| 1 |
- {"version":3,"file":"segmentit.js","sources":["../../src/POSTAG.js","../../src/module/BaseModule.js","../../src/Tokenizer.js","../../src/Optimizer.js","../../src/Segment.js","../../src/module/COLORS.js","../../src/module/AdjectiveOptimizer.js","../../src/module/CHS_NAMES.js","../../src/module/ChsNameTokenizer.js","../../src/module/DictOptimizer.js","../../src/module/EmailOptimizer.js","../../src/module/PunctuationTokenizer.js","../../src/module/URLTokenizer.js","../../src/module/ChsNameOptimizer.js","../../src/module/DatetimeOptimizer.js","../../src/module/DictTokenizer.js","../../src/module/ForeignTokenizer.js","../../src/module/SingleTokenizer.js","../../src/module/WildcardTokenizer.js","../../src/module/index.js","../../src/knowledge/index.js","../../src/index.js"],"sourcesContent":["// @flow\n\n/**\n * 单词类型\n */\n\nconst POSTAG = {\n D_A: 0x40000000, // 形容词 形语素\n D_B: 0x20000000, // 区别词 区别语素\n D_C: 0x10000000, // 连词 连语素\n D_D: 0x08000000, // 副词 副语素\n D_E: 0x04000000, // 叹词 叹语素\n D_F: 0x02000000, // 方位词 方位语素\n D_I: 0x01000000, // 成语\n D_L: 0x00800000, // 习语\n A_M: 0x00400000, // 数词 数语素\n D_MQ: 0x00200000, // 数量词\n D_N: 0x00100000, // 名词 名语素\n D_O: 0x00080000, // 拟声词\n D_P: 0x00040000, // 介词\n A_Q: 0x00020000, // 量词 量语素\n D_R: 0x00010000, // 代词 代语素\n D_S: 0x00008000, // 处所词\n D_T: 0x00004000, // 时间词\n D_U: 0x00002000, // 助词 助语素\n D_V: 0x00001000, // 动词 动语素\n D_W: 0x00000800, // 标点符号\n D_X: 0x00000400, // 非语素字\n D_Y: 0x00000200, // 语气词 语气语素\n D_Z: 0x00000100, // 状态词\n A_NR: 0x00000080, // 人名\n A_NS: 0x00000040, // 地名\n A_NT: 0x00000020, // 机构团体\n A_NX: 0x00000010, // 外文字符\n A_NZ: 0x00000008, // 其他专名\n D_ZH: 0x00000004, // 前接成分\n D_K: 0x00000002, // 后接成分\n UNK: 0x00000000, // 未知词性\n URL: 0x00000001, // 网址、邮箱地址\n};\n\nconst CN_POS_NAMES = {\n D_A: '形容词 形语素',\n D_B: '区别词 区别语素',\n D_C: '连词 连语素',\n D_D: '副词 副语素',\n D_E: '叹词 叹语素',\n D_F: '方位词 方位语素',\n D_I: '成语',\n D_L: '习语',\n A_M: '数词 数语素',\n D_MQ: '数量词',\n D_N: '名词 名语素',\n D_O: '拟声词',\n D_P: '介词',\n A_Q: '量词 量语素',\n D_R: '代词 代语素',\n D_S: '处所词',\n D_T: '时间词',\n D_U: '助词 助语素',\n D_V: '动词 动语素',\n D_W: '标点符号',\n D_X: '非语素字',\n D_Y: '语气词 语气语素',\n D_Z: '状态词',\n A_NR: '人名',\n A_NS: '地名',\n A_NT: '机构团体',\n A_NX: '外文字符',\n A_NZ: '其他专名',\n D_ZH: '前接成分',\n D_K: '后接成分',\n UNK: '未知',\n URL: '网址 邮箱地址',\n};\n\nconst EN_POS_NAMES = {\n D_A: 'a',\n D_B: 'b',\n D_C: 'c',\n D_D: 'd',\n D_E: 'e',\n D_F: 'f',\n D_I: 'i',\n D_L: 'l',\n A_M: 'm',\n D_MQ: 'mq',\n D_N: 'n',\n D_O: 'o',\n D_P: 'p',\n A_Q: 'q',\n D_R: 'r',\n D_S: 's',\n D_T: 't',\n D_U: 'u',\n D_V: 'v',\n D_W: 'w',\n D_X: 'x',\n D_Y: 'y',\n D_Z: 'z',\n A_NR: 'nr',\n A_NS: 'ns',\n A_NT: 'nt',\n A_NX: 'nx',\n A_NZ: 'nz',\n D_ZH: 'h',\n D_K: 'k',\n UNK: 'un',\n URL: 'uri',\n};\n\nexport function getPOSTagTranslator(POSTagDict: Object, I18NDict: Object): string {\n return (posTagNumber: number): string => {\n if (isNaN(posTagNumber)) {\n return I18NDict[posTagNumber] || I18NDict.UNK;\n }\n const result = [];\n for (const key in POSTagDict) {\n if ((posTagNumber & POSTagDict[key]) > 0) {\n result.push(I18NDict[key]);\n }\n }\n if (result.length < 1) {\n return I18NDict.UNK;\n }\n return result.toString();\n };\n}\n\nexport const cnPOSTag = getPOSTagTranslator(POSTAG, CN_POS_NAMES);\nexport const enPOSTag = getPOSTagTranslator(POSTAG, EN_POS_NAMES);\n\nexport default POSTAG;\n","// @flow\nimport Segment from '../Segment';\n\nexport class Module {\n type: 'optimizer' | 'tokenizer';\n\n constructor(segment: Segment) {\n this.segment = segment;\n }\n}\n\nexport class Tokenizer extends Module {\n type = 'tokenizer';\n}\n\nexport class Optimizer extends Module {\n type = 'optimizer';\n}\n","// @flow\nimport type { SegmentToken } from './module/type';\n\nexport default class Tokenizer {\n /**\n * 分词模块管理器\n *\n * @param {Segment} 分词接口\n */\n constructor(segment) {\n this.segment = segment;\n }\n\n /**\n * 对一段文本进行分词\n *\n * @param {string} text 文本\n * @param {array} modules 分词模块数组\n * @return {array}\n */\n split(text, modules): SegmentToken[] {\n if (modules.length < 1) {\n throw Error('No tokenizer module!');\n }\n // 按顺序分别调用各个module来进行分词 ： 各个module仅对没有识别类型的单词进行分词\n let result = [{ w: text }];\n modules.forEach(module => {\n result = module.split(result);\n });\n return result;\n }\n}\n","// @flow\nimport type { SegmentToken } from './module/type';\n\nexport default class Optimizer {\n /**\n * 优化模块管理器\n *\n * @param {Segment} 分词接口\n */\n constructor(segment) {\n this.segment = segment;\n }\n\n /**\n * 分词一段文本\n *\n * @param {array} words 单词数组\n * @param {array} modules 分词模块数组\n * @return {array}\n */\n doOptimize(words, modules): SegmentToken[] {\n let result = [...words];\n // 按顺序分别调用各个module来进行分词，各个module仅对没有识别类型的单词进行分词\n modules.forEach(module => {\n result = module.doOptimize(result);\n });\n return result;\n }\n}\n","// @flow\n/**\n * 分词器接口\n *\n * @author 老雷<leizongmin@gmail.com>\n * @author 林一二<linonetwo012@gmail.com>\n */\n\nimport POSTAG from './POSTAG';\nimport Tokenizer from './Tokenizer';\nimport Optimizer from './Optimizer';\n\n/**\n * 创建分词器接口\n */\nexport default class Segment {\n constructor() {\n this.POSTAG = POSTAG; // 词性\n this.DICT = {}; // 词典表\n this.modules = {\n tokenizer: [], // 分词模块\n optimizer: [], // 优化模块\n };\n this.tokenizer = new Tokenizer(this);\n this.optimizer = new Optimizer(this);\n }\n\n /**\n * 载入分词模块\n *\n * @param {String|Array|Object} module 模块名称(数组)或模块对象\n * @return {Segment}\n */\n use = (Module: Array | Object): Segment => {\n // 传入列表的话就递归调用\n if (Array.isArray(Module)) {\n Module.forEach(this.use);\n } else {\n // 初始化并注册模块\n if (typeof Module.init === 'function') {\n Module.init(this);\n this.modules[Module.type].push(Module);\n } else {\n const module = new Module(this);\n this.modules[module.type].push(module);\n }\n }\n\n return this;\n };\n\n /**\n * 载入字典文件\n *\n * @param {String} name 字典文件名\n * @param {String} type 类型\n * @param {Boolean} convertToLower 是否全部转换为小写\n * @return {Segment}\n */\n loadDict = (\n dict: string | string[],\n type = 'TABLE',\n convertToLower: boolean = false,\n ): Segment => {\n if (Array.isArray(dict)) {\n dict.forEach(d => this.loadDict(d));\n } else {\n // 初始化词典\n if (!this.DICT[type]) this.DICT[type] = {};\n if (!this.DICT[`${type}2`]) this.DICT[`${type}2`] = {};\n const TABLE = this.DICT[type]; // 词典表 '词' => {属性}\n const TABLE2 = this.DICT[`${type}2`]; // 词典表 '长度' => '词' => 属性\n // 导入数据\n dict\n .split(/\\r?\\n/)\n .map(line => {\n if (convertToLower) return line.toLowerCase();\n return line;\n })\n .forEach(line => {\n const blocks = line.split('|');\n if (blocks.length > 2) {\n const w = blocks[0].trim();\n const p = Number(blocks[1]);\n const f = Number(blocks[2]);\n\n // 一定要检查单词是否为空，如果为空会导致Bug\n if (w.length > 0) {\n TABLE[w] = { f, p };\n if (!TABLE2[w.length]) TABLE2[w.length] = {};\n TABLE2[w.length][w] = TABLE[w];\n }\n }\n });\n }\n\n return this;\n };\n\n /**\n * 取词典表\n *\n * @param {String} type 类型\n * @return {object}\n */\n getDict = (type: string) => this.DICT[type];\n\n /**\n * 载入同义词词典\n *\n * @param {Object} dict 字典文件\n */\n loadSynonymDict = (dict: string | string[]): Segment => {\n if (Array.isArray(dict)) {\n dict.forEach(d => this.loadSynonymDict(d));\n } else {\n const type = 'SYNONYM';\n\n // 初始化词典\n if (!this.DICT[type]) this.DICT[type] = {};\n const TABLE = this.DICT[type]; // 词典表 '同义词' => '标准词'\n // 导入数据\n dict\n .split(/\\r?\\n/)\n .map(line => line.split(','))\n .forEach(blocks => {\n if (blocks.length > 1) {\n const n1 = blocks[0].trim();\n const n2 = blocks[1].trim();\n TABLE[n1] = n2;\n if (TABLE[n2] === n1) {\n delete TABLE[n2];\n }\n }\n });\n }\n\n return this;\n };\n\n /**\n * 载入停止符词典\n *\n * @param {Object} dict 字典文件\n */\n loadStopwordDict = (dict: string | string[]): Segment => {\n if (Array.isArray(dict)) {\n dict.forEach(d => this.loadStopwordDict(d));\n } else {\n const type = 'STOPWORD';\n\n // 初始化词典\n if (!this.DICT[type]) this.DICT[type] = {};\n const TABLE = this.DICT[type]; // 词典表 '同义词' => '标准词'\n // 导入数据\n dict\n .split(/\\r?\\n/)\n .map(line => line.trim())\n .forEach(line => {\n if (line) {\n TABLE[line] = true;\n }\n });\n }\n\n return this;\n };\n\n /**\n * 开始分词\n *\n * @param {String} text 文本\n * @param {Object} options 选项\n * - {Boolean} simple 是否仅返回单词内容\n * - {Boolean} stripPunctuation 去除标点符号\n * - {Boolean} convertSynonym 转换同义词\n * - {Boolean} stripStopword 去除停止符\n * @return {Array}\n */\n doSegment = (text, options) => {\n const me = this;\n options = options || {};\n let ret = [];\n\n // 将文本按照换行符分割成多段，并逐一分词\n text\n .replace(/\\r/g, '\\n')\n // 用换行符和空格把长文本切小，以减小传入中间件的数组长度\n .split(/\\n+/)\n .forEach(section => {\n var section = section.trim();\n if (section.length < 1) return;\n // ======================================\n // 分词\n let sret = me.tokenizer.split(section, me.modules.tokenizer);\n\n // 优化\n sret = me.optimizer.doOptimize(sret, me.modules.optimizer);\n\n // ======================================\n // 连接分词结果\n if (sret.length > 0) ret = ret.concat(sret);\n });\n\n // 去除标点符号\n if (options.stripPunctuation) {\n ret = ret.filter(item => item.p !== POSTAG.D_W);\n }\n\n // 转换同义词\n function convertSynonym(list) {\n let count = 0;\n const TABLE = me.getDict('SYNONYM');\n list = list.map(item => {\n if (item.w in TABLE) {\n count++;\n return { w: TABLE[item.w], p: item.p };\n }\n return item;\n });\n return { count, list };\n }\n if (options.convertSynonym) {\n do {\n var result = convertSynonym(ret);\n ret = result.list;\n } while (result.count > 0);\n }\n\n // 去除停止符\n if (options.stripStopword) {\n const STOPWORD = me.getDict('STOPWORD');\n ret = ret.filter(item => !(item.w in STOPWORD));\n }\n\n // 仅返回单词内容\n if (options.simple) {\n ret = ret.map(item => item.w);\n }\n\n return ret;\n };\n\n /**\n * 将单词数组连接成字符串\n *\n * @param {Array} words 单词数组\n * @return {String}\n */\n toString(words) {\n return words.map(item => item.w).join('');\n }\n\n /**\n * 根据某个单词或词性来分割单词数组\n *\n * @param {Array} words 单词数组\n * @param {Number|String} s 用于分割的单词或词性\n * @return {Array}\n */\n split(words, s) {\n const ret = [];\n let lasti = 0;\n let i = 0;\n const f = typeof s === 'string' ? 'w' : 'p';\n\n while (i < words.length) {\n if (words[i][f] === s) {\n if (lasti < i) ret.push(words.slice(lasti, i));\n ret.push(words.slice(i, i + 1));\n i++;\n lasti = i;\n } else {\n i++;\n }\n }\n if (lasti < words.length - 1) {\n ret.push(words.slice(lasti, words.length));\n }\n\n return ret;\n }\n\n /**\n * 在单词数组中查找某一个单词或词性所在的位置\n *\n * @param {Array} words 单词数组\n * @param {Number|String} s 要查找的单词或词性\n * @param {Number} cur 开始位置\n * @return {Number} 找不到，返回-1\n */\n indexOf(words, s, cur) {\n cur = isNaN(cur) ? 0 : cur;\n const f = typeof s === 'string' ? 'w' : 'p';\n\n while (cur < words.length) {\n if (words[cur][f] === s) return cur;\n cur++;\n }\n\n return -1;\n }\n}\n","// @flow\n\nexport const COLOR_WITH_RGB: string[][] = [\n ['薰衣草紫红', '#fff0f5', '255,245,245'],\n ['淡藕合', '#f0dfee', '240,238,238'],\n ['甘石粉', '#ffe8f3', '255,243,243'],\n ['浅粉红', '#ffd9e6', '255,230,230'],\n ['浅血牙', '#ffd7ea', '255,234,234'],\n ['粉凤仙', '#fccce6', '252,230,230'],\n ['浅珍珠红', '#ffb3e6', '255,230,230'],\n ['淡粉', '#ffb3cc', '255,204,204'],\n ['火鹤红', '#e68ab8', '230,184,184'],\n ['豆棕', '#502e3e', '80,62,62'],\n ['茄皮紫', '#3a212f', '58,47,47'],\n ['蒲萄灰', '#4e2c3f', '78,63,63'],\n ['荷花', '#eb7faf', '235,175,175'],\n ['灰紫红', '#db7093', '219,147,147'],\n ['浅珊瑚红', '#ff80bf', '255,191,191'],\n ['暗粉', '#ff77bb', '255,187,187'],\n ['陈玫红', '#b85798', '184,152,152'],\n ['肉红', '#d26091', '210,145,145'],\n ['尖晶石红', '#ff73b3', '255,179,179'],\n ['银红', '#c85b92', '200,146,146'],\n ['暖粉红', '#ff69b4', '255,180,180'],\n ['浅玫瑰红', '#ff66cc', '255,204,204'],\n ['紫扇贝色', '#923a60', '146,96,96'],\n ['长春花色', '#ff47d1', '255,209,209'],\n ['山茶红', '#e63995', '230,149,149'],\n ['中青紫红', '#c71585', '199,133,133'],\n ['深粉红', '#ff1493', '255,147,147'],\n ['洋玫瑰红', '#ff0da6', '255,166,166'],\n ['玫瑰红', '#ff007f', '255,127,127'],\n ['品红', '#f400a1', '244,161,161'],\n ['胭脂红', '#e6005c', '230,92,92'],\n ['艶粉', '#e6005c', '230,92,92'],\n ['红宝石色', '#cc0080', '204,128,128'],\n ['枢机红', '#990036', '153,54,54'],\n ['毅红', '#8a002e', '138,46,46'],\n ['牵牛紫', '#800040', '128,64,64'],\n ['紫粉', '#80002b', '128,43,43'],\n ['鶏冠花红', '#660033', '102,51,51'],\n ['枣红', '#640021', '100,33,33'],\n ['紫绛色', '#51001b', '81,27,27'],\n ['玫瑰灰', '#4d0026', '77,38,38'],\n ['勃艮第酒红', '#470024', '71,36,36'],\n ['黒紫', '#3e0014', '62,20,20'],\n ['雪紫', '#33001a', '51,26,26'],\n ['深红', '#33001a', '51,26,26'],\n ['雪色', '#fffafa', '255,250,250'],\n ['浅肉粉', '#f5eeeb', '245,235,235'],\n ['粉棕', '#ebdad3', '235,211,211'],\n ['雾玫瑰色', '#ffe4e1', '255,225,225'],\n ['米红', '#fddfdf', '253,223,223'],\n ['沙棕', '#e6c3c3', '230,195,195'],\n ['肉粉', '#ffd7d7', '255,215,215'],\n ['奶棕', '#deb6b6', '222,182,182'],\n ['玫瑰褐', '#bc8f8f', '188,143,143'],\n ['红灰莲', '#ab8282', '171,130,130'],\n ['粉红', '#ffc0cb', '255,203,203'],\n ['亮粉红', '#ffb6c1', '255,193,193'],\n ['壳黄红', '#ffb3bf', '255,191,191'],\n ['浅粉', '#ffb3b3', '255,179,179'],\n ['十様锦', '#ffb3b3', '255,179,179'],\n ['深烟', '#311f1e', '49,30,30'],\n ['凤仙粉', '#ff9393', '255,147,147'],\n ['亮珊瑚色', '#f08080', '240,128,128'],\n ['暗鲑红', '#e9967a', '233,122,122'],\n ['绛紫', '#492525', '73,37,37'],\n ['浅鲑红', '#ff8099', '255,153,153'],\n ['紫铜色', '#6b3636', '107,54,54'],\n ['珈琲色', '#623131', '98,49,49'],\n ['烟栗色', '#221311', '34,17,17'],\n ['亮鲑红', '#ffa07a', '255,122,122'],\n ['红莲灰', '#c76079', '199,121,121'],\n ['鲑红', '#fa8072', '250,114,114'],\n ['印度红', '#cd5c5c', '205,92,92'],\n ['银朱', '#c85554', '200,84,84'],\n ['赦红', '#d1634b', '209,75,75'],\n ['二硃', '#c24545', '194,69,69'],\n ['芯木色', '#c14444', '193,68,68'],\n ['銹红', '#4d1919', '77,25,25'],\n ['珊瑚红', '#ff7f50', '255,80,80'],\n ['辰沙', '#ce3b3b', '206,59,59'],\n ['红扇贝色', '#ce3b3b', '206,59,59'],\n ['蕃茄红', '#ff6347', '255,71,71'],\n ['柿子橙', '#ff4d40', '255,64,64'],\n ['妃红', '#b8542e', '184,46,46'],\n ['甎红', '#ab2b2b', '171,43,43'],\n ['褐色', '#a52a2a', '165,42,42'],\n ['硃砂', '#972626', '151,38,38'],\n ['土红', '#bc2e2e', '188,46,46'],\n ['樱桃红', '#de3163', '222,99,99'],\n ['砖红色', '#b22222', '178,34,34'],\n ['茜红', '#e32636', '227,54,54'],\n ['银星海棠', '#f52443', '245,67,67'],\n ['橙色', '#fe621f', '254,31,31'],\n ['桔红', '#ff441a', '255,26,26'],\n ['柿子色', '#ff531a', '255,26,26'],\n ['绯红', '#dc143c', '220,60,60'],\n ['月季红', '#e10b2b', '225,43,43'],\n ['鸟罗松红', '#c80926', '200,38,38'],\n ['蕉红色', '#d70419', '215,25,25'],\n ['艶红', '#bf0417', '191,23,23'],\n ['烟色', '#2e0a01', '46,1,1'],\n ['牡丹红', '#b80233', '184,51,51'],\n ['榴花红', '#b02502', '176,2,2'],\n ['枣紫', '#540101', '84,1,1'],\n ['红色', '#ff0000', '255,0,0'],\n ['腥红', '#ff2400', '255,0,0'],\n ['橙红', '#ff4500', '255,0,0'],\n ['朱红', '#ff4d00', 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'#fee3d1', '254,209,209'],\n ['杏仁白', '#ffebcd', '255,205,205'],\n ['柠檬绸色', '#fffacd', '255,205,205'],\n ['乳白色', '#fef3c9', '254,201,201'],\n ['淡黄', '#fef8c9', '254,201,201'],\n ['真丝紬色', '#ffe3c8', '255,200,200'],\n ['肉黄', '#fce2c4', '252,196,196'],\n ['芦黄', '#ebd2b8', '235,184,184'],\n ['陶坯黄', '#ffe4c4', '255,196,196'],\n ['深银灰', '#c7bd9a', '199,154,154'],\n ['承徳梍', '#474336', '71,54,54'],\n ['粉扑桃色', '#ffdab9', '255,185,185'],\n ['小麦色', '#f5deb3', '245,179,179'],\n ['乳黄色', '#feefb6', '254,182,182'],\n ['小鸠黄', '#efe7ab', '239,171,171'],\n ['鹿皮鞋色', '#ffe4b5', '255,181,181'],\n ['桃色', '#ffe5b4', '255,180,180'],\n ['牙黄', '#fef4b4', '254,180,180'],\n ['富春纺色', '#fef4b4', '254,180,180'],\n ['乳灰', '#cfc592', '207,146,146'],\n ['灰米', '#c4be8c', '196,140,140'],\n ['庭院瓦灰色', '#706750', '112,80,80'],\n ['肉棕', '#debf9a', '222,154,154'],\n ['灰土色', '#ccb38c', '204,140,140'],\n ['那瓦霍白', '#ffdead', '255,173,173'],\n ['甘草黄', '#eddd9e', '237,158,158'],\n ['日晒色', '#d2b48c', '210,140,140'],\n ['纸棕', '#d2b38c', '210,140,140'],\n ['相思灰', '#4a4331', '74,49,49'],\n ['蝶粉', '#ffd0a2', '255,162,162'],\n ['乳棕', '#c9b481', '201,129,129'],\n ['燋茶色', '#3d3a27', '61,39,39'],\n ['浅驼色', '#d7af88', '215,136,136'],\n ['中驼', '#ad876b', '173,107,107'],\n ['玛瑙粉', '#ffbc9b', '255,155,155'],\n ['硬木色', '#deb887', '222,135,135'],\n ['土布色', '#fdd599', '253,153,153'],\n ['骆驼色', '#dcb183', '220,131,131'],\n ['亮卡其色', '#f0e68c', '240,140,140'],\n ['中棕灰', '#695f3d', '105,61,61'],\n ['淡棕茶', '#c0a86d', '192,109,109'],\n ['草灰色', '#3d3123', '61,35,35'],\n ['肉棕灰', '#cd9b69', '205,105,105'],\n ['珈琲棕', '#705438', '112,56,56'],\n ['深栗色', '#402f20', '64,32,32'],\n ['深褐色', '#251c12', '37,18,18'],\n ['黄棕色', '#b39255', '179,85,85'],\n ['杏黄', '#e69966', '230,102,102'],\n ['獣皮色', '#c69c57', '198,87,87'],\n ['驼色', '#a16b47', '161,71,71'],\n ['卡其黄', '#cda456', '205,86,86'],\n ['蜜橙', '#ffb366', '255,102,102'],\n ['茉莉黄', '#e6c35c', '230,92,92'],\n ['红孤色', '#c98150', '201,80,80'],\n ['沙褐', '#f4a460', '244,96,96'],\n ['蛤蜊粉', 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['粉橙色', '#ff7b24', '255,36,36'],\n ['巧克力色', '#d2691e', '210,30,30'],\n ['鞍褐', '#8b4513', '139,19,19'],\n ['选金', '#e9c61f', '233,31,31'],\n ['库金', '#e0b712', '224,18,18'],\n ['玛瑙色', '#b1470e', '177,14,14'],\n ['黄金色', '#dab710', '218,16,16'],\n ['琉璃色', '#c9780c', '201,12,12'],\n ['暗金菊色', '#b8860b', '184,11,11'],\n ['坛香色', '#d57306', '213,6,6'],\n ['苍黄', '#c65306', '198,6,6'],\n ['艶黄', '#fc8705', '252,5,5'],\n ['雄精', '#d94f04', '217,4,4'],\n ['姜黄', '#806102', '128,2,2'],\n ['朱古力色', '#381801', '56,1,1'],\n ['蛋黄', '#e6a202', '230,2,2'],\n ['黄琉璃', '#e4b803', '228,3,3'],\n ['枝黄', '#cfb603', '207,3,3'],\n ['鹿皮棕', '#a67902', '166,2,2'],\n ['阳橙', '#ff7300', '255,0,0'],\n ['暗橙', '#ff8c00', '255,0,0'],\n ['万寿菊黄', '#ff9900', '255,0,0'],\n ['琥珀色', '#ffbf00', '255,0,0'],\n ['金色', '#ffd700', '255,0,0'],\n ['石黄', '#f9bb00', '249,0,0'],\n ['鹅黄', '#f8b500', '248,0,0'],\n ['橘色', '#f28500', '242,0,0'],\n ['桔黄', '#f08300', '240,0,0'],\n ['硃膘', '#eb6101', '235,1,1'],\n ['土黄', '#e88b00', '232,0,0'],\n ['铬黄', 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'102,89,89'],\n ['叶绿', '#73b839', '115,57,57'],\n ['嫩绿', '#99ff4d', '153,77,77'],\n ['常春藤绿', '#36bf36', '54,54,54'],\n ['柠檬绿', '#32cd32', '50,50,50'],\n ['森林绿', '#228b22', '34,34,34'],\n ['鹦鹉緑', '#156a1e', '21,30,30'],\n ['孔雀石绿', '#22c32e', '34,46,46'],\n ['双緑', '#043403', '4,3,3'],\n ['明绿', '#66ff00', '102,0,0'],\n ['鲜绿色', '#00ff00', '0,0,0'],\n ['绿色', '#008000', '0,0,0'],\n ['暗绿', '#006400', '0,0,0'],\n ['鉄灰', '#3e403f', '62,63,63'],\n ['薄荷奶油色', '#f5fffa', '245,250,250'],\n ['深瓦灰', '#717774', '113,116,116'],\n ['大理石色', '#c9d8cd', '201,205,205'],\n ['淡灰緑', '#c0d6cb', '192,203,203'],\n ['粗晶梍', '#323836', '50,54,54'],\n ['枝緑', '#dffff5', '223,245,245'],\n ['淡緑', '#dbfbe3', '219,227,227'],\n ['青虾色', '#6f847d', '111,125,125'],\n ['浅水緑', '#ccfbea', '204,234,234'],\n ['蟹青', '#6f9388', '111,136,136'],\n ['油緑', '#2b3c2f', '43,47,47'],\n ['三緑', '#b3ffbc', '179,188,188'],\n ['苍色', '#a6ffcc', '166,204,204'],\n ['墨緑', '#1e3124', '30,36,36'],\n ['老緑', '#2d5131', '45,49,49'],\n ['碧蓝色', '#7fffd4', '127,212,212'],\n ['青瓷绿', '#73e68c', '115,140,140'],\n ['中碧蓝色', '#66cdaa', '102,170,170'],\n ['玉緑', '#6ff9c1', '111,193,193'],\n ['夜緑色', '#285b41', '40,65,65'],\n ['冬緑', '#337d56', '51,86,86'],\n ['碧绿', '#50c878', '80,120,120'],\n ['緑琉璃色', '#296939', '41,57,57'],\n ['中海绿', '#3cb371', '60,113,113'],\n ['绿松石绿', '#4de680', '77,128,128'],\n ['冬瓜緑', '#349e69', '52,105,105'],\n ['海绿', '#2e8b57', '46,87,87'],\n ['铬绿', '#127436', '18,54,54'],\n ['薄荷绿', '#16982b', '22,43,43'],\n ['粗孔雀緑', '#028255', '2,85,85'],\n ['深緑宝石', '#02774f', '2,79,79'],\n ['巴黎緑', '#02b340', '2,64,64'],\n ['翡翠', '#015437', '1,55,55'],\n ['春绿', '#00ff80', '0,128,128'],\n ['中春绿色', '#00fa9a', '0,154,154'],\n ['孔雀绿', '#00a15c', '0,92,92'],\n ['正灰', '#bcc7c7', '188,199,199'],\n ['鸭蛋青', '#e6fffd', '230,253,253'],\n ['浅青色', '#e0f3f8', '224,248,248'],\n ['月白色', '#daf3ef', '218,239,239'],\n ['云灰', '#cbe2e4', '203,228,228'],\n ['亮青', '#e0ffff', '224,255,255'],\n ['蓝灰色', '#8ba39e', '139,158,158'],\n ['春蓝', '#98b4b3', '152,179,179'],\n ['石板色', '#4a595b', '74,91,91'],\n ['婴儿粉蓝', '#b0e0e6', '176,230,230'],\n ['水色', '#afdfe4', '175,228,228'],\n ['亮蓝', '#add8e6', '173,230,230'],\n ['灰绿松石色', '#afeeee', '175,238,238'],\n ['浅鲜緑', '#b6fee9', '182,233,233'],\n ['鸠灰', '#9ec8da', '158,218,218'],\n ['湖蓝灰', '#425c60', '66,96,96'],\n ['蟹蓝', '#597e82', '89,130,130'],\n ['灰蓝', '#477066', '71,102,102'],\n ['黛蓝', '#131e1e', '19,30,30'],\n ['果灰', '#7ecdb6', '126,182,182'],\n ['碧玉石', '#66a8a8', '102,168,168'],\n ['灰緑', '#5c968a', '92,138,138'],\n ['军服蓝', '#5f9ea0', '95,160,160'],\n ['暗岩灰', '#2f4f4f', '47,79,79'],\n ['浅松緑', '#79d2d2', '121,210,210'],\n ['天蓝', '#87ceeb', '135,235,235'],\n ['玉石蓝', '#427371', '66,113,113'],\n ['毛緑', '#28414a', '40,74,74'],\n ['水蓝', '#66ffe6', '102,230,230'],\n ['萨克斯蓝', '#4798b3', '71,179,179'],\n ['深松緑', '#42bfac', '66,172,172'],\n ['中绿松石色', '#48d1cc', '72,204,204'],\n ['绿松石色', '#30d5c8', '48,200,200'],\n ['土耳其蓝', '#33e6cc', '51,204,204'],\n ['亮海绿', '#20b2aa', '32,170,170'],\n ['青蓝', '#0dbf8c', '13,140,140'],\n ['品緑', '#07817a', '7,122,122'],\n ['湖緑', '#015f5a', '1,90,90'],\n ['青色', '#00ffff', '0,255,255'],\n ['深天蓝', '#00bfff', '0,255,255'],\n ['暗绿松石色', '#00ced1', '0,209,209'],\n ['暗青', '#008b8b', '0,139,139'],\n ['孔雀蓝', '#00808c', '0,140,140'],\n ['凫绿', '#008080', '0,128,128'],\n ['浓蓝', '#006374', '0,116,116'],\n ['沙緑', '#003129', '0,41,41'],\n ['银蓝', '#e1e3e6', '225,230,230'],\n ['爱丽丝蓝', '#f0f8ff', '240,255,255'],\n ['鉄青', '#292c2e', '41,46,46'],\n ['亮钢蓝', '#b0c4de', '176,222,222'],\n ['亮岩灰', '#778899', '119,153,153'],\n ['岩灰', '#708090', '112,144,144'],\n ['花青', '#576d93', '87,147,147'],\n ['鹊灰', '#283144', '40,68,68'],\n ['钴蓝', '#6b9bb8', '107,184,184'],\n ['浅蓝', '#89cff0', '137,240,240'],\n ['绀青', '#3f4470', '63,112,112'],\n ['浅天蓝', '#87cefa', '135,250,250'],\n ['竹月色', '#6b9ac9', '107,201,201'],\n ['栢坊灰蓝', '#41627c', '65,124,124'],\n ['灰丁宁蓝', '#5e86c1', '94,193,193'],\n ['韦奇伍德瓷蓝', '#5686bf', '86,191,191'],\n ['矢车菊蓝', '#6495ed', '100,237,237'],\n ['钢青色', '#4682b4', '70,180,180'],\n ['浅海昌蓝', '#2f4988', '47,136,136'],\n ['鼠尾草蓝', '#4d80e6', '77,230,230'],\n ['労动布色', '#1c2859', '28,89,89'],\n ['品蓝', '#4169e1', '65,225,225'],\n ['暗矿蓝', '#24367d', '36,125,125'],\n ['深毛月色', '#284f95', '40,149,149'],\n ['沙青', '#205580', '32,128,128'],\n ['琉璃蓝', '#183a65', '24,101,101'],\n ['浅土蓝', '#122c4b', '18,75,75'],\n ['绒蓝', '#1f4f89', '31,137,137'],\n ['北京毛蓝', '#1f4f89', '31,137,137'],\n ['深竹月', '#2e62cd', '46,205,205'],\n ['毛月色', '#2c80c5', '44,197,197'],\n ['蔚蓝', '#2a52be', '42,190,190'],\n ['深蓝', '#0e1a49', '14,73,73'],\n ['蔵蓝', '#111f6a', '17,106,106'],\n ['道奇蓝', '#1e90ff', '30,255,255'],\n ['海蓝', '#0b2d64', '11,100,100'],\n ['蓝宝石色', '#082567', '8,103,103'],\n ['深毛蓝', '#031025', '3,37,37'],\n ['天青石蓝', '#0d33ff', '13,255,255'],\n ['郡青', '#043ab9', '4,185,185'],\n ['湛蓝', '#007fff', '0,255,255'],\n ['极浓海蓝', '#0033ff', '0,255,255'],\n ['国际奇连蓝', '#002fa7', '0,167,167'],\n ['矿蓝', '#004d99', '0,153,153'],\n ['暗婴儿粉蓝', '#003399', '0,153,153'],\n ['水手蓝', '#00477d', '0,125,125'],\n ['普鲁士蓝', '#003153', '0,83,83'],\n ['雪灰', '#ededef', '237,239,239'],\n ['幽灵白', '#f8f8ff', '248,255,255'],\n ['薰衣草紫', '#e6e6fa', '230,250,250'],\n ['雪青灰', '#bfbed3', '191,211,211'],\n ['闪蝶紫', '#b4a4ca', '180,202,202'],\n ['矿紫', '#b8a1cf', '184,207,207'],\n ['青金色', '#9fa0d7', '159,215,215'],\n ['罗蓝灰', '#3c374a', '60,74,74'],\n ['浅灰紫红', '#8674a1', '134,161,161'],\n ['紫水晶', '#d9b3ff', '217,255,255'],\n ['紫丁香色', '#b399ff', '179,255,255'],\n ['浅石英紫', '#cc99ff', '204,255,255'],\n ['蔵墨蓝', '#27243c', '39,60,60'],\n ['雪蓝', '#777bce', '119,206,206'],\n ['中紫红', '#9370db', '147,219,219'],\n ['靛蓝', '#201f3d', '32,61,61'],\n ['木槿紫', '#bf80ff', '191,255,255'],\n ['钛鉄', '#1a152b', '26,43,43'],\n ['中岩蓝', '#7b68ee', '123,238,238'],\n ['岩蓝', '#6a5acd', '106,205,205'],\n ['暗岩蓝', '#483d8b', '72,139,139'],\n ['紫藤色', '#5c50e6', '92,230,230'],\n ['紫水晶色', '#6633cc', '102,204,204'],\n ['午夜蓝', '#191970', '25,112,112'],\n ['宝石蓝', '#1e25bb', '30,187,187'],\n ['鲜蓝', '#1515f9', '21,249,249'],\n ['蓝色', '#0000ff', '0,255,255'],\n ['中蓝', '#0000cd', '0,205,205'],\n ['缬草紫', '#5000b8', '80,184,184'],\n ['暗蓝', '#00008b', '0,139,139'],\n ['藏青', '#000080', '0,128,128'],\n ['浅藤紫', '#ece4f3', '236,243,243'],\n ['淡紫丁香色', '#e6cfe6', '230,230,230'],\n ['蓟紫', '#d8bfd8', '216,216,216'],\n ['莲灰', '#d1bfdd', '209,221,221'],\n ['紫藤灰', '#b5a4c4', '181,196,196'],\n ['铁线莲紫', '#cca3cc', '204,204,204'],\n ['梅红色', '#dda0dd', '221,221,221'],\n ['紫薇花', '#ff99ff', '255,255,255'],\n ['凤仙紫', '#9565b1', '149,177,177'],\n ['丁香紫', '#503a65', '80,101,101'],\n ['亮紫', '#ee82ee', '238,238,238'],\n ['兰紫', '#da70d6', '218,214,214'],\n ['洋葱紫', '#8e488e', '142,142,142'],\n ['优品紫红', '#e680ff', '230,255,255'],\n ['浅凤仙紫', '#e666ff', '230,255,255'],\n ['中兰紫', '#ba55d3', '186,211,211'],\n ['紫罗蓝', '#732e7e', '115,126,126'],\n ['锦葵紫', '#d94dff', '217,255,255'],\n ['暗兰紫', '#9932cc', '153,204,204'],\n ['蓝紫', '#8a2be2', '138,226,226'],\n ['紫罗兰色', '#8b00ff', '139,255,255'],\n ['洋红', '#ff00ff', '255,255,255'],\n ['暗紫', '#9400d3', '148,211,211'],\n ['三色堇紫', '#7400a1', '116,161,161'],\n ['青莲紫', '#730099', '115,153,153'],\n ['暗洋红', '#8b008b', '139,139,139'],\n ['靛色', '#4b0080', '75,128,128'],\n ['紫色', '#800080', '128,128,128'],\n ['竜胆紫', '#1a0033', '26,51,51'],\n ['白色', '#ffffff', '255,255,255'],\n ['立徳粉', '#ffffff', '255,255,255'],\n ['白烟色', '#f5f5f5', '245,245,245'],\n ['庚斯博罗灰', '#dcdcdc', '220,220,220'],\n ['亮灰色', '#d3d3d3', '211,211,211'],\n ['银箔', '#d4d4d4', '212,212,212'],\n ['银色', '#c0c0c0', '192,192,192'],\n ['灰色', '#808080', '128,128,128'],\n ['银鼠灰', '#797979', '121,121,121'],\n ['昏灰', '#696969', '105,105,105'],\n ['青灰色', '#484848', '72,72,72'],\n ['百草霜', '#1c1c1c', '28,28,28'],\n ['黑色', '#000000', '0,0,0'],\n ['妃色', '#ED5736', '237,87,54'],\n ['桃红', '#F47983', '244,121,131'],\n ['海棠红', '#DB5A6B', '219,90,107'],\n ['石榴红', '#F20C00', '242,12,0'],\n ['樱桃色', '#C93756', '201,55,86'],\n ['大红', '#FF2121', '255,33,33'],\n ['胭脂', '#9D2933', '157,41,51'],\n ['丹', '#FF4E20', '255,78,32'],\n ['彤', '#F35336', '243,83,54'],\n ['茜色', '#CB3A56', '203,58,86'],\n ['火红', '#FF2D51', '255,45,81'],\n ['赫赤', '#C91F37', '201,31,55'],\n ['嫣红', '#EF7A82', '239,122,130'],\n ['炎', '#FF3300', '255,51,0'],\n ['赤', '#C3272B', '195,39,43'],\n ['绾', '#A98175', '169,129,117'],\n ['檀', '#B36D61', '179,109,97'],\n ['殷红', '#BE002F', '190,0,47'],\n ['酡红', '#DC3023', '220,48,35'],\n ['酡颜', '#F9906F', '249,144,111'],\n ['鸭黄', '#FAFF72', '250,255,114'],\n ['樱草色', '#EAFF56', '234,255,86'],\n ['杏红', '#FF8C31', '255,140,49'],\n ['橘黄', '#FF8936', '255,137,54'],\n ['橙黄', '#FFA400', '255,164,0'],\n ['橘红', '#FF7500', '255,117,0'],\n ['缃色', '#F0C239', '240,194,57'],\n ['茶色', '#B35C44', '179,92,68'],\n ['昏黄', '#C89B40', '200,155,64'],\n ['棕色', '#B25D25', '178,93,37'],\n ['棕绿', '#827100', '130,113,0'],\n ['棕黑', '#7C4B00', '124,75,0'],\n ['棕黄', '#AE7000', '174,112,0'],\n ['琥珀', '#CA6924', '202,105,36'],\n ['枯黄', '#D3B17D', '211,177,125'],\n ['黄栌', '#E29C45', '226,156,69'],\n ['秋色', '#896C39', '137,108,57'],\n ['秋香色', '#D9B611', '217,182,17'],\n ['柳黄', '#C9DD22', '201,221,34'],\n ['柳绿', '#AFDD22', '175,221,34'],\n ['竹青', '#789262', '120,146,98'],\n ['葱黄', '#A3D900', '163,217,0'],\n ['葱绿', '#9ED900', '158,217,0'],\n ['葱青', '#0EB83A', '14,184,58'],\n ['青葱', '#0AA344', '10,163,68'],\n ['油绿', '#00BC12', '0,188,18'],\n ['绿沉', '#0C8918', '12,137,24'],\n ['碧色', '#1BD1A5', '27,209,165'],\n ['青碧', '#48C0A3', '72,192,163'],\n ['翡翠色', '#3DE1AD', '61,225,173'],\n ['青翠', '#00E079', '0,224,121'],\n ['青白', '#C0EBD7', '192,235,215'],\n ['鸭卵青', '#E0EEE8', '224,238,232'],\n ['蟹壳青', '#BBCDC5', '187,205,197'],\n ['鸦青', '#424C50', '66,76,80'],\n ['豆绿', '#9ED048', '158,208,72'],\n ['豆青', '#96CE54', '150,206,84'],\n ['石青', '#7BCFA6', '123,207,166'],\n ['玉色', '#7BCFA6', '46,223,163'],\n ['缥', '#7FECAD', '127,236,173'],\n ['艾绿', '#A4E2C6', '164,226,198'],\n ['松柏绿', '#21A675', '33,166,117'],\n ['松花绿', '#057748', '5,119,72'],\n ['松花色', '#BCE672', '5,119,72'],\n ['蓝', '#44CEF6', '68,206,246'],\n ['靛青', '#177CB0', '23,124,176'],\n ['碧蓝', '#3EEDE7', '62,237,231'],\n ['宝蓝', '#4B5CC4', '75,92,196'],\n ['藏蓝', '#3B2E7E', '59,46,126'],\n ['黛', '#4A4266', '74,66,102'],\n ['黛绿', '#426666', '66,102,102'],\n ['黛紫', '#574266', '87,66,102'],\n ['紫酱', '#815463', '129,84,99'],\n ['酱紫', '#815476', '129,84,118'],\n ['紫檀', '#4C221B', '76,34,27'],\n ['紫棠', '#56004F', '86,0,79'],\n ['青莲', '#801DAE', '128,29,174'],\n ['群青', '#4C8DAE', '76,141,174'],\n ['雪青', '#B0A4E3', '176,164,227'],\n ['丁香色', '#CCA4E3', '204,164,227'],\n ['藕色', '#EDD1D8', '237,209,216'],\n ['藕荷色', '#E4C6D0', '228,198,208'],\n ['苍青', '#A29B7C', '162,155,124'],\n ['苍黑', '#7397AB', '115,151,171'],\n ['苍白', '#D1D9E0', '209,217,224'],\n ['水红', '#F3D3E7', '243,211,231'],\n ['水绿', '#D4F2E7', '212,242,231'],\n ['淡青', '#D3E0F3', '211,224,243'],\n ['湖蓝', '#30DFF3', '48,223,243'],\n ['湖绿', '#25F8CB', '37,248,203'],\n ['精白', '#FFFFFF', '255,255,255'],\n ['像牙白', '#FFFBF0', '255,251,240'],\n ['雪白', '#F0FCFF', '240,252,255'],\n ['月白', '#D6ECF0', '214,236,240'],\n ['缟', '#F2ECDE', '242,236,222'],\n ['素', '#E0F0E9', '224,240,233'],\n ['荼白', '#F3F9F1', '243,249,241'],\n ['霜色', '#E9F1F6', '233,241,246'],\n ['花白', '#C2CCD0', '194,204,208'],\n ['鱼肚白', '#FCEFE8', '252,239,232'],\n ['莹白', '#E3F9FD', '227,239,253'],\n ['牙色', '#EEDEB0', '238,222,176'],\n ['铅白', '#F0F0F4', '240,240,244'],\n ['玄色', '#622A1D', '98,42,29'],\n ['玄青', '#3D3B4F', '61,59,79'],\n ['乌色', '#725E82', '114,94,130'],\n ['乌黑', '#392F41', '57,47,65'],\n ['漆黑', '#161823', '22,24,35'],\n ['墨色', '#50616D', '80,97,109'],\n ['墨灰', '#758A99', '117,138,153'],\n ['缁色', '#493131', '73,49,49'],\n ['煤黑', '#312520', '49,37,32'],\n ['黧', '#5D513C', '93,81,60'],\n ['黎', '#75664D', '117,102,77'],\n ['黝', '#6B6882', '107,104,130'],\n ['黝黑', '#665757', '102,87,87'],\n ['黯', '#41555D', '65,85,93'],\n ['赤金', '#F2BE45', '242,190,69'],\n ['银白', '#E9E7EF', '233,231,239'],\n ['铜绿', '#549688', '84,150,136'],\n ['乌金', '#A78E44', '167,142,68'],\n ['老银', '#BACAC6', '186,202,198'],\n ['猩红色', '#FF2400', '255,36,0'],\n ['朱红色', '#FF4D00', '255,77,0'],\n ['柠檬绿色', '#CCFF00', '204,255,0'],\n ['黄绿色', '#66FF00', '102,255,0'],\n ['蔚蓝色', '#007FFF', '0,127,255'],\n ['蓝绿色', '#7FFFD4', '127,255,212'],\n ['浅蓝色', '#E0FFFF', '137,207,240'],\n ['绿松色', '#30D5C8', '48,213,200'],\n ['粉末蓝', '#003399', '0,51,153'],\n ['皇室蓝', '#4169E1', '65,105,225'],\n ['天青蓝', '#2A52BE', '42,82,190'],\n ['钴蓝色', '#0047AB', '0,71,171'],\n ['海军蓝', '#000080', '0,0,128'],\n ['白牛仔布色', '#5E86C1', '94,134,193'],\n ['青玉色', '#082567', '8,37,103'],\n ['深茜红', '#E32636', '227,38,54'],\n ['洋红色', '#FF00FF', '255,0,255'],\n ['橙黄色', '#FFCC00', '255,204,0'],\n ['春绿色', '#00FF80', '0,255,128'],\n ['鸭绿色', '#008080', '0,128,128'],\n ['鲑肉色', '#FF8C69', '255,140,105'],\n ['米色', '#F5F5DC', '245,245,210'],\n ['薰衣草色', '#E6E6FA', '230,230,250'],\n ['兰花色', '#DA70D6', '218,112,214'],\n ['粉红色', '#FFC0CB', '255,192,203'],\n ['燃橙色', '#CC5500', '204,85,0'],\n ['暗灰色', '#404040', '64,64,64'],\n];\n\nexport const colors: string[] = [\n '赤',\n '朱',\n '丹',\n '绯',\n '彤',\n '绛',\n '茜',\n '纁',\n '赭',\n '栗',\n '褐',\n '驼',\n '赭',\n '橘',\n '曙',\n '翠',\n '碧',\n '金',\n '米',\n '缃',\n '靛',\n '紫',\n '藕',\n '桃',\n '青',\n '玄',\n '皂',\n '乌',\n '墨',\n '黛',\n '黝',\n '素',\n '杏',\n '缟',\n '鹤',\n '皓',\n '苍',\n '华',\n '银',\n ...COLOR_WITH_RGB.map(item => item[0]),\n];\n","// @flow\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<SegmentToken>): Array<SegmentToken> {\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 && this.isNominal(nextword.p) && 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 isNominal(pos: number | number[]): boolean {\n if (Array.isArray(pos)) {\n return this.isNominal(pos[0]);\n }\n const { POSTAG } = this.segment;\n return (\n pos === POSTAG.D_N\n || pos === POSTAG.A_NT\n || pos === POSTAG.A_NX\n || pos === POSTAG.A_NZ\n || pos === POSTAG.A_NR\n || pos === POSTAG.A_NS\n || pos === POSTAG.URL\n );\n }\n}\n","// @flow\n\n/**\n * 中文姓\n */\n\nfunction addOrderInfo(chars: string[], order: number): { [char: string]: number } {\n const result = {};\n chars.forEach(char => {\n result[char] = order;\n });\n return result;\n}\n\n// 单姓\nexport const FAMILY_NAME_1 = addOrderInfo(\n [\n // 有明显歧义的姓氏\n '王',\n '张',\n '黄',\n '周',\n '徐',\n '胡',\n '高',\n '林',\n '马',\n '于',\n '程',\n '傅',\n '曾',\n '叶',\n '余',\n '夏',\n '钟',\n '田',\n '任',\n '方',\n '石',\n '熊',\n '白',\n '毛',\n '江',\n '史',\n '候',\n '龙',\n '万',\n '段',\n '雷',\n '钱',\n '汤',\n '易',\n '常',\n '武',\n '赖',\n '文',\n '查',\n // 没有明显歧义的姓氏\n '赵',\n '肖',\n '孙',\n '李',\n '吴',\n '郑',\n '冯',\n '陈',\n '褚',\n '卫',\n '蒋',\n '沈',\n '韩',\n '杨',\n '朱',\n '秦',\n '尤',\n '许',\n '何',\n '吕',\n '施',\n '桓',\n '孔',\n '曹',\n '严',\n '华',\n '金',\n '魏',\n '陶',\n '姜',\n '戚',\n '谢',\n '邹',\n '喻',\n '柏',\n '窦',\n '苏',\n '潘',\n '葛',\n '奚',\n '范',\n '彭',\n '鲁',\n '韦',\n '昌',\n '俞',\n '袁',\n '酆',\n '鲍',\n '唐',\n '费',\n '廉',\n '岑',\n '薛',\n '贺',\n '倪',\n '滕',\n '殷',\n '罗',\n '毕',\n '郝',\n '邬',\n '卞',\n '康',\n '卜',\n '顾',\n '孟',\n '穆',\n '萧',\n '尹',\n '姚',\n '邵',\n '湛',\n '汪',\n '祁',\n '禹',\n '狄',\n '贝',\n '臧',\n '伏',\n '戴',\n '宋',\n '茅',\n '庞',\n '纪',\n '舒',\n '屈',\n '祝',\n '董',\n '梁',\n '杜',\n '阮',\n '闵',\n '贾',\n '娄',\n '颜',\n '郭',\n '邱',\n '骆',\n '蔡',\n '樊',\n '凌',\n '霍',\n '虞',\n '柯',\n '昝',\n '卢',\n '柯',\n '缪',\n '宗',\n '丁',\n '贲',\n '邓',\n '郁',\n '杭',\n '洪',\n '崔',\n '龚',\n '嵇',\n '邢',\n '滑',\n '裴',\n '陆',\n '荣',\n '荀',\n '惠',\n '甄',\n '芮',\n '羿',\n '储',\n '靳',\n '汲',\n '邴',\n '糜',\n '隗',\n '侯',\n '宓',\n '蓬',\n '郗',\n '仲',\n '栾',\n '钭',\n '历',\n '戎',\n '刘',\n '詹',\n '幸',\n '韶',\n '郜',\n '黎',\n '蓟',\n '溥',\n '蒲',\n '邰',\n '鄂',\n '咸',\n '卓',\n '蔺',\n '屠',\n '乔',\n '郁',\n '胥',\n '苍',\n '莘',\n '翟',\n '谭',\n '贡',\n '劳',\n '冉',\n '郦',\n '雍',\n '璩',\n '桑',\n '桂',\n '濮',\n '扈',\n '冀',\n '浦',\n '庄',\n '晏',\n '瞿',\n '阎',\n '慕',\n '茹',\n '习',\n '宦',\n '艾',\n '容',\n '慎',\n '戈',\n '廖',\n '庾',\n '衡',\n '耿',\n '弘',\n '匡',\n '阙',\n '殳',\n '沃',\n '蔚',\n '夔',\n '隆',\n '巩',\n '聂',\n '晁',\n '敖',\n '融',\n '訾',\n '辛',\n '阚',\n '毋',\n '乜',\n '鞠',\n '丰',\n '蒯',\n '荆',\n '竺',\n '盍',\n '单',\n '欧',\n ],\n 1,\n);\n// 复姓\nexport const FAMILY_NAME_2 = addOrderInfo(\n [\n '司马',\n '上官',\n '欧阳',\n '夏侯',\n '诸葛',\n '闻人',\n '东方',\n '赫连',\n '皇甫',\n '尉迟',\n '公羊',\n '澹台',\n '公冶',\n '宗政',\n '濮阳',\n '淳于',\n '单于',\n '太叔',\n '申屠',\n '公孙',\n '仲孙',\n '轩辕',\n '令狐',\n '徐离',\n '宇文',\n '长孙',\n '慕容',\n '司徒',\n '司空',\n '万俟',\n ],\n 2,\n);\n\n// 双字姓名第一个字\nexport const DOUBLE_NAME_1 = addOrderInfo(\n [\n '阿',\n '建',\n '小',\n '晓',\n '文',\n '志',\n '国',\n '玉',\n '丽',\n '永',\n '海',\n '春',\n '金',\n '明',\n '新',\n '德',\n '秀',\n '红',\n '亚',\n '伟',\n '雪',\n '俊',\n '桂',\n '爱',\n '美',\n '世',\n '正',\n '庆',\n '学',\n '家',\n '立',\n '淑',\n '振',\n '云',\n '华',\n '光',\n '惠',\n '兴',\n '天',\n '长',\n '艳',\n '慧',\n '利',\n '宏',\n '佳',\n '瑞',\n '凤',\n '荣',\n '秋',\n '继',\n '嘉',\n '卫',\n '燕',\n '思',\n '维',\n '少',\n '福',\n '忠',\n '宝',\n '子',\n '成',\n '月',\n '洪',\n '东',\n '一',\n '泽',\n '林',\n '大',\n '素',\n '旭',\n '宇',\n '智',\n '锦',\n '冬',\n '玲',\n '雅',\n '伯',\n '翠',\n '传',\n '启',\n '剑',\n '安',\n '树',\n '良',\n '中',\n '梦',\n '广',\n '昌',\n '元',\n '万',\n '清',\n '静',\n '友',\n '宗',\n '兆',\n '丹',\n '克',\n '彩',\n '绍',\n '喜',\n '远',\n '朝',\n '敏',\n '培',\n '胜',\n '祖',\n '先',\n '菊',\n '士',\n '向',\n '有',\n '连',\n '军',\n '健',\n '巧',\n '耀',\n '莉',\n '英',\n '方',\n '和',\n '仁',\n '孝',\n '梅',\n '汉',\n '兰',\n '松',\n '水',\n '江',\n '益',\n '开',\n '景',\n '运',\n '贵',\n '祥',\n '青',\n '芳',\n '碧',\n '婷',\n '龙',\n '鹏',\n '自',\n '顺',\n '双',\n '书',\n '生',\n '义',\n '跃',\n '银',\n '佩',\n '雨',\n '保',\n '贤',\n '仲',\n '鸿',\n '浩',\n '加',\n '定',\n '炳',\n '飞',\n '锡',\n '柏',\n '发',\n '超',\n '道',\n '怀',\n '进',\n '其',\n '富',\n '平',\n '全',\n '阳',\n '吉',\n '茂',\n '彦',\n '诗',\n '洁',\n '润',\n '承',\n '治',\n '焕',\n '如',\n '君',\n '增',\n '善',\n '希',\n '根',\n '应',\n '勇',\n '宜',\n '守',\n '会',\n '凯',\n '育',\n '湘',\n '凌',\n '本',\n '敬',\n '博',\n '延',\n '乐',\n '三',\n '二',\n '四',\n '五',\n '六',\n '七',\n '八',\n '九',\n '十',\n ],\n 1,\n);\n\n// 双字姓名第二个字\nexport const DOUBLE_NAME_2 = addOrderInfo(\n [\n '华',\n '平',\n '明',\n '英',\n '军',\n '林',\n '萍',\n '芳',\n '玲',\n '红',\n '生',\n '霞',\n '梅',\n '文',\n '荣',\n '珍',\n '兰',\n '娟',\n '峰',\n '琴',\n '云',\n '辉',\n '东',\n '龙',\n '敏',\n '伟',\n '强',\n '丽',\n '春',\n '杰',\n '燕',\n '民',\n '君',\n '波',\n '国',\n '芬',\n '清',\n '祥',\n '斌',\n '婷',\n '飞',\n '良',\n '忠',\n '新',\n '凤',\n '锋',\n '成',\n '勇',\n '刚',\n '玉',\n '元',\n '宇',\n '海',\n '兵',\n '安',\n '庆',\n '涛',\n '鹏',\n '亮',\n '青',\n '阳',\n '艳',\n '松',\n '江',\n '莲',\n '娜',\n '兴',\n '光',\n '德',\n '武',\n '香',\n '俊',\n '秀',\n '慧',\n '雄',\n '才',\n '宏',\n '群',\n '琼',\n '胜',\n '超',\n '彬',\n '莉',\n '中',\n '山',\n '富',\n '花',\n '宁',\n '利',\n '贵',\n '福',\n '发',\n '义',\n '蓉',\n '喜',\n '娥',\n '昌',\n '仁',\n '志',\n '全',\n '宝',\n '权',\n '美',\n '琳',\n '建',\n '金',\n '贤',\n '星',\n '丹',\n '根',\n '和',\n '珠',\n '康',\n '菊',\n '琪',\n '坤',\n '泉',\n '秋',\n '静',\n '佳',\n '顺',\n '源',\n '珊',\n '达',\n '欣',\n '如',\n '莹',\n '章',\n '浩',\n '勤',\n '芹',\n '容',\n '友',\n '芝',\n '豪',\n '洁',\n '鑫',\n '惠',\n '洪',\n '旺',\n '虎',\n '远',\n '妮',\n '森',\n '妹',\n '南',\n '雯',\n '奇',\n '健',\n '卿',\n '虹',\n '娇',\n '媛',\n '怡',\n '铭',\n '川',\n '进',\n '博',\n '智',\n '来',\n '琦',\n '学',\n '聪',\n '洋',\n '乐',\n '年',\n '翔',\n '然',\n '栋',\n '凯',\n '颖',\n '鸣',\n '丰',\n '瑞',\n '奎',\n '立',\n '堂',\n '威',\n '雪',\n '鸿',\n '晶',\n '桂',\n '凡',\n '娣',\n '先',\n '洲',\n '毅',\n '雅',\n '月',\n '旭',\n '田',\n '晖',\n '方',\n '恒',\n '亚',\n '泽',\n '风',\n '银',\n '高',\n '贞',\n '九',\n '薇',\n ],\n 2,\n);\n\n// 单字姓名\nexport const SINGLE_NAME = addOrderInfo(\n [\n '家',\n '民',\n '敏',\n '伟',\n '勇',\n '军',\n '斌',\n '静',\n '丽',\n '涛',\n '芳',\n '杰',\n '萍',\n '强',\n '俊',\n '明',\n '燕',\n '磊',\n '玲',\n '华',\n '平',\n '鹏',\n '健',\n '波',\n '红',\n '丹',\n '辉',\n '超',\n '艳',\n '莉',\n '刚',\n '娟',\n '峰',\n '婷',\n '亮',\n '洁',\n '颖',\n '琳',\n '英',\n '慧',\n '飞',\n '霞',\n '浩',\n '凯',\n '宇',\n '毅',\n '林',\n '佳',\n '云',\n '莹',\n '娜',\n '晶',\n '洋',\n '文',\n '鑫',\n '欣',\n '琴',\n '宁',\n '琼',\n '兵',\n '青',\n '琦',\n '翔',\n '彬',\n '锋',\n '阳',\n '璐',\n '旭',\n '蕾',\n '剑',\n '虹',\n '蓉',\n '建',\n '倩',\n '梅',\n '宏',\n '威',\n '博',\n '君',\n '力',\n '龙',\n '晨',\n '薇',\n '雪',\n '琪',\n '欢',\n '荣',\n '江',\n '炜',\n '成',\n '庆',\n '冰',\n '东',\n '帆',\n '雷',\n '楠',\n '锐',\n '进',\n '海',\n '凡',\n '巍',\n '维',\n '迪',\n '媛',\n '玮',\n '杨',\n '群',\n '瑛',\n '悦',\n '春',\n '瑶',\n '婧',\n '兰',\n '茜',\n '松',\n '爽',\n '立',\n '瑜',\n '睿',\n '晖',\n '聪',\n '帅',\n '瑾',\n '骏',\n '雯',\n '晓',\n '昊',\n '勤',\n '新',\n '瑞',\n '岩',\n '星',\n '忠',\n '志',\n '怡',\n '坤',\n '康',\n '航',\n '利',\n '畅',\n '坚',\n '雄',\n '智',\n '萌',\n '哲',\n '岚',\n '洪',\n '捷',\n '珊',\n '恒',\n '靖',\n '清',\n '扬',\n '昕',\n '乐',\n '武',\n '玉',\n '诚',\n '菲',\n '锦',\n '凤',\n '珍',\n '晔',\n '妍',\n '璇',\n '胜',\n '菁',\n '科',\n '芬',\n '露',\n '越',\n '彤',\n '曦',\n '义',\n '良',\n '鸣',\n '芸',\n '方',\n '月',\n '铭',\n '光',\n '震',\n '冬',\n '源',\n '政',\n '虎',\n '莎',\n '彪',\n '蓓',\n '钢',\n '凌',\n '奇',\n '卫',\n '彦',\n '烨',\n '可',\n '黎',\n '川',\n '淼',\n '惠',\n '祥',\n '然',\n '三',\n '二',\n '一',\n '四',\n '五',\n '六',\n '七',\n '八',\n '九',\n '十',\n ],\n 1,\n);\n\nexport default {\n FAMILY_NAME_1,\n FAMILY_NAME_2,\n DOUBLE_NAME_1,\n DOUBLE_NAME_2,\n SINGLE_NAME,\n};\n","// @flow\nimport { Tokenizer } from './BaseModule';\nimport type { SegmentToken, TokenStartPosition } from './type';\n\nimport {\n FAMILY_NAME_1, FAMILY_NAME_2, SINGLE_NAME, DOUBLE_NAME_1, DOUBLE_NAME_2,\n} from './CHS_NAMES';\n\nexport default class ChsNameTokenizer extends Tokenizer {\n split(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n const ret = [];\n for (var i = 0, word; (word = words[i]); i++) {\n if (word.p > 0) {\n ret.push(word);\n continue;\n }\n // 仅对未识别的词进行匹配\n const nameinfo: TokenStartPosition = ChsNameTokenizer.matchName(word.w);\n if (nameinfo.length < 1) {\n ret.push(word);\n continue;\n }\n // 分离出人名\n let lastc = 0;\n for (var ui = 0, url; (url = nameinfo[ui]); ui++) {\n if (url.c > lastc) {\n ret.push({ w: word.w.substr(lastc, url.c - lastc) });\n }\n ret.push({ w: url.w, p: POSTAG.A_NR });\n lastc = url.c + url.w.length;\n }\n const lastn = nameinfo[nameinfo.length - 1];\n if (lastn.c + lastn.w.length < word.w.length) {\n ret.push({ w: word.w.substr(lastn.c + lastn.w.length) });\n }\n }\n return ret;\n }\n\n // 匹配包含的人名，并返回相关信息\n static matchName(text: string, startPos: number): Array<TokenStartPosition> {\n let startPosition = 0;\n if (!isNaN(startPos)) startPosition = startPos;\n const result = [];\n while (startPosition < text.length) {\n let name = false;\n // 取两个字，看看在不在复姓表里\n const f2 = text.substr(startPosition, 2);\n if (f2 in FAMILY_NAME_2) {\n const n1 = text.charAt(startPosition + 2);\n const n2 = text.charAt(startPosition + 3);\n // 看看姓后面的字在不在名表里\n if (n1 in DOUBLE_NAME_1 && n2 in DOUBLE_NAME_2) {\n name = f2 + n1 + n2;\n } else if (n1 in SINGLE_NAME) {\n name = f2 + n1 + (n1 === n2 ? n2 : '');\n }\n }\n // 单姓\n const f1 = text.charAt(startPosition);\n if (name === false && f1 in FAMILY_NAME_1) {\n const n1 = text.charAt(startPosition + 1);\n const n2 = text.charAt(startPosition + 2);\n if (n1 in DOUBLE_NAME_1 && n2 in DOUBLE_NAME_2) {\n name = f1 + n1 + n2;\n } else if (n1 in SINGLE_NAME) {\n name = f1 + n1 + (n1 === n2 ? n2 : '');\n }\n }\n // 检查是否匹配成功\n if (name === false) {\n startPosition++;\n } else {\n result.push({ w: name, c: startPosition });\n startPosition += name.length;\n }\n }\n return result;\n }\n}\n","// @flow\nimport { Optimizer } from './BaseModule';\nimport type { SegmentToken } from './type';\n\nexport default class DictOptimizer extends Optimizer {\n /**\n * 词典优化\n *\n * @param {array} words 单词数组\n * @param {bool} isNotFirst 是否为管理器调用的\n * @return {array}\n */\n doOptimize(words: Array<SegmentToken>, isNotFirst: boolean): Array<SegmentToken> {\n // debug(words);\n if (typeof isNotFirst === 'undefined') {\n isNotFirst = false;\n }\n // 合并相邻的能组成一个单词的两个词\n const TABLE = this.segment.getDict('TABLE');\n const POSTAG = this.segment.POSTAG;\n\n let i = 0;\n let ie = words.length - 1;\n while (i < ie) {\n const w1 = words[i];\n const w2 = words[i + 1];\n // debug(w1.w + ', ' + w2.w);\n\n // ==========================================\n // 能组成一个新词的(词性必须相同)\n const nw = w1.w + w2.w;\n if (w1.p === w2.p && nw in TABLE) {\n words.splice(i, 2, {\n w: nw,\n p: TABLE[nw].p,\n });\n ie--;\n continue;\n }\n\n // 形容词 + 助词 = 形容词，如： 不同 + 的 = 不同的\n if ((w1.p & POSTAG.D_A) > 0 && w2.p & POSTAG.D_U) {\n words.splice(i, 2, {\n w: nw,\n p: POSTAG.D_A,\n });\n ie--;\n continue;\n }\n\n // ============================================\n // 数词组合\n if ((w1.p & POSTAG.A_M) > 0) {\n // debug(w2.w + ' ' + (w2.p & POSTAG.A_M));\n // 百分比数字 如 10%，或者下一个词也是数词，则合并\n if ((w2.p & POSTAG.A_M) > 0 || w2.w === '%') {\n words.splice(i, 2, {\n w: w1.w + w2.w,\n p: POSTAG.A_M,\n });\n ie--;\n continue;\n }\n // 数词 + 量词，合并。如： 100个\n if ((w2.p & POSTAG.A_Q) > 0) {\n words.splice(i, 2, {\n w: w1.w + w2.w,\n p: POSTAG.D_MQ, // 数量词\n });\n ie--;\n continue;\n }\n // 带小数点的数字 ，如 “3 . 14”，或者 “十五点三”\n // 数词 + \"分之\" + 数词，如“五十分之一”\n var w3 = words[i + 2];\n if (w3 && (w3.p & POSTAG.A_M) > 0 && (w2.w === '.' || w2.w === '点' || w2.w === '分之')) {\n words.splice(i, 3, {\n w: w1.w + w2.w + w3.w,\n p: POSTAG.A_M,\n });\n ie -= 2;\n continue;\n }\n }\n\n // 修正 “十五点五八”问题\n if ((w1.p & POSTAG.D_MQ) > 0 && w1.w.substr(-1) === '点' && w2.p & POSTAG.A_M) {\n // debug(w1, w2);\n let i2 = 2;\n let w4w = '';\n for (let j = i + i2; j < ie; j++) {\n var w3 = words[j];\n if ((w3.p & POSTAG.A_M) > 0) {\n w4w += w3.w;\n i2++;\n } else {\n break;\n }\n }\n words.splice(i, i2, {\n w: w1.w + w2.w + w4w,\n p: POSTAG.D_MQ, // 数量词\n });\n ie -= i2 - 1;\n continue;\n }\n\n // 移到下一个词\n i++;\n }\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","// @flow\nimport { Optimizer } from './BaseModule';\nimport type { SegmentToken } from './type';\n\n// 邮箱地址中允许出现的字符\n// 参考：http://www.cs.tut.fi/~jkorpela/rfc/822addr.html\nconst _EMAILCHAR = '!\"#$%&\\'*+-/0123456789=?@ABCDEFGHIJKLMNOPQRSTUVWXYZ^_`abcdefghijklmnopqrstuvwxyz{|}~.'.split('');\nconst EMAILCHAR = {};\nfor (const i in _EMAILCHAR) EMAILCHAR[_EMAILCHAR[i]] = 1;\n\nexport default class EmailOptimizer extends Optimizer {\n doOptimize(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n // debug(words);\n\n let i = 0;\n let ie = words.length - 1;\n let addr_start = false;\n let has_at = false;\n while (i < ie) {\n var word = words[i];\n var is_ascii = !!(word.p === POSTAG.A_NX || (word.p === POSTAG.A_M && word.w.charCodeAt(0) < 128));\n\n // 如果是外文字符或者数字，符合电子邮件地址开头的条件\n if (addr_start === false && is_ascii) {\n addr_start = i;\n i++;\n continue;\n } else {\n // 如果遇到@符号，符合第二个条件\n if (has_at === false && word.w === '@') {\n has_at = true;\n i++;\n continue;\n }\n // 如果已经遇到过@符号，且出现了其他字符，则截取邮箱地址\n if (has_at !== false && words[i - 1].w != '@' && is_ascii === false && !(word.w in EMAILCHAR)) {\n var mailws = words.slice(addr_start, i);\n words.splice(addr_start, mailws.length, {\n w: EmailOptimizer.toEmailAddress(mailws),\n p: POSTAG.URL,\n });\n i = addr_start + 1;\n ie -= mailws.length - 1;\n addr_start = false;\n has_at = false;\n continue;\n }\n // 如果已经开头\n if (addr_start !== false && (is_ascii || word.w in EMAILCHAR)) {\n i++;\n continue;\n }\n }\n\n // 移到下一个词\n addr_start = false;\n has_at = false;\n i++;\n }\n\n // 检查剩余部分\n if (addr_start && has_at && words[ie]) {\n var word = words[ie];\n var is_ascii = !!(word.p === POSTAG.A_NX || (word.p === POSTAG.A_M && word.w in EMAILCHAR));\n if (is_ascii) {\n var mailws = words.slice(addr_start, words.length);\n words.splice(addr_start, mailws.length, {\n w: EmailOptimizer.toEmailAddress(mailws),\n p: POSTAG.URL,\n });\n }\n }\n\n return words;\n }\n\n /**\n * 根据一组单词生成邮箱地址\n *\n * @param {array} words 单词数组\n * @return {string}\n */\n static toEmailAddress(words: Array<SegmentToken>): string {\n let ret = words[0].w;\n for (var i = 1, word; (word = words[i]); i++) {\n ret += word.w;\n }\n return ret;\n }\n}\n","// @flow\nimport { Tokenizer } from './BaseModule';\nimport type { SegmentToken, TokenStartPosition } from './type';\n\n// 标点符号\nlet _STOPWORD = ' ,.;+-|/\\\\\\'\":?<>[]{}=!@#$%^&*()~`'\n + '。，、＇：∶；?‘’“”〝〞ˆˇ﹕︰﹔﹖﹑·¨….¸;！´？！～—ˉ｜‖＂〃｀@﹫¡¿﹏﹋﹌︴々﹟#﹩$﹠&﹪%*﹡﹢﹦'\n + '﹤‐￣¯―﹨ˆ˜﹍﹎+=<＿_-ˇ~﹉﹊（）〈〉‹›﹛﹜『』〖〗［］《》〔〕{}「」【】︵︷︿︹︽_﹁﹃︻︶︸'\n + '﹀︺︾ˉ﹂﹄︼＋－×÷﹢﹣±／＝≈≡≠∧∨∑∏∪∩∈⊙⌒⊥∥∠∽≌＜＞≤≥≮≯∧∨√﹙﹚[]﹛﹜∫∮∝∞⊙∏'\n + '┌┬┐┏┳┓╒╤╕─│├┼┤┣╋┫╞╪╡━┃└┴┘┗┻┛╘╧╛┄┆┅┇╭─╮┏━┓╔╦╗┈┊│╳│┃┃╠╬╣┉┋╰─╯┗━┛'\n + '╚╩╝╲╱┞┟┠┡┢┦┧┨┩┪╉╊┭┮┯┰┱┲┵┶┷┸╇╈┹┺┽┾┿╀╁╂╃╄╅╆'\n + '○◇□△▽☆●◆■▲▼★♠♥♦♣☼☺◘♀√☻◙♂×▁▂▃▄▅▆▇█⊙◎۞卍卐╱╲▁▏↖↗↑←↔◤◥╲╱▔▕↙↘↓→↕◣◢∷▒░℡™';\n_STOPWORD = _STOPWORD.split('');\nconst STOPWORD = {};\nconst STOPWORD2 = {};\nfor (const i in _STOPWORD) {\n if (_STOPWORD[i] === '') continue;\n const len = _STOPWORD[i].length;\n STOPWORD[_STOPWORD[i]] = len;\n if (!STOPWORD2[len]) STOPWORD2[len] = {};\n STOPWORD2[len][_STOPWORD[i]] = len;\n}\n\nexport default class PunctuationTokenizer extends Tokenizer {\n split(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n const ret = [];\n for (var i = 0, word; (word = words[i]); i++) {\n if (word.p > 0) {\n ret.push(word);\n continue;\n }\n // 仅对未识别的词进行匹配\n const stopinfo = PunctuationTokenizer.matchStopword(word.w);\n if (stopinfo.length < 1) {\n ret.push(word);\n continue;\n }\n // 分离出标点符号\n let lastc = 0;\n for (var ui = 0, sw; (sw = stopinfo[ui]); ui++) {\n if (sw.c > lastc) {\n ret.push({ w: word.w.substr(lastc, sw.c - lastc) });\n }\n // 忽略空格\n if (sw.w != ' ') {\n ret.push({ w: sw.w, p: POSTAG.D_W });\n }\n lastc = sw.c + sw.w.length;\n }\n const lastsw = stopinfo[stopinfo.length - 1];\n if (lastsw.c + lastsw.w.length < word.w.length) {\n ret.push({ w: word.w.substr(lastsw.c + lastsw.w.length) });\n }\n }\n return ret;\n }\n\n /**\n * 匹配包含的标点符号，返回相关信息\n *\n * @param {string} text 文本\n * @param {int} cur 开始位置\n * @return {array} 返回格式 {w: '网址', c: 开始位置}\n */\n static matchStopword(text: string, cur: number): Array<TokenStartPosition> {\n if (isNaN(cur)) cur = 0;\n const ret = [];\n let isMatch = false;\n while (cur < text.length) {\n for (const i in STOPWORD2) {\n var w = text.substr(cur, i);\n if (w in STOPWORD2[i]) {\n ret.push({ w, c: cur });\n isMatch = true;\n break;\n }\n }\n cur += isMatch === false ? 1 : w.length;\n isMatch = false;\n }\n\n return ret;\n }\n}\n","// @flow\nimport { Tokenizer } from './BaseModule';\nimport type { SegmentToken, TokenStartPosition } from './type';\n\n// 协议URL头\nconst PROTOTAL = ['http://', 'https://', 'ftp://', 'news://', 'telnet://'];\n// 协议头最小长度\nlet MIN_PROTOTAL_LEN = 100;\nfor (var i in PROTOTAL) {\n if (PROTOTAL[i].length < MIN_PROTOTAL_LEN) {\n MIN_PROTOTAL_LEN = PROTOTAL[i].length;\n }\n}\n// 允许出现在URL中的字符\nconst _URLCHAR = [\n 'a',\n 'b',\n 'c',\n 'd',\n 'e',\n 'f',\n 'g',\n 'h',\n 'i',\n 'j',\n 'k',\n 'l',\n 'm',\n 'n',\n 'o',\n 'p',\n 'q',\n 'r',\n 's',\n 't',\n 'u',\n 'v',\n 'w',\n 'x',\n 'y',\n 'z',\n 'A',\n 'B',\n 'C',\n 'D',\n 'E',\n 'F',\n 'G',\n 'H',\n 'I',\n 'J',\n 'K',\n 'L',\n 'M',\n 'N',\n 'O',\n 'P',\n 'Q',\n 'R',\n 'S',\n 'T',\n 'U',\n 'V',\n 'W',\n 'X',\n 'Y',\n 'Z',\n '0',\n '1',\n '2',\n '3',\n '4',\n '5',\n '6',\n '7',\n '8',\n '9',\n '!',\n '#',\n '$',\n '%',\n '&',\n '‘',\n '(',\n ')',\n '*',\n '+',\n ',',\n '-',\n '.',\n '/',\n ':',\n ';',\n '=',\n '?',\n '@',\n '[',\n '\\\\',\n ']',\n '^',\n '_',\n '`',\n '|',\n '~',\n];\nconst URLCHAR = {};\nfor (var i in _URLCHAR) {\n URLCHAR[_URLCHAR[i]] = 1;\n}\n\nexport default class URLTokenizer extends Tokenizer {\n split(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n const ret = [];\n for (var i = 0, word; (word = words[i]); i++) {\n if (word.p > 0) {\n ret.push(word);\n continue;\n }\n // 仅对未识别的词进行匹配\n const urlinfo = URLTokenizer.matchURL(word.w);\n if (urlinfo.length < 1) {\n ret.push(word);\n continue;\n }\n // 分离出URL\n let lastc = 0;\n for (var ui = 0, url; (url = urlinfo[ui]); ui++) {\n if (url.c > lastc) {\n ret.push({ w: word.w.substr(lastc, url.c - lastc) });\n }\n ret.push({ w: url.w, p: POSTAG.URL });\n lastc = url.c + url.w.length;\n }\n const lasturl = urlinfo[urlinfo.length - 1];\n if (lasturl.c + lasturl.w.length < word.w.length) {\n ret.push({ w: word.w.substr(lasturl.c + lasturl.w.length) });\n }\n }\n // debug(ret);\n return ret;\n }\n\n /**\n * 匹配包含的网址，返回相关信息\n *\n * @param {string} text 文本\n * @param {int} cur 开始位置\n * @return {array} 返回格式 {w: '网址', c: 开始位置}\n */\n static matchURL(text: string, cur: number): Array<TokenStartPosition> {\n if (isNaN(cur)) cur = 0;\n const ret = [];\n let s = false;\n while (cur < text.length) {\n // 判断是否为 http:// 之类的文本开头\n if (s === false && cur < text.length - MIN_PROTOTAL_LEN) {\n for (var i = 0, prot; (prot = PROTOTAL[i]); i++) {\n if (text.substr(cur, prot.length) === prot) {\n s = cur;\n cur += prot.length - 1;\n break;\n }\n }\n } else if (s !== false && !(text.charAt(cur) in URLCHAR)) {\n // 如果以http://之类开头，遇到了非URL字符，则结束\n ret.push({\n w: text.substr(s, cur - s),\n c: s,\n });\n s = false;\n }\n cur++;\n }\n // 检查剩余部分\n if (s !== false) {\n ret.push({\n w: text.substr(s, cur - s),\n c: s,\n });\n }\n\n return ret;\n }\n}\n","// @flow\nimport { Optimizer } from './BaseModule';\nimport type { SegmentToken } from './type';\n\nimport {\n FAMILY_NAME_1, FAMILY_NAME_2, SINGLE_NAME, DOUBLE_NAME_1, DOUBLE_NAME_2,\n} from './CHS_NAMES';\n\nexport default class ChsNameOptimizer extends Optimizer {\n doOptimize(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n let i = 0;\n\n /* 第一遍扫描 */\n while (i < words.length) {\n const word = words[i];\n const nextword = words[i + 1];\n if (nextword) {\n // debug(nextword);\n // 如果为 \"小|老\" + 姓\n if (\n nextword\n && (word.w === '小' || word.w === '老')\n && (nextword.w in FAMILY_NAME_1 || nextword.w in FAMILY_NAME_2)\n ) {\n words.splice(i, 2, {\n w: word.w + nextword.w,\n p: POSTAG.A_NR,\n });\n i++;\n continue;\n }\n\n // 如果是 姓 + 名（2字以内）\n if (\n (word.w in FAMILY_NAME_1 || word.w in FAMILY_NAME_2)\n && ((nextword.p & POSTAG.A_NR) > 0 && nextword.w.length <= 2)\n ) {\n words.splice(i, 2, {\n w: word.w + nextword.w,\n p: POSTAG.A_NR,\n });\n i++;\n continue;\n }\n\n // 如果相邻两个均为单字且至少有一个字是未识别的，则尝试判断其是否为人名\n if (!word.p || !nextword.p) {\n if (\n (word.w in SINGLE_NAME && word.w === nextword.w)\n || (word.w in DOUBLE_NAME_1 && nextword.w in DOUBLE_NAME_2)\n ) {\n words.splice(i, 2, {\n w: word.w + nextword.w,\n p: POSTAG.A_NR,\n });\n // 如果上一个单词可能是一个姓，则合并\n const preword = words[i - 1];\n if (preword && (preword.w in FAMILY_NAME_1 || preword.w in FAMILY_NAME_2)) {\n words.splice(i - 1, 2, {\n w: preword.w + word.w + nextword.w,\n p: POSTAG.A_NR,\n });\n } else {\n i++;\n }\n continue;\n }\n }\n\n // 如果为 无歧义的姓 + 名（2字以内） 且其中一个未未识别词\n if ((word.w in FAMILY_NAME_1 || word.w in FAMILY_NAME_2) && (!word.p || !nextword.p)) {\n // debug(word, nextword);\n words.splice(i, 2, {\n w: word.w + nextword.w,\n p: POSTAG.A_NR,\n });\n }\n }\n\n // 移到下一个单词\n i++;\n }\n\n /* 第二遍扫描 */\n i = 0;\n while (i < words.length) {\n const word = words[i];\n const nextword = words[i + 1];\n if (nextword) {\n // 如果为 姓 + 单字名\n if ((word.w in FAMILY_NAME_1 || word.w in FAMILY_NAME_2) && nextword.w in SINGLE_NAME) {\n words.splice(i, 2, {\n w: word.w + nextword.w,\n p: POSTAG.A_NR,\n });\n i++;\n continue;\n }\n }\n\n // 移到下一个单词\n i++;\n }\n\n return words;\n }\n}\n","// @flow\nimport { Optimizer } from './BaseModule';\nimport type { SegmentToken } from './type';\n\n// 日期时间常见组合\nconst DATETIME_WORDS = ['世纪', '年', '年份', '年度', '月', '月份', '月度', '日', '号', '时', '点', '点钟', '分', '分钟', '秒', '毫秒'];\nconst DATETIME = {};\n// eslint-disable-next-line\nfor (const i in DATETIME_WORDS) {\n DATETIME[DATETIME_WORDS[i]] = DATETIME_WORDS[i].length;\n}\n\nexport default class DatetimeOptimizer extends Optimizer {\n /**\n * 日期时间优化\n *\n * @param {array} words 单词数组\n * @param {bool} isNotFirst 是否为管理器调用的\n * @return {array}\n */\n doOptimize(words: Array<SegmentToken>, isNotFirst: boolean): Array<SegmentToken> {\n if (typeof isNotFirst === 'undefined') {\n isNotFirst = false;\n }\n // 合并相邻的能组成一个单词的两个词\n const TABLE = this.segment.getDict('TABLE');\n const POSTAG = this.segment.POSTAG;\n\n let i = 0;\n let ie = words.length - 1;\n while (i < ie) {\n var w1 = words[i];\n var w2 = words[i + 1];\n // debug(w1.w + ', ' + w2.w);\n\n if ((w1.p & POSTAG.A_M) > 0) {\n // =========================================\n // 日期时间组合 数字 + 日期单位，如 “2005年\"\n if (w2.w in DATETIME) {\n let nw = w1.w + w2.w;\n let len = 2;\n // 继续搜索后面连续的日期时间描述，必须符合 数字 + 日期单位\n while (true) {\n var w1 = words[i + len];\n var w2 = words[i + len + 1];\n if (w1 && w2 && (w1.p & POSTAG.A_M) > 0 && w2.w in DATETIME) {\n len += 2;\n nw += w1.w + w2.w;\n } else {\n break;\n }\n }\n words.splice(i, len, {\n w: nw,\n p: POSTAG.D_T,\n });\n ie -= len - 1;\n continue;\n }\n // =========================================\n }\n\n // 移到下一个词\n i++;\n }\n\n return words;\n }\n}\n","// @flow\nimport { Tokenizer } from './BaseModule';\nimport { FAMILY_NAME_1, FAMILY_NAME_2 } from './CHS_NAMES';\nimport type { SegmentToken, TokenStartPosition } from './type';\n\n// 日期时间常见组合\nconst _DATETIME = ['世纪', '年', '年份', '年度', '月', '月份', '月度', '日', '号', '时', '点', '点钟', '分', '分钟', '秒', '毫秒'];\nconst DATETIME = {};\nfor (const i in _DATETIME) DATETIME[_DATETIME[i]] = _DATETIME[i].length;\n\n/**\n * 对未识别的单词进行分词\n *\n * @param {array} words 单词数组\n * @return {array}\n */\nexport default class DictTokenizer extends Tokenizer {\n split(words: Array<SegmentToken>): Array<SegmentToken> {\n // debug(words);\n const POSTAG = this.segment.POSTAG;\n const TABLE = this.segment.getDict('TABLE');\n const ret = [];\n for (var i = 0, word; (word = words[i]); i++) {\n if (word.p > 0) {\n ret.push(word);\n continue;\n }\n // 仅对未识别的词进行匹配\n const wordinfo = this.matchWord(word.w, 0, words[i - 1]);\n if (wordinfo.length < 1) {\n ret.push(word);\n continue;\n }\n // 分离出已识别的单词\n let lastc = 0;\n for (var ui = 0, bw; (bw = wordinfo[ui]); ui++) {\n if (bw.c > lastc) {\n ret.push({ w: word.w.substr(lastc, bw.c - lastc) });\n }\n ret.push({ w: bw.w, p: TABLE[bw.w].p });\n lastc = bw.c + bw.w.length;\n }\n const lastword = wordinfo[wordinfo.length - 1];\n if (lastword.c + lastword.w.length < word.w.length) {\n ret.push({ w: word.w.substr(lastword.c + lastword.w.length) });\n }\n }\n return ret;\n }\n\n /**\n * 匹配单词，返回相关信息\n *\n * @param {string} text 文本\n * @param {int} cur 开始位置\n * @param {object} preword 上一个单词\n * @return {array} 返回格式 {w: '单词', c: 开始位置}\n */\n matchWord(text, cur, preword): Array<TokenStartPosition> {\n if (isNaN(cur)) cur = 0;\n const ret = [];\n const s = false;\n const TABLE = this.segment.getDict('TABLE2');\n // 匹配可能出现的单词\n while (cur < text.length) {\n for (const i in TABLE) {\n const w = text.substr(cur, i);\n if (w in TABLE[i]) {\n ret.push({ w, c: cur, f: TABLE[i][w].f });\n }\n }\n cur++;\n }\n\n return this.filterWord(ret, preword, text);\n }\n // debug(matchWord('长春市长春药店'));\n\n /**\n * 选择最有可能匹配的单词\n *\n * @param {array} words 单词信息数组\n * @param {object} preword 上一个单词\n * @param {string} text 本节要分词的文本\n * @return {array}\n */\n filterWord(words, preword, text) {\n const POSTAG = this.segment.POSTAG;\n const TABLE = this.segment.getDict('TABLE');\n let ret = [];\n\n // 将单词按位置分组\n const wordpos = DictTokenizer.getPosInfo(words, text);\n // debug(wordpos);\n\n // 使用类似于MMSG的分词算法\n // 找出所有分词可能，主要根据一下几项来评价：\n // x、词数量最少；\n // a、词平均频率最大；\n // b、每个词长度标准差最小；\n // c、未识别词最少；\n // d、符合语法结构项：如两个连续的动词减分，数词后面跟量词加分；\n // 取以上几项综合排名最最好的\n const chunks = DictTokenizer.getChunks(wordpos, 0, text);\n // debug(chunks);\n const assess = []; // 评价表\n\n // 对各个分支就行评估\n for (var i = 0, chunk; (chunk = chunks[i]); i++) {\n assess[i] = {\n x: chunk.length,\n a: 0,\n b: 0,\n c: 0,\n d: 0,\n };\n // 词平均长度\n const sp = text.length / chunk.length;\n // 句子经常包含的语法结构\n let has_D_V = false; // 是否包含动词\n\n // 遍历各个词\n if (preword) {\n var prew = { w: preword.w, p: preword.p, f: preword.f };\n } else {\n prew = false;\n }\n for (var j = 0, w; (w = chunk[j]); j++) {\n if (w.w in TABLE) {\n w.p = TABLE[w.w].p;\n assess[i].a += w.f; // 总词频\n\n // ================ 检查语法结构 ===================\n if (prew) {\n // 如果上一个词是数词且当前词是量词（单位），则加分\n if ((prew.p & POSTAG.A_M) > 0 && ((TABLE[w.w].p & POSTAG.A_Q) > 0 || w.w in DATETIME)) {\n assess[i].d++;\n }\n // 如果当前词是动词\n if ((w.p & POSTAG.D_V) > 0) {\n has_D_V = true;\n // 如果是连续的两个动词，则减分\n // if ((prew.p & POSTAG.D_V) > 0)\n // assess[i].d--;\n // 如果是 形容词 + 动词，则加分\n if ((prew.p & POSTAG.D_A) > 0) {\n assess[i].d++;\n }\n }\n // 如果是地区名、机构名或形容词，后面跟地区、机构、代词、名词等，则加分\n if (\n ((prew.p & POSTAG.A_NS) > 0 || prew.p & POSTAG.A_NT || (prew.p & POSTAG.D_A) > 0)\n && ((w.p & POSTAG.D_N) > 0\n || (w.p & POSTAG.A_NR) > 0\n || (w.p & POSTAG.A_NS) > 0\n || (w.p & POSTAG.A_NZ) > 0\n || (w.p & POSTAG.A_NT) > 0)\n ) {\n assess[i].d++;\n }\n // 如果是 方位词 + 数量词，则加分\n if ((prew.p & POSTAG.D_F) > 0 && (w.p & (POSTAG.A_M > 0) || w.p & (POSTAG.D_MQ > 0))) {\n // debug(prew, w);\n assess[i].d++;\n }\n // 如果是 姓 + 名词，则加分\n if (\n (prew.w in FAMILY_NAME_1 || prew.w in FAMILY_NAME_2)\n && ((w.p & POSTAG.D_N) > 0 || (w.p & POSTAG.A_NZ) > 0)\n ) {\n // debug(prew, w);\n assess[i].d++;\n }\n\n // 探测下一个词\n const nextw = chunk[j + 1];\n if (nextw) {\n if (nextw.w in TABLE) {\n nextw.p = TABLE[nextw.w].p;\n }\n // 如果是连词，前后两个词词性相同则加分\n if ((w.p & POSTAG.D_C) > 0 && prew.p === nextw.p) {\n assess[i].d++;\n }\n // 如果当前是“的”+ 名词，则加分\n if (\n (w.w === '的' || w.w === '之')\n && ((nextw.p & POSTAG.D_N) > 0\n || (nextw.p & POSTAG.A_NR) > 0\n || (nextw.p & POSTAG.A_NS) > 0\n || (nextw.p & POSTAG.A_NZ) > 0\n || (nextw.p & POSTAG.A_NT) > 0)\n ) {\n assess[i].d += 1.5;\n }\n }\n }\n // ===========================================\n } else {\n assess[i].c++; // 未识别的词数量\n }\n // 标准差\n assess[i].b += Math.pow(sp - w.w.length, 2);\n prew = chunk[j];\n }\n // 如果句子中包含了至少一个动词\n if (has_D_V === false) assess[i].d -= 0.5;\n\n assess[i].a = assess[i].a / chunk.length;\n assess[i].b = assess[i].b / chunk.length;\n }\n\n // 计算排名\n const top = DictTokenizer.getTops(assess);\n const currchunk = chunks[top];\n\n // 剔除不能识别的词\n for (var i = 0, word; (word = currchunk[i]); i++) {\n if (!(word.w in TABLE)) {\n currchunk.splice(i--, 1);\n }\n }\n ret = currchunk;\n\n // debug(ret);\n return ret;\n }\n\n /* 将单词按照位置排列\n *\n * @param {array} words\n * @param {string} text\n * @return {object}\n */\n static getPosInfo(words, text) {\n const wordpos = {};\n // 将单词按位置分组\n for (var i = 0, word; (word = words[i]); i++) {\n if (!wordpos[word.c]) {\n wordpos[word.c] = [];\n }\n wordpos[word.c].push(word);\n }\n // 按单字分割文本，填补空缺的位置\n for (var i = 0; i < text.length; i++) {\n if (!wordpos[i]) {\n wordpos[i] = [{ w: text.charAt(i), c: i, f: 0 }];\n }\n }\n\n return wordpos;\n }\n\n /**\n * 取所有分支\n *\n * @param {object} wordpos\n * @param {int} pos 当前位置\n * @param {string} text 本节要分词的文本\n * @return {array}\n */\n static getChunks(wordpos, pos, text) {\n const words = wordpos[pos] || [];\n const ret = [];\n for (let i = 0; i < words.length; i++) {\n const word = words[i];\n // debug(word);\n const nextcur = word.c + word.w.length;\n if (!wordpos[nextcur]) {\n ret.push([word]);\n } else {\n const chunks = DictTokenizer.getChunks(wordpos, nextcur);\n for (let j = 0; j < chunks.length; j++) {\n ret.push([word].concat(chunks[j]));\n }\n }\n }\n return ret;\n }\n\n /**\n * 评价排名\n *\n * @param {object} assess\n * @return {object}\n */\n static getTops(assess) {\n // 取各项最大值\n const top = {\n x: assess[0].x,\n a: assess[0].a,\n b: assess[0].b,\n c: assess[0].c,\n d: assess[0].d,\n };\n for (var i = 1, ass; (ass = assess[i]); i++) {\n if (ass.a > top.a) top.a = ass.a; // 取最大平均词频\n if (ass.b < top.b) top.b = ass.b; // 取最小标准差\n if (ass.c > top.c) top.c = ass.c; // 取最大未识别词\n if (ass.d < top.d) top.d = ass.d; // 取最小语法分数\n if (ass.x > top.x) top.x = ass.x; // 取最大单词数量\n }\n // debug(top);\n\n // 评估排名\n const tops = [];\n for (var i = 0, ass; (ass = assess[i]); i++) {\n tops[i] = 0;\n // 词数量，越小越好\n tops[i] += (top.x - ass.x) * 1.5;\n // 词总频率，越大越好\n if (ass.a >= top.a) tops[i] += 1;\n // 词标准差，越小越好\n if (ass.b <= top.b) tops[i] += 1;\n // 未识别词，越小越好\n tops[i] += top.c - ass.c; // debug(tops[i]);\n // 符合语法结构程度，越大越好\n tops[i] += (ass.d < 0 ? top.d + ass.d : ass.d - top.d) * 1;\n // debug(tops[i]);debug('---');\n }\n // debug(tops.join(' '));\n\n // 取分数最高的\n let curri = 0;\n let maxs = tops[0];\n for (var i in tops) {\n const s = tops[i];\n if (s > maxs) {\n curri = i;\n maxs = s;\n } else if (s === maxs) {\n // 如果分数相同，则根据词长度、未识别词个数和平均频率来选择\n let a = 0;\n let b = 0;\n if (assess[i].c < assess[curri].c) a++;\n else b++;\n if (assess[i].a > assess[curri].a) a++;\n else b++;\n if (assess[i].x < assess[curri].x) a++;\n else b++;\n if (a > b) {\n curri = i;\n maxs = s;\n }\n }\n }\n return curri;\n }\n}\n","// @flow\nimport { Tokenizer } from './BaseModule';\nimport type { SegmentToken, TokenStartPosition } from './type';\n\nexport default class ForeignTokenizer extends Tokenizer {\n split(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n let ret = [];\n for (var i = 0, word; (word = words[i]); i++) {\n if (word.p) {\n ret.push(word);\n } else {\n // 仅对未识别的词进行匹配\n ret = ret.concat(this.splitForeign(word.w));\n }\n }\n return ret;\n }\n\n /**\n * 匹配包含的英文字符和数字，并分割\n *\n * @param {string} text 文本\n * @param {int} cur 开始位置\n * @return {array} 返回格式 {w: '单词', c: 开始位置}\n */\n splitForeign(text: string, cur: number): Array<TokenStartPosition> {\n const POSTAG = this.segment.POSTAG;\n if (isNaN(cur)) cur = 0;\n const ret = [];\n\n // 取第一个字符的ASCII码\n let lastcur = 0;\n let lasttype = 0;\n var c = text.charCodeAt(0);\n // 全角数字或字母\n if (c >= 65296 && c <= 65370) c -= 65248;\n // 数字 lasttype = POSTAG.A_M\n if (c >= 48 && c <= 57) lasttype = POSTAG.A_M;\n else if ((c >= 65 && c <= 90) || (c >= 97 && c <= 122)) {\n // 字母 lasttype = POSTAG.A_NX\n lasttype = POSTAG.A_NX;\n } else lasttype = POSTAG.UNK;\n\n for (var i = 1; i < text.length; i++) {\n var c = text.charCodeAt(i);\n // 全角数字或字母\n if (c >= 65296 && c <= 65370) c -= 65248;\n // 数字 lasttype = POSTAG.A_M\n if (c >= 48 && c <= 57) {\n if (lasttype !== POSTAG.A_M) {\n var nw = { w: text.substr(lastcur, i - lastcur) };\n if (lasttype !== POSTAG.UNK) nw.p = lasttype;\n ret.push(nw);\n lastcur = i;\n }\n lasttype = POSTAG.A_M;\n } else if ((c >= 65 && c <= 90) || (c >= 97 && c <= 122)) {\n // 字母 lasttype = POSTAG.A_NX\n if (lasttype !== POSTAG.A_NX) {\n var nw = { w: text.substr(lastcur, i - lastcur) };\n if (lasttype !== POSTAG.UNK) nw.p = lasttype;\n ret.push(nw);\n lastcur = i;\n }\n lasttype = POSTAG.A_NX;\n } else {\n // 其他\n if (lasttype !== POSTAG.UNK) {\n ret.push({\n w: text.substr(lastcur, i - lastcur),\n p: [lasttype],\n });\n lastcur = i;\n }\n lasttype = POSTAG.UNK;\n }\n }\n // 剩余部分\n var nw = { w: text.substr(lastcur, i - lastcur) };\n if (lasttype !== POSTAG.UNK) nw.p = lasttype;\n ret.push(nw);\n\n // debug(ret);\n return ret;\n }\n}\n","// @flow\nimport { Tokenizer } from './BaseModule';\nimport type { SegmentToken, TokenStartPosition } from './type';\n\n// 单字切分模块\nexport default class SingleTokenizer extends Tokenizer {\n split(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n let ret = [];\n for (var i = 0, word; (word = words[i]); i++) {\n if (word.p) {\n ret.push(word);\n } else {\n // 仅对未识别的词进行匹配\n ret = ret.concat(this.splitSingle(word.w));\n }\n }\n return ret;\n }\n\n /**\n * 单字切分\n *\n * @param {string} text 要切分的文本\n * @param {int} cur 开始位置\n * @return {array}\n */\n splitSingle(text: string, cur: number): Array<TokenStartPosition> {\n const POSTAG = this.segment.POSTAG;\n if (isNaN(cur)) cur = 0;\n const ret = [];\n while (cur < text.length) {\n ret.push({\n w: text.charAt(cur),\n p: POSTAG.UNK,\n });\n cur++;\n }\n return ret;\n }\n}\n","// @flow\nimport { Tokenizer } from './BaseModule';\nimport type { SegmentToken, TokenStartPosition } from './type';\n\n// 通配符识别模块\nexport default class WildcardTokenizer extends Tokenizer {\n split(words: Array<SegmentToken>): Array<SegmentToken> {\n const POSTAG = this.segment.POSTAG;\n const TABLE = this.segment.getDict('WILDCARD');\n const ret = [];\n for (var i = 0, word; (word = words[i]); i++) {\n if (word.p > 0) {\n ret.push(word);\n continue;\n }\n // 仅对未识别的词进行匹配\n const wordinfo = this.matchWord(word.w);\n if (wordinfo.length < 1) {\n ret.push(word);\n continue;\n }\n // 分离出已识别的单词\n let lastc = 0;\n for (var ui = 0, bw; (bw = wordinfo[ui]); ui++) {\n if (bw.c > lastc) {\n ret.push({ w: word.w.substr(lastc, bw.c - lastc) });\n }\n ret.push({ w: bw.w, p: TABLE[bw.w.toLowerCase()].p });\n lastc = bw.c + bw.w.length;\n }\n const lastword = wordinfo[wordinfo.length - 1];\n if (lastword.c + lastword.w.length < word.w.length) {\n ret.push({ w: word.w.substr(lastword.c + lastword.w.length) });\n }\n }\n return ret;\n }\n\n /**\n * 匹配单词，返回相关信息\n *\n * @param {string} text 文本\n * @param {int} cur 开始位置\n * @return {array} 返回格式 {w: '单词', c: 开始位置}\n */\n matchWord(text: string, cur: number): Array<TokenStartPosition> {\n if (isNaN(cur)) cur = 0;\n const ret = [];\n const s = false;\n const TABLE = this.segment.getDict('WILDCARD2');\n // 匹配可能出现的单词，取长度最大的那个\n const lowertext = text.toLowerCase();\n while (cur < text.length) {\n let stopword = false;\n for (const i in TABLE) {\n if (lowertext.substr(cur, i) in TABLE[i]) {\n stopword = { w: text.substr(cur, i), c: cur };\n }\n }\n if (stopword !== false) {\n ret.push(stopword);\n cur += stopword.w.length;\n } else {\n cur++;\n }\n }\n return ret;\n }\n}\n","// @flow\nimport AdjectiveOptimizer from './AdjectiveOptimizer';\nimport CHS_NAMES from './CHS_NAMES';\nimport ChsNameTokenizer from './ChsNameTokenizer';\nimport DictOptimizer from './DictOptimizer';\nimport EmailOptimizer from './EmailOptimizer';\nimport PunctuationTokenizer from './PunctuationTokenizer';\nimport URLTokenizer from './URLTokenizer';\nimport ChsNameOptimizer from './ChsNameOptimizer';\nimport DatetimeOptimizer from './DatetimeOptimizer';\nimport DictTokenizer from './DictTokenizer';\nimport ForeignTokenizer from './ForeignTokenizer';\nimport SingleTokenizer from './SingleTokenizer';\nimport WildcardTokenizer from './WildcardTokenizer';\n\nexport {\n AdjectiveOptimizer,\n CHS_NAMES,\n ChsNameTokenizer,\n DictOptimizer,\n EmailOptimizer,\n PunctuationTokenizer,\n URLTokenizer,\n ChsNameOptimizer,\n DatetimeOptimizer,\n DictTokenizer,\n ForeignTokenizer,\n SingleTokenizer,\n WildcardTokenizer,\n};\n\nexport const modules = [\n // 强制分割类单词识别\n URLTokenizer, // URL识别\n WildcardTokenizer, // 通配符，必须在标点符号识别之前\n PunctuationTokenizer, // 标点符号识别\n ForeignTokenizer, // 外文字符、数字识别，必须在标点符号识别之后\n // 中文单词识别\n DictTokenizer, // 词典识别\n ChsNameTokenizer, // 人名识别，建议在词典识别之后\n // 优化模块\n EmailOptimizer, // 邮箱地址识别\n ChsNameOptimizer, // 人名识别优化\n DictOptimizer, // 词典识别优化\n DatetimeOptimizer, // 日期时间识别优化\n AdjectiveOptimizer,\n];\n\nexport { Module, Tokenizer, Optimizer } from './BaseModule';\nexport type { SegmentToken, TokenStartPosition } from './type';\n","// @flow\nimport preval from 'preval.macro';\n\nconst importExport = `\n const fs = require('fs');\n const path = require('path');\n module.exports = fs.readFileSync(path.join(__dirname, '`;\nconst tail = \".txt'), 'utf8')\";\n\n// 盘古词典\nexport const pangu: string = preval`${importExport}pangu${tail}`;\n// 扩展词典（用于调整原盘古词典）\nexport const panguExtend1: string = preval`${importExport}panguExtend1${tail}`;\nexport const panguExtend2: string = preval`${importExport}panguExtend2${tail}`;\n// 常见名词、人名\nexport const names: string = preval`${importExport}names${tail}`;\n// 通配符\nexport const wildcard: string = preval`${importExport}wildcard${tail}`;\n// 同义词\nexport const synonym: string = preval`${importExport}synonym${tail}`;\n// 停止符\nexport const stopword: string = preval`${importExport}stopword${tail}`;\n\n// 字典集，方便 import\nexport const dicts: string[] = [pangu, panguExtend1, panguExtend2, names, wildcard];\nexport const synonyms: string[] = [synonym];\nexport const stopwords: string[] = [stopword];\n","// @flow\nimport Segment from './Segment';\n\nimport {\n modules,\n Module,\n Tokenizer,\n Optimizer,\n CHS_NAMES,\n ChsNameTokenizer,\n DictOptimizer,\n EmailOptimizer,\n PunctuationTokenizer,\n URLTokenizer,\n ChsNameOptimizer,\n DatetimeOptimizer,\n DictTokenizer,\n ForeignTokenizer,\n SingleTokenizer,\n WildcardTokenizer,\n} from 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