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\n\nnpm version\n\n\n\nCoverage Status\n\n\n\nCI Status\n\n\n\nMin Zip Size\n\n

\n\n# 中文分词模块\n\n本模块基于 [node-segment](https://github.com/leizongmin/node-segment) 魔改，增加了 electron、浏览器支持，并准备针对 electron 多线程运行环境进行优化。\n\n之所以要花时间魔改，是因为 `segment` 和 `nodejieba` 虽然在 node 环境下很好用，但根本无法在浏览器和 electron 环境下运行。我把代码重构为 ES2015，并用 babel 插件内联了字典文件，全部载入的话大小是 3.8M，但如果有些字典你并不需要，字典和模块是支持 tree shaking 的（请使用 ESM 模块）。\n\n## Usage\n\n```javascript\nimport { Segment, useDefault } from 'segmentit';\n\nconst segmentit = useDefault(new Segment());\nconst result = segmentit.doSegment('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。');\nconsole.log(result);\n```\n\n对于 runkit 环境：\n\n```javascript\nconst { Segment, useDefault } = require('segmentit');\nconst segmentit = useDefault(new Segment());\nconst result = segmentit.doSegment('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。');\nconsole.log(result);\n```\n\n[在 Runkit 上免费试用](https://npm.runkit.com/segmentit)\n\n## 获取词类标注\n\n结巴分词风格的词类标注：\n\n```javascript\n// import Segment, { useDefault, cnPOSTag, enPOSTag } from 'segmentit';\nconst = require('segmentit').default;\nconst { Segment, useDefault, cnPOSTag, enPOSTag } = require('segmentit');\n\nconst segmentit = useDefault(new Segment());\n\nconsole.log(segmentit.doSegment('一人得道，鸡犬升天').map(i => `${i.w} <${cnPOSTag(i.p)}> <${enPOSTag(i.p)}>`))\n// ↑ [\"一人得道 <习语,数词 数语素> \", \"， <标点符号> \", \"鸡犬升天 <成语> \"]\n```\n\n## 只使用部分词典或使用自定义词典\n\nuseDefault 的具体实现是这样的：\n\n```javascript\n// useDefault\nimport { Segment, modules, dicts, synonyms, stopwords } from 'segmentit';\n\nconst segmentit = new Segment();\nsegmentit.use(modules);\nsegmentit.loadDict(dicts);\nsegmentit.loadSynonymDict(synonyms);\nsegmentit.loadStopwordDict(stopwords);\n```\n\n因此你实际上可以 import 所需的那部分字典和模块，然后一个个如下载入。没有 import 的那些字典和模块应该会被 webpack 的 tree shaking 去掉。你也可以这样载入自己定义的字典文件，只需要主要 loadDict 的函数签名是 `(dicts: string | string[]): Segment`。\n\n```javascript\n// load custom module and dicts\nimport {\n Segment,\n ChsNameTokenizer,\n DictOptimizer,\n EmailOptimizer,\n PunctuationTokenizer,\n URLTokenizer,\n ChsNameOptimizer,\n DatetimeOptimizer,\n DictTokenizer,\n ForeignTokenizer,\n SingleTokenizer,\n WildcardTokenizer,\n pangu,\n panguExtend1,\n panguExtend2,\n names,\n wildcard,\n synonym,\n stopword,\n} from 'segmentit';\n\nconst segmentit = new Segment();\n\n// load them one by one, or by array\nsegmentit.use(ChsNameTokenizer);\nsegmentit.loadDict(pangu);\nsegmentit.loadDict([panguExtend1, panguExtend2]);\nsegmentit.loadSynonymDict(synonym);\nsegmentit.loadStopwordDict(stopword);\n```\n\n盘古的词典比较复古了，像「软萌萝莉」这种词都是没有的，请有能力的朋友 PR 一下自己的词库。\n\n## 创造自己的分词中间件（Tokenizer）和结果优化器（Optimizer）\n\n### Tokenizer\n\nTokenizer 是分词时要经过的一个个中间件，类似于 Redux 的 MiddleWare，它的 split 函数接受分词分到一半的 token 数组，返回一个同样格式的 token 数组（这也就是不要对太长的文本分词的原因，不然这个数组会巨爆大）。\n\n例子如下：\n\n```javascript\n// @flow\nimport { Tokenizer } from 'segmentit';\nimport type { SegmentToken, TokenStartPosition } from 'segmentit';\nexport default class ChsNameTokenizer extends Tokenizer {\n split(words: Array): Array {\n // 可以获取到 this.segment 里的各种信息\n const POSTAG = this.segment.POSTAG;\n const TABLE = this.segment.getDict('TABLE');\n // ...\n }\n```\n\n### Optimizer\n\nOptimizer 是在分词结束后，发现有些难以利用字典处理的情况，却可以用启发式规则处理时，可以放这些启发式规则的地方，它的 doOptimize 函数同样接收一个 token 数组，返回一个同样格式的 token 数组。\n\n除了 token 数组以外，你还可以自定义余下的参数，比如在下面的例子里，我们会递归调用自己一次，通过第二个参数判断递归深度：\n\n```javascript\n// @flow\nimport { Optimizer } from './BaseModule';\nimport type { SegmentToken } from './type';\nexport default class DictOptimizer extends Optimizer {\n doOptimize(words: Array, isNotFirst: boolean): Array {\n // 可以获取到 this.segment 里的各种信息\n const POSTAG = this.segment.POSTAG;\n const TABLE = this.segment.getDict('TABLE');\n // ...\n // 针对组合数字后无法识别新组合的数字问题，需要重新扫描一次\n return isNotFirst === true ? words : this.doOptimize(words, true);\n }\n```\n\n例如目前各种分词工具都没法把「一条红色内裤」中的红色标对词性，但在 segmentit 里我加了个简单的 AdjectiveOptimizer 来处理它：\n\n```javascript\n// @flow\n// https://github.com/linonetwo/segmentit/blob/master/src/module/AdjectiveOptimizer.js\nimport { Optimizer } from './BaseModule';\nimport type { SegmentToken } from './type';\n\nimport { colors } from './COLORS';\n\n// 把一些错认为名词的词标注为形容词，或者对名词作定语的情况\nexport default class AdjectiveOptimizer extends Optimizer {\n doOptimize(words: Array): Array {\n const { POSTAG } = this.segment;\n let index = 0;\n while (index < words.length) {\n const word = words[index];\n const nextword = words[index + 1];\n if (nextword) {\n // 对于<颜色>+<的>，直接判断颜色是形容词（字典里颜色都是名词）\n if (nextword.p === POSTAG.D_U && colors.includes(word.w)) {\n word.p = POSTAG.D_A;\n }\n // 如果是连续的两个名词，前一个是颜色，那这个颜色也是形容词\n if (word.p === POSTAG.D_N && nextword.p === POSTAG.D_N && colors.includes(word.w)) {\n word.p = POSTAG.D_A;\n }\n }\n // 移到下一个单词\n index += 1;\n }\n return words;\n }\n}\n```\n\n## License\n\nMIT LICENSED\n","_attachments":{},"homepage":"https://github.com/linonetwo/segmentit#readme","bugs":{"url":"https://github.com/linonetwo/segmentit/issues"}}