String;, [Ljava.lang.String;, [Ljava.lang.String;);
  public java.util.Map parse([Ljava.lang.String;);
  public java.util.Map parse([[Ljava.lang.String;);
  public java.util.Map parse(java.lang.String, [Ljava.lang.String;, [Ljava.lang.String;);
  public java.util.Map parse(java.lang.String);
  public java.util.Map parse([Ljava.lang.String;, [Ljava.lang.String;, [Ljava.lang.String;);
  public java.util.List coreferenceResolution([[Ljava.lang.String;, [Ljava.lang.String;);
  public java.util.List coreferenceResolution([[Ljava.lang.String;);
  public com.hankcs.hanlp.restful.CoreferenceResolutionOutput coreferenceResolution(java.lang.String);
  private static java.util.List _convert_clusters(java.util.List);
  public [Lcom.hankcs.hanlp.restful.mrp.MeaningRepresentation; abstractMeaningRepresentation(java.lang.String);
  public [Lcom.hankcs.hanlp.restful.mrp.MeaningRepresentation; abstractMeaningRepresentation([[Ljava.lang.String;);
  private java.lang.String post(java.lang.String, java.lang.Object);
  public java.util.List tokenize(java.lang.String);
  public java.util.List tokenize(java.lang.String, java.lang.Boolean);
  public java.util.List textStyleTransfer(java.util.List, java.lang.String);
  public java.lang.String textStyleTransfer(java.lang.String, java.lang.String);
  public java.lang.Float semanticTextualSimilarity(java.lang.String, java.lang.String);
  public java.util.List semanticTextualSimilarity([[Ljava.lang.String;);

  private java.lang.String url;
  private java.lang.String auth;
  private java.lang.String language;
  private int timeout;
  private com.fasterxml.jackson.databind.ObjectMapper mapper;
}

同样调用 parse 接口，传入一篇文章，得到 HanLP 精准的分析结果。

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import java.util.Map;

import com.hankcs.hanlp.restful.HanLPClient;

public class App {
    public static void main(String[] args ) throws Exception {
        HanLPClient hanlp = new HanLPClient("https://www.hanlp.com/api", null, "zh", 10);
        Map result = hanlp.parse("2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。阿婆主来到北京立方庭参观自然语义科技公司。");
        System.out.println(result);
    }
}

结果如下:

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{
    tok/fine=[[2021年, HanLPv2.1, 为, 生产, 环境, 带来, 次, 世代, 最, 先进, 的, 多, 语种, NLP, 技术, 。], [阿婆主, 来到, 北京, 立方庭, 参观, 自然, 语义, 科技, 公司, 。]], t
ok/coarse=[[2021年, HanLPv2.1, 为, 生产环境, 带来, 次世代, 最, 先进, 的, 多语种, NLP, 技术, 。], [阿婆主, 来到, 北京, 立方庭, 参观, 自然语义科技公司, 。]], pos/ctb=[[NT,
 URL, P, NN, NN, VV, M, NN, AD, VA, DEC, CD, NN, NN, NN, PU], [NN, VV, NR, NR, VV, NN, NN, NN, NN, PU]], pos/pku=[[t, nx, p, vn, n, v, q, n, d, a, u, a, n, nx, n, w], [n
, v, ns, ns, v, n, n, n, n, w]], pos/863=[[nt, w, p, v, n, v, q, nt, d, a, u, a, n, w, n, w], [n, v, ns, n, v, n, n, n, n, w]], ner/msra=[[[2021年, DATE, 0, 1], [HanLPv2
.1, ORGANIZATION, 1, 2]], [[北京立方庭, LOCATION, 2, 4], [自然语义科技公司, ORGANIZATION, 5, 9]]], ner/pku=[[], [[北京, ns, 2, 3], [立方庭, ns, 3, 4], [自然语义科技公司,
 nt, 5, 9]]], ner/ontonotes=[[[2021年, DATE, 0, 1], [次世代, DATE, 6, 8]], [[北京, FAC, 2, 3], [立方庭, LOC, 3, 4], [自然语义科技公司, ORG, 5, 9]]], srl=[[[[2021年, ARGM
-TMP, 0, 1], [HanLPv2.1, ARG0, 1, 2], [为生产环境, ARG2, 2, 5], [带来, PRED, 5, 6], [次世代最先进的多语种NLP技术, ARG1, 6, 15]], [[次世代, ARGM-TMP, 6, 8], [最, ARGM-ADV
, 8, 9], [先进, PRED, 9, 10], [NLP技术, ARG0, 13, 15]]], [[[阿婆主, ARG0, 0, 1], [来到, PRED, 1, 2], [北京立方庭, ARG1, 2, 4]], [[阿婆主, ARG0, 0, 1], [参观, PRED, 4, 5]
, [自然语义科技公司, ARG1, 5, 9]]]], dep=[[[6, tmod], [6, nsubj], [6, prep], [5, nn], [3, pobj], [0, root], [8, clf], [10, dep], [10, advmod], [15, rcmod], [10, cpm], [1
3, nummod], [15, nn], [15, nn], [6, dobj], [6, punct]], [[2, nsubj], [0, root], [4, nn], [2, dobj], [2, conj], [9, nn], [9, nn], [9, nn], [5, dobj], [2, punct]]], sdp=[[
[[6, Time]], [[6, Agt]], [[5, mPrep]], [[5, Desc]], [[6, Datv]], [[0, Root]], [[8, Qp]], [[15, TDur]], [[10, mDegr]], [[15, Desc]], [[10, mAux]], [[13, Quan]], [[15, Des
c]], [[15, Nmod]], [[6, Cont]], [[6, mPunc]]], [[[2, Agt]], [[4, mPrep]], [[4, Nmod]], [[2, Lfin]], [[2, ePurp]], [[7, Nmod]], [[9, Nmod]], [[9, Desc]], [[5, Cont]], [[2
, mPunc]]]], con=[[TOP, [[IP, [[NP, [[NT, [2021年]]]], [NP, [[URL, [HanLPv2.1]]]], [VP, [[PP, [[P, [为]], [NP, [[NN, [生产]], [NN, [环境]]]]]], [VP, [[VV, [带来]], [NP,
[[IP, [[VP, [[NP, [[QP, [[CLP, [[M, [次]]]]]], [NP, [[NN, [世代]]]]]], [ADVP, [[AD, [最]]]], [VP, [[VA, [先进]]]]]]]], [DEC, [的]], [NP, [[QP, [[CD, [多]]]], [NP, [[NN,
[语种]]]]]], [NP, [[NN, [NLP]], [NN, [技术]]]]]]]]]], [PU, [。]]]]]], [TOP, [[IP, [[NP, [[NN, [阿婆主]]]], [VP, [[VP, [[VV, [来到]], [NP, [[NR, [北京]], [NP, [[NR, [立方
庭]]]]]]]], [VP, [[VV, [参观]], [NP, [[NN, [自然]], [NN, [语义]], [NN, [科技]], [NN, [公司]]]]]]]], [PU, [。]]]]]]]}

$2 海量级 Native API

$2-1 2.x 版

$2-1-1 Python 接口

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pip install hanlp

例子：多任务模型

HanLP 的工作流程为加载模型然后将其当作函数调用，例如下列联合多任务模型：

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import hanlp

HanLP = hanlp.load(hanlp.pretrained.mtl.CLOSE_TOK_POS_NER_SRL_DEP_SDP_CON_ELECTRA_SMALL_ZH) # 世界最大中文语料库
result = HanLP(['2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。', '阿婆主来到北京立方庭参观自然语义科技公司。'])

结果如下：

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{
  "tok/fine": [
    ["2021年", "HanLPv2.1", "为", "生产", "环境", "带来", "次世代", "最", "先进", "的", "多语种", "NLP", "技术", "。"],
    ["阿婆主", "来到", "北京", "立方庭", "参观", "自然", "语义", "科技", "公司", "。"]
  ],
  "tok/coarse": [
    ["2021年", "HanLPv2.1", "为", "生产", "环境", "带来", "次世代", "最", "先进", "的", "多语种", "NLP", "技术", "。"],
    ["阿婆主", "来到", "北京立方庭", "参观", "自然语义科技公司", "。"]
  ],
  "pos/ctb": [
    ["NT", "NR", "P", "NN", "NN", "VV", "NN", "AD", "VA", "DEC", "NN", "NR", "NN", "PU"],
    ["NN", "VV", "NR", "NR", "VV", "NN", "NN", "NN", "NN", "PU"]
  ],
  "pos/pku": [
    ["t", "nx", "p", "vn", "n", "v", "n", "d", "a", "u", "n", "nx", "n", "w"],
    ["n", "v", "ns", "ns", "v", "n", "n", "n", "n", "w"]
  ],
  "pos/863": [
    ["nt", "w", "p", "v", "n", "v", "nt", "d", "a", "u", "n", "w", "n", "w"],
    ["n", "v", "ns", "ni", "v", "a", "n", "n", "n", "w"]
  ],
  "ner/msra": [
    [["2021年", "DATE", 0, 1], ["次世代", "DATE", 6, 7]],
    [["北京立方庭", "ORGANIZATION", 2, 4], ["自然语义科技公司", "ORGANIZATION", 5, 9]]
  ],
  "ner/pku": [
    [],
    [["北京立方庭", "nt", 2, 4]]
  ],
  "ner/ontonotes": [
    [["2021年", "DATE", 0, 1], ["HanLPv2.1", "PERSON", 1, 2]],
    [["北京立方庭", "FAC", 2, 4], ["自然语义科技公司", "ORG", 5, 9]]
  ],
  "srl": [
    [[["2021年", "ARGM-TMP", 0, 1], ["HanLPv2.1", "ARG0", 1, 2], ["为生产环境", "ARG2", 2, 5], ["带来", "PRED", 5, 6], ["次世代最先进的多语种NLP技术", "ARG1", 6, 13]], [["次世代", "ARGM-TMP", 6, 7], ["最", "ARGM-ADV", 7, 8], ["先进", "PRED", 8, 9], ["技术", "ARG0", 12, 13]]],
    [[["阿婆主", "ARG0", 0, 1], ["来到", "PRED", 1, 2], ["北京立方庭", "ARG1", 2, 4]], [["阿婆主", "ARG0", 0, 1], ["参观", "PRED", 4, 5], ["自然语义科技公司", "ARG1", 5, 9]]]
  ],
  "dep": [
    [[6, "tmod"], [6, "nsubj"], [6, "prep"], [5, "nn"], [3, "pobj"], [0, "root"], [9, "dep"], [9, "advmod"], [13, "rcmod"], [9, "cpm"], [13, "nn"], [13, "nn"], [6, "dobj"], [6, "punct"]],
    [[2, "nsubj"], [0, "root"], [4, "nn"], [2, "dobj"], [2, "conj"], [7, "nn"], [9, "nn"], [9, "nn"], [5, "dobj"], [2, "punct"]]
  ],
  "sdp": [
    [[[6, "Time"]], [[6, "Exp"]], [[5, "mPrep"]], [[5, "Desc"]], [[6, "Datv"]], [[0, "Root"]], [[9, "Time"]], [[9, "mDegr"]], [[13, "Desc"]], [[9, "mAux"]], [[13, "Desc"]], [[13, "Nmod"]], [[6, "Pat"]], [[6, "mPunc"]]],
    [[[2, "Agt"], [5, "Agt"]], [[0, "Root"]], [[4, "Nmod"]], [[2, "Lfin"]], [[2, "ePurp"]], [[7, "Nmod"]], [[9, "Nmod"]], [[9, "Desc"]], [[5, "Cont"]], [[5, "mPunc"]]]
  ],
  "con": [
    ["TOP", [["IP", [["NP", [["NT", ["2021年"]]]], ["NP", [["NR", ["HanLPv2.1"]]]], ["VP", [["PP", [["P", ["为"]], ["NP", [["NN", ["生产"]], ["NN", ["环境"]]]]]], ["VP", [["VV", ["带来"]], ["NP", [["CP", [["CP", [["IP", [["VP", [["NP", [["NN", ["次世代"]]]], ["ADVP", [["AD", ["最"]]]], ["VP", [["VA", ["先进"]]]]]]]], ["DEC", ["的"]]]]]], ["NP", [["NN", ["多语种"]]]], ["NP", [["NR", ["NLP"]], ["NN", ["技术"]]]]]]]]]], ["PU", ["。"]]]]]],
    ["TOP", [["IP", [["NP", [["NN", ["阿婆主"]]]], ["VP", [["VP", [["VV", ["来到"]], ["NP", [["NR", ["北京"]], ["NR", ["立方庭"]]]]]], ["VP", [["VV", ["参观"]], ["NP", [["NN", ["自然"]], ["NN", ["语义"]], ["NN", ["科技"]], ["NN", ["公司"]]]]]]]], ["PU", ["。"]]]]]]
  ]
}

例子：单任务模型

多任务学习的优势在于速度和显存，然而精度往往不如单任务模型。所以，HanLP 预训练了许多单任务模型并设计了优雅的流水线模式将其组装起来。代码如下：

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import hanlp
HanLP = hanlp.pipeline() \
    .append(hanlp.utils.rules.split_sentence, output_key='sentences') \
    .append(hanlp.load('FINE_ELECTRA_SMALL_ZH'), output_key='tok') \
    .append(hanlp.load('CTB9_POS_ELECTRA_SMALL'), output_key='pos') \
    .append(hanlp.load('MSRA_NER_ELECTRA_SMALL_ZH'), output_key='ner', input_key='tok') \
    .append(hanlp.load('CTB9_DEP_ELECTRA_SMALL', conll=0), output_key='dep', input_key='tok')\
    .append(hanlp.load('CTB9_CON_ELECTRA_SMALL'), output_key='con', input_key='tok')
result = HanLP('2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。阿婆主来到北京立方庭参观自然语义科技公司。')

with open("a.json", "w") as f:
    f.write(str(result))

结果如下：

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{
  "sentences": [
    "2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。",
    "阿婆主来到北京立方庭参观自然语义科技公司。"
  ],
  "tok": [
    ["2021年", "HanLPv2.1", "为", "生产", "环境", "带来", "次", "世代", "最", "先进", "的", "多", "语种", "NLP", "技术", "。"],
    ["阿婆", "主", "来到", "北京", "立方庭", "参观", "自然", "语义", "科技", "公司", "。"]
  ],
  "pos": [
    ["NT", "NR", "P", "NN", "NN", "VV", "JJ", "NN", "AD", "VA", "DEC", "CD", "NN", "NR", "NN", "PU"],
    ["NR", "AD", "VV", "NR", "NR", "VV", "NN", "NN", "NN", "NN", "PU"]
  ],
  "ner": [
    [["2021年", "DATE", 0, 1]],
    [["北京", "ORGANIZATION", 3, 4], ["立方庭", "LOCATION", 4, 5], ["自然语义科技公司", "ORGANIZATION", 6, 10]]
  ],
  "dep": [
    [[6, "tmod"], [6, "nsubj"], [6, "prep"], [5, "nn"], [3, "pobj"], [0, "root"], [8, "det"], [15, "nn"], [10, "advmod"], [15, "rcmod"], [10, "cpm"], [13, "nummod"], [15, "nn"], [15, "nn"], [6, "dobj"], [6, "punct"]],
    [[2, "nn"], [3, "nsubj"], [0, "root"], [5, "nn"], [3, "dobj"], [3, "conj"], [10, "nn"], [10, "nn"], [10, "nn"], [6, "dobj"], [3, "punct"]]
  ],
  "con": [
    ["TOP", [["IP", [["NP", [["_", ["2021年"]]]], ["NP", [["_", ["hanlpv2.1"]]]], ["VP", [["PP", [["_", ["为"]], ["NP", [["_", ["生产"]], ["_", ["环境"]]]]]], ["VP", [["_", ["带来"]], ["NP", [["CP", [["CP", [["IP", [["VP", [["NP", [["DP", [["_", ["次"]]]], ["NP", [["_", ["世代"]]]]]], ["ADVP", [["_", ["最"]]]], ["VP", [["_", ["先进"]]]]]]]], ["_", ["的"]]]]]], ["NP", [["ADJP", [["_", ["多"]]]], ["NP", [["_", ["语种"]]]]]], ["NP", [["_", ["nlp"]], ["_", ["技术"]]]]]]]]]], ["_", ["。"]]]]]],
    ["TOP", [["IP", [["NP", [["NP", [["_", ["阿婆"]]]], ["ADVP", [["_", ["主"]]]]]], ["VP", [["VP", [["_", ["来到"]], ["NP", [["_", ["北京"]], ["_", ["立方庭"]]]]]], ["VP", [["_", ["参观"]], ["NP", [["_", ["自然"]], ["_", ["语义"]], ["_", ["科技"]], ["_", ["公司"]]]]]]]], ["_", ["。"]]]]]]
  ]
}

$2-2 1.x 版

$2-2-1 Python 接口

pyhanlp 的 github 仓库链接为: https://github.com/hankcs/pyhanlp

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pip install pyhanlp

如果顺利的话，在命令行第一次 import pyhanlp 或者第一次在命令行执行 pyhanlp 时，会自动下载 HanLp 的 jar 包（包含很多算法）和数据包（包含很多模型）到 pyhanlp 的系统路径。例如：

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下载 https://file.hankcs.com/hanlp/hanlp-1.8.3-release.zip 到 /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/hanlp-1.8.3-release.zip
100%   1.8 MiB   1.8 MiB/s ETA:  0 s [=============================================================]
下载 https://file.hankcs.com/hanlp/data-for-1.7.5.zip 到 /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data-for-1.8.3.zip
100% 637.7 MiB   1.1 MiB/s ETA:  0 s [=============================================================]
解压 data.zip...

命令行工具

在命令行执行 hanlp 命令，会显示以下信息。

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usage: hanlp [-h] [-v] {segment,parse,serve,update} ...

HanLP: Han Language Processing v1.8.3

positional arguments:
  {segment,parse,serve,update}
                        which task to perform?
    segment             word segmentation
    parse               dependency parsing
    serve               start http server
    update              update jar and data of HanLP

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show installed versions of HanLP

我们可以不写代码调用 HanLP 的一些常见功能。例如 hanlp segment 执行的是分词，hanlp parse 执行的是句法分析。下面是分词的例子(<<< 是字符串重定向)。

(1) 中文分词，带词性标注 。

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hanlp segment <<< 我们可以在不写代码的前提下轻松调用很多常见功能

结果如下

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我们/rr 可以/v 在/p 不/d 写/v 代码/n 的/ude1 前提/n 下/f 轻松/a 调用/v 很多/m 常见/a 功能/n

(2) 中文分词，不带词性标注。

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hanlp segment --no-tag <<< 我们可以在不写代码的前提下轻松调用很多常见功能

结果如下

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我们 可以 在 不 写 代码 的 前提 下 轻松 调用 很多 常见 功能

(3) 中文分词，不带词性标注，指定分词算法为 CRF

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hanlp segment < input.txt > output.txt -a crf --no-tag

Python 接口的例子

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from pyhanlp import HanLP

print(HanLP.segment("你好，欢迎在Python汇总调用HanLP的API"))
for term in HanLP.segment("下雨天地面积水"):
    print("{}\t{}".format(term.word, term.nature))

# 分词
testCases = ["商品和服务"
            ,"结婚的和尚未结婚的确实在干扰分词啊"
            ,"买水果然后来世博园最后去世博会"
            ,"中国的首都是北京"
            ,"欢迎新老师生前来就餐"
            ,"工信处女干事每月经过下属科室都要亲口交代24口交换机等技术性器件的安装工作"
            ,"随着页游兴起到现在的页游繁盛，依赖于存档进行逻辑判断的设计减少了，但这块也不能完全忽略掉"
            ]
for sentence in testCases:
    print(HanLP.segment(sentence))

# 关键词提取
document = "水利部水资源司司长陈明忠9月29日在国务院新闻办举行的新闻发布会上透露，" \
           "根据刚刚完成了水资源管理制度的考核，有部分省接近了红线的指标，" \
           "有部分省超过红线的指标。对一些超过红线的地方，陈明忠表示，对一些取用水项目进行区域的现批" \
           "严格地进行水资源的论证和取水许可的批准"
print(HanLP.extractKeyword(document, 2))
# 自动摘要
print(HanLP.extractKeyword(document, 3))

# 依存句法分析
print(HanLP.parseDependency("徐先生还具体帮助他确定了把画雄鹰、松鼠和麻雀作为主攻目标"))

结果如下

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[你好/vl, ，/w, 欢迎/v, 在/p, Python/nx, 汇总/v, 调用/v, HanLP/nx, 的/ude1, API/nx]
下雨天  n
地面    n
积水    n
[商品/n, 和/cc, 服务/vn]
[结婚/vi, 的/ude1, 和/cc, 尚未/d, 结婚/vi, 的/ude1, 确实/ad, 在/p, 干扰/vn, 分词/n, 啊/y]
[买/v, 水果/n, 然后/c, 来/vf, 世博园/n, 最后/f, 去/vf, 世博会/n]
[中国/ns, 的/ude1, 首都/n, 是/vshi, 北京/ns]
[欢迎/v, 新/a, 老/a, 师生/n, 前来/vi, 就餐/vi]
[工信处/n, 女干事/n, 每月/t, 经过/p, 下属/v, 科室/n, 都/d, 要/v, 亲口/d, 交代/v, 24/m, 口/n, 交换机/n, 等/udeng, 技术性/n, 器件/n, 的/ude1, 安装/v, 工作/vn]
[随着/p, 页游/nz, 兴起/v, 到/v, 现在/t, 的/ude1, 页游/nz, 繁盛/a, ，/w, 依赖于/v, 存档/vi, 进行/vn, 逻辑/n, 判断/v, 的/ude1, 设计/vn, 减少/v, 了/ule, ，/w, 但/c, 这块/r, 也/d, 不能/v, 完全/ad, 忽略/v, 掉/v]
[水资源, 陈明忠]
[水资源, 陈明忠, 进行]
1       徐先生  徐先生  nh      nr      _       4       主谓关系        _       _
2       还      还      d       d       _       4       状中结构        _       _
3       具体    具体    a       ad      _       4       状中结构        _       _
4       帮助    帮助    v       v       _       0       核心关系        _       _
5       他      他      r       r       _       4       兼语    _       _
6       确定    确定    v       v       _       4       动宾关系        _       _
7       了      了      u       u       _       6       右附加关系      _       _
8       把      把      p       p       _       15      状中结构        _       _
9       画      画      v       v       _       8       介宾关系        _       _
10      雄鹰    雄鹰    n       n       _       9       动宾关系        _       _
11      、      、      wp      w       _       12      标点符号        _       _
12      松鼠    松鼠    n       n       _       10      并列关系        _       _
13      和      和      c       c       _       14      左附加关系      _       _
14      麻雀    麻雀    n       n       _       10      并列关系        _       _
15      作为    作为    v       v       _       6       动宾关系        _       _
16      主攻    主攻    v       vn      _       17      定中关系        _       _
17      目标    目标    n       n       _       15      动宾关系        _       _

在 pyhanlp 仓库的 tests/demos 目录中有很多例子可以参考。

$2-2-2 Java 接口

(1) Maven

可以通过 Maven 引入 HanLP，只需要在项目的 pom.xml 中添加以下依赖项即可。

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<dependency>
    <groupId>com.hankcs</groupId>
    <artifactId>hanlp</artifactId>
    <version>portable-1.8.3</version>
</dependency>

例子：HanLP.segment。

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package com.czx.hanlphelloworld;

import com.hankcs.hanlp.HanLP;

public class App {
    public static void main( String[] args ) {
        System.out.println(HanLP.segment("你好，欢迎使用 HanLP 汉语处理包"));
    }
}

两种方法运行

  • (1) 编译执行 mainClass
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  • (2) install 后运行 jar 包
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mvn clean install
java -jar target/hanlp_helloworld-1.0-SNAPSHOT.jar

结果如下

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[你好/l, ，/w, 欢迎/v, 使用/v,  /w, HanLP/nx,  /w, 汉语/nz, 处理/v, 包/v]

(2) 自行配置 data、hanlp.properties

HanLP 项目的目录结构如下：

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|---- data
|     |---- dictionary
|     |---- model
|---- pom.xml
|---- src
      |---- main
      |---- test

在 HanLP 中，数据与程序是分离的。为了减少 jar 包的体积，portable 版只含有少量数据。一些高级功能(CRF分词、句法分析等)，需要下载额外的数据包，并通过配置文件将数据包的位置告诉 HanLP。

其中词典是词法分析必需的，模型是句法分析必需的。

如果已经安装了 pyhanlp，则数据包和配置文件已经安装就绪。可以通过以下命令获取：

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hanlp -v

结果如下

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jar  1.8.3: /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/hanlp-1.8.3.jar
data 1.8.3: /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data
config    : /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/hanlp.properties

一些资源，例如模型，数据包，配置文件等，需要手动添加。

最后一行的 hanlp.properties 就是所需的配置文件，只需要将它复制到项目的资源目录 src/main/resources 即可；data/model 也放到版本库相应目录。

完成后的目录结构如下：

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|---- data
|     |---- dictionary
|     |---- model
|           |---- dependency
|           |---- perceptron
|           |---- segment
|---- pom.xml
|---- src
      |---- main
      |     |---- java
      |     |---- resources
      |           |---- hanlp.properties
      |---- test

将目录调整成以上形式，并将 properties，data 等所需文件复制到特定位置后，通过调整 pom.xml，然后用 mvn 命令编译运行即可。

也可以不用 maven，而是直接自行设计项目的目录结构，将 hanlp 的 jar 包，properties 文件，data 文件夹等复制进来，然后用 java 命令行进行编译运行也是可以的。

一个具体的运行例子可以看这篇文章：HanLP1-x-Java运行例子。

对于仓库 https://github.com/hankcs/HanLP 来说，《自然语言处理入门》写作时，版本为 1.7.5，可以 git checkout v1.7.5 来恢复到与书对应的版本。书里的代码可以到上面的目录结构中的 src/test/java/ 下的 com/hankcs/book 中找到。此外在 com/hankcs/demo 中还有很多例子，可以照着下面的方法运行。

例子1：下面是书中 ch01 的例子 HelloWord.java：

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/*
 * <author>Han He</author>
 * <email>me@hankcs.com</email>
 * <create-date>2018-05-18 下午5:38</create-date>
 *
 * <copyright file="HelloWord.java">
 * Copyright (c) 2018, Han He. All Rights Reserved, http://www.hankcs.com/
 * This source is subject to Han He. Please contact Han He for more information.
 * </copyright>
 */
package com.hankcs.book.ch01;

import com.hankcs.hanlp.HanLP;

/**
 * 《自然语言处理入门》1.6 开源工具
 * 配套书籍：http://nlp.hankcs.com/book.php
 * 讨论答疑：https://bbs.hankcs.com/
 *
 * @author hankcs
 * @see <a href="http://nlp.hankcs.com/book.php">《自然语言处理入门》</a>
 * @see <a href="https://bbs.hankcs.com/">讨论答疑</a>
 */
public class HelloWord
{
    public static void main(String[] args)
    {
        HanLP.Config.enableDebug();         // 首次运行会自动建立模型缓存，为了避免你等得无聊，开启调试模式说点什么:-)
        System.out.println(HanLP.segment("王国维和服务员"));
    }
}

例子2：下面是 demo 中的 DemoSegment.java

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/*
 * <summary></summary>
 * <author>He Han</author>
 * <email>hankcs.cn@gmail.com</email>
 * <create-date>2014/12/7 19:02</create-date>
 *
 * <copyright file="DemoSegment.java" company="上海林原信息科技有限公司">
 * Copyright (c) 2003-2014, 上海林原信息科技有限公司. All Right Reserved, http://www.linrunsoft.com/
 * This source is subject to the LinrunSpace License. Please contact 上海林原信息科技有限公司 to get more information.
 * </copyright>
 */
package com.hankcs.demo;

import com.hankcs.hanlp.HanLP;
import com.hankcs.hanlp.seg.Segment;
import com.hankcs.hanlp.seg.common.Term;

import java.util.List;

/**
 * 标准分词
 *
 * @author hankcs
 */
public class DemoSegment
{
    public static void main(String[] args)
    {
        String[] testCase = new String[]{
                "商品和服务",
                "当下雨天地面积水分外严重",
                "结婚的和尚未结婚的确实在干扰分词啊",
                "买水果然后来世博园最后去世博会",
                "中国的首都是北京",
                "欢迎新老师生前来就餐",
                "工信处女干事每月经过下属科室都要亲口交代24口交换机等技术性器件的安装工作",
                "随着页游兴起到现在的页游繁盛，依赖于存档进行逻辑判断的设计减少了，但这块也不能完全忽略掉。",
        };
        for (String sentence : testCase)
        {
            List<Term> termList = HanLP.segment(sentence);
            System.out.println(termList);
        }
    }
}

首先我们构建一个运行书中代码和仓库中的示例的项目，目录结果如下

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|---- data
|---- pom.xml
|---- src
      |---- main
      |     |---- java
      |     |     |---- com
      |     |           |---- hankcs
      |     |                 |---- book
      |     |                 |     |---- ch01
      |     |                 |           |---- HelloWord.java
      |     |                 |---- demo
      |     |                       |---- DemoSegment.java
      |     |---- resources
      |           |---- hanlp.properties
      |---- test

pom.xml 如下：

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<?xml version="1.0" encoding="UTF-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
                             http://maven.apache.org/xsd/maven-4.0.0.xsd">

  <modelVersion>4.0.0</modelVersion>
  <groupId>com.hankcs</groupId>
  <artifactId>Hello_Word</artifactId>
  <version>1.0-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>Hello-Word</name>
  <url>https://www.hanlp.com/</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>1.7</maven.compiler.source>
    <maven.compiler.target>1.7</maven.compiler.target>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>com.hankcs</groupId>
      <artifactId>hanlp</artifactId>
      <version>portable-1.8.3</version>
    </dependency>
  </dependencies>

  <build>
    <plugins>
      <plugin>
        <artifactId>maven-compiler-plugin</artifactId>
        <version>3.1</version>
        <configuration>
          <source>10</source>
          <target>10</target>
          <encoding>UTF-8</encoding>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>1.2.1</version>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>shade</goal>
            </goals>
            <configuration>
              <transformers>
                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                  <mainClass>com.hankcs.book.ch01.HelloWord</mainClass>
                  <mainClass>com.hankcs.demo.DemoSegment</mainClass>
                </transformer>
              </transformers>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

下面我们编译运行：

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mvn clean compile

运行书中 ch01/HelloWord.java 的例子

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mvn exec:java -Dexec.mainClass="com.hankcs.book.ch01.HelloWord"

结果如下：

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[INFO] Scanning for projects...                                                                                                                                  [9/1980]
[INFO]                                                                                                                                                                   
[INFO] ------------------< com.hankcs.book.ch01:Hello_Word >-------------------                                                                                          
[INFO] Building Hello-Word 1.0-SNAPSHOT                                                                                                                                  
[INFO] --------------------------------[ jar ]---------------------------------                                                                                          
[INFO]                                                                                                                                                                   
[INFO] --- exec-maven-plugin:3.0.0:java (default-cli) @ Hello_Word ---                                                                                                   
May 31, 2022 11:30:52 PM com.hankcs.hanlp.dictionary.DynamicCustomDictionary loadMainDictionary                                                                          
INFO: 自定义词典开始加载:/home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data/dictionary/custom/CustomDictionary.txt                      
May 31, 2022 11:30:52 PM com.hankcs.hanlp.dictionary.DynamicCustomDictionary load                                                                                        
INFO: 自定义词典加载成功:381940个词条，耗时82ms                                                                                                                          
May 31, 2022 11:30:52 PM com.hankcs.hanlp.dictionary.CoreDictionary load
INFO: 核心词典开始加载:/home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data/dictionary/CoreNatureDictionary.txt
May 31, 2022 11:30:53 PM com.hankcs.hanlp.dictionary.CoreDictionary <clinit>                         
INFO: /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data/dictionary/CoreNatureDictionary.txt加载成功，153091个词条，耗时18ms
粗分词网：                     
0:[ ] 
1:[王, 王国]                                                                   
2:[国]                                 
3:[维, 维和]                                                                   
4:[和, 和服]
5:[服, 服务, 服务员]                                                    
6:[务]                                                 
7:[员]
8:[ ]                                                                               
                                                         
May 31, 2022 11:30:53 PM com.hankcs.hanlp.dictionary.CoreBiGramTableDictionary <clinit>
INFO: 开始加载二元词典/home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data/dictionary/CoreNatureDictionary.ngram.txt.table
May 31, 2022 11:30:53 PM com.hankcs.hanlp.dictionary.CoreBiGramTableDictionary <clinit>
INFO: /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data/dictionary/CoreNatureDictionary.ngram.txt.table加载成功，耗时43ms
粗分结果[王国/n, 维和/vn, 服务员/nnt]                                            
May 31, 2022 11:30:53 PM com.hankcs.hanlp.dictionary.nr.PersonDictionary <clinit>
INFO: /home/ppp/anaconda3/envs/python-3.6/lib/python3.6/site-packages/pyhanlp/static/data/dictionary/person/nr.txt加载成功，耗时81ms
人名角色观察：[  K 1 A 1 ][王国 X 232 L 3 ][维和 L 2 V 1 Z 1 ][服务员 K 14 ][  K 1 A 1 ]
人名角色标注：[ /K ,王国/X ,维和/V ,服务员/K , /K]
识别出人名：王国维 XD                        
细分词网：                                                                     
0:[ ]                                                                                                      
1:[王国, 王国维]                                            
2:[]                                                                                                                                                                     
3:[维和]                                                            
4:[和, 和服]                                                                                                       
5:[服务员]                                                                                           
6:[]                                                                     
7:[]
8:[ ]

[王国维/nr, 和/cc, 服务员/nnt]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time:  0.774 s
[INFO] Finished at: 2022-05-31T23:30:53+08:00
[INFO] ------------------------------------------------------------------------

运行 demo/DemoSegment.java:

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mvn exec:java -Dexec.mainClass="com.hankcs.demo.DemoSegment""

结果如下：

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[INFO] Scanning for projects...
[INFO] 
[INFO] -----------------------< com.hankcs:Hello_Word >------------------------
[INFO] Building Hello-Word 1.0-SNAPSHOT
[INFO] --------------------------------[ jar ]---------------------------------
[INFO] 
[INFO] --- exec-maven-plugin:3.0.0:java (default-cli) @ Hello_Word ---
[商品/n, 和/cc, 服务/vn]
[当/p, 下雨天/n, 地面/n, 积水/n, 分外/d, 严重/a]
[结婚/vi, 的/ude1, 和/cc, 尚未/d, 结婚/vi, 的/ude1, 确实/ad, 在/p, 干扰/vn, 分词/n, 啊/y]
[买/v, 水果/n, 然后/c, 来/vf, 世博园/n, 最后/f, 去/vf, 世博会/n]
[中国/ns, 的/ude1, 首都/n, 是/vshi, 北京/ns]
[欢迎/v, 新/a, 老/a, 师生/n, 前来/vi, 就餐/vi]
[工信处/n, 女干事/n, 每月/t, 经过/p, 下属/v, 科室/n, 都/d, 要/v, 亲口/d, 交代/v, 24/m, 口/n, 交换机/n, 等/udeng, 技术性/n, 器件/n, 的/ude1, 安装/v, 工作/vn]
[随着/p, 页游/nz, 兴起/v, 到/v, 现在/t, 的/ude1, 页游/nz, 繁盛/a, ，/w, 依赖于/v, 存档/vi, 进行/vn, 逻辑/n, 判断/v, 的/ude1, 设计/vn, 减少/v, 了/ule, ，/w, 但/c, 这块/r, 也/d, 不能/v, 完全/ad, 忽略/v, 掉/v, 。/w]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time:  0.708 s
[INFO] Finished at: 2022-05-31T23:40:59+08:00
[INFO] ------------------------------------------------------------------------
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