[依語法結構計算，而非統計方法的中文斷詞。]

Articut API Website

Document

Benchmark

設計目標

處理速度

大量文本

ArticutAPI

安裝方法

pip3 install ArticutAPI

說明文件

使用方法

Articut CWS (Chinese word segmentation)

from ArticutAPI import Articutfrom pprint import pprintusername = "" #這裡填入您在 https://api.droidtown.co 使用的帳號 email。若使用空字串，則預設使用每小時 2000 字的公用額度。apikey   = "" #這裡填入您在 https://api.droidtown.co 登入後取得的 api Key。若使用空字串，則預設使用每小時 2000 字的公用額度。articut = Articut(username, apikey)inputSTR = "會被大家盯上，才證明你有實力。"resultDICT = articut.parse(inputSTR)pprint(resultDICT)

回傳結果

{"exec_time": 0.06723856925964355, "level": "lv2", "msg": "Success!",  "result_pos": ["<MODAL>會</MODAL><ACTION_lightVerb>被</ACTION_lightVerb><ENTITY_nouny>大家</ENTITY_nouny><ACTION_verb>盯上</ACTION_verb>",                "，",                "<MODAL>才</MODAL><ACTION_verb>證明</ACTION_verb><ENTITY_pronoun>你</ENTITY_pronoun><ACTION_verb>有</ACTION_verb><ENTITY_noun>實力</ENTITY_noun>",                "。"], "result_segmentation": "會/被/大家/盯上/，/才/證明/你/有/實力/。/", "status": True, "version": "v118", "word_count_balance": 9985, "product": "https://api.droidtown.co/product/", "document": "https://api.droidtown.co/document/"}

列出斷詞結果所有詞性標記的內容詞

inputSTR = "你計劃過地球人類補完計劃"resultDICT = articut.parse(inputSTR, level="lv1")pprint(resultDICT["result_pos"])#列出所有的 content word.contentWordLIST = articut.getContentWordLIST(resultDICT)pprint(contentWordLIST)#列出所有的 verb word. (動詞)verbStemLIST = articut.getVerbStemLIST(resultDICT)pprint(verbStemLIST)#列出所有的 noun word. (名詞)nounStemLIST = articut.getNounStemLIST(resultDICT)pprint(nounStemLIST)#列出所有的 location word. (地方名稱)locationStemLIST = articut.getLocationStemLIST(resultDICT)pprint(locationStemLIST)

回傳結果

#resultDICT["result_pos"]["<ENTITY_pronoun>你</ENTITY_pronoun><ACTION_verb>計劃</ACTION_verb><ASPECT>過</ASPECT><LOCATION>地球</LOCATION><ENTITY_oov>人類</ENTITY_oov><ACTION_verb>補完</ACTION_verb><ENTITY_nounHead>計劃</ENTITY_nounHead>"]#列出所有的 content word.[[(47, 49, '計劃'), (117, 119, '人類'), (146, 147, '補'), (196, 198, '計劃')]]#列出所有的 verb word. (動詞)[[(47, 49, '計劃'), (146, 147, '補')]]#列出所有的 noun word. (名詞)[[(117, 119, '人類'), (196, 198, '計劃')]]#列出所有的 location word. (地方名稱)[[(91, 93, '地球')]]

取得 Articut 版本列表

resultDICT = articut.versions()pprint(resultDICT)

回傳結果

{"msg": "Success!", "status": True, "versions": [{"level": ["lv1", "lv2"],               "release_date": "2019-04-25",               "version": "latest"},              {"level": ["lv1", "lv2"],               "release_date": "2019-04-25",               "version": "v118"},              {"level": ["lv1", "lv2"],               "release_date": "2019-04-24",               "version": "v117"},...}

進階用法

進階用法01 >> Articut Level :斷詞的深度。數字愈小，切得愈細 (預設: lv2)。

inputSTR = "小紅帽"resultDICT = articut.parse(inputSTR, level="lv1")pprint(resultDICT)

回傳結果 lv1

{"exec_time": 0.04814624786376953, "level": "lv1", "msg": "Success!", "result_pos": ["<MODIFIER>小</MODIFIER><MODIFIER_color>紅</MODIFIER_color><ENTITY_nounHead>帽</ENTITY_nounHead>"], "result_segmentation": "小/紅/帽/", "status": True, "version": "v118", "word_count_balance": 9997,...}

回傳結果 lv2

{"exec_time": 0.04195523262023926, "level": "lv2", "msg": "Success!", "result_pos": ["<ENTITY_nouny>小紅帽</ENTITY_nouny>"], "result_segmentation": "小紅帽/", "status": True, "version": "v118", "word_count_balance": 9997,...}

進階用法 02 >> UserDefinedDictFile :使用者自定詞典。

{"雷姆":["小老婆"], "艾蜜莉亞":["大老婆"], "初音未來": ["初音", "只是個軟體"], "李敏鎬": ["全民歐巴", "歐巴"]}

from ArticutAPI import Articutfrom pprint import pprintarticut = Articut()userDefined = "./UserDefinedFile.json"inputSTR = "我的最愛是小老婆，不是初音未來。"# 使用自定義詞典resultDICT = articut.parse(inputSTR, userDefinedDictFILE=userDefined)pprint(resultDICT)# 未使用自定義詞典resultDICT = articut.parse(inputSTR)pprint(resultDICT)

回傳結果

# 使用自定義詞典{"result_pos": ["<ENTITY_pronoun>我</ENTITY_pronoun><FUNC_inner>的</FUNC_inner><ACTION_verb>最愛</ACTION_verb><AUX>是</AUX><UserDefined>小老婆</UserDefined>",                "，",                "<FUNC_negation>不</FUNC_negation><AUX>是</AUX><UserDefined>初音未來</UserDefined>",                "。"], "result_segmentation": "我/的/最愛/是/小老婆/，/不/是/初音未來/。/",...}# 未使用自定義詞典{"result_pos": ["<ENTITY_pronoun>我</ENTITY_pronoun><FUNC_inner>的</FUNC_inner><ACTION_verb>最愛</ACTION_verb><AUX>是</AUX><ENTITY_nouny>小老婆</ENTITY_nouny>",                "，",                "<FUNC_negation>不</FUNC_negation><AUX>是</AUX><ENTITY_nouny>初音</ENTITY_nouny><TIME_justtime>未來</TIME_justtime>",                "。"], "result_segmentation": "我/的/最愛/是/小老婆/，/不/是/初音/未來/。/",...}

進階用法 03 - 調用資料觀光資訊資料庫

{"username": "test@email.com","api_key": "anapikeyfordocthatdoesnwork@all","input_str": "花蓮的原野牧場有一間餐廳","version": "v137","level": "lv1","opendata_place": true}

{"exec_time": 0.013453006744384766,"level": "lv1","msg": "Success!","result_pos": ["<LOCATION>花蓮</LOCATION><FUNC_inner>的</FUNC_inner><KNOWLEDGE_place>原野牧場</KNOWLEDGE_place><ACTION_verb>有</ACTION_verb><ENTITY_classifier>一間</ENTITY_classifier><ENTITY_noun>餐廳</ENTITY_noun>"],"result_segmentation": "花蓮/的/原野牧場/有/一間/餐廳/","status": True,"version": "v137","word_count_balance": 99987}

進階用法 04 - 基於 TF-IDF 算法的關鍵詞抽取

進階用法 05 - 基於 TextRank 算法的關鍵詞抽取

基本思想：

進階用法 06 - 使用 GraphQL 查詢斷詞結果

使用 GraphiQL 工具

Python 3.6.1$ pip install graphene$ pip install starlette$ pip install jinja2$ pip install uvicorn

$ python ArticutGraphQL.py articutResult.json

使用範例 01

使用範例 02

使用 Articut-GraphQL

$ pip install graphene

使用範例 01

inputSTR = "地址：宜蘭縣宜蘭市縣政北七路六段55巷1號2樓"result = articut.parse(inputSTR)with open("articutResult.json", "w", encoding="utf-8") as resultFile:    json.dump(result, resultFile, ensure_ascii=False)graphQLResult = articut.graphQL.query(    filePath="articutResult.json",    query="""{  meta {    lang    description  }  doc {    text    tokens {      text      pos_      tag_      isStop      isEntity      isVerb      isTime      isClause      isKnowledge    }  }}""")pprint(graphQLResult)

回傳結果

使用範例 02

inputSTR = "劉克襄在本次活動當中，分享了台北中山北路一日遊路線。他表示當初自己領著柯文哲一同探索了雙連市場與中山捷運站的小吃與商圈，還有商圈內的文創商店與日系雜物店鋪，都令柯文哲留下深刻的印象。劉克襄也認為，雙連市場內的魯肉飯、圓仔湯與切仔麵，還有九條通的日式店家、居酒屋等特色，也能讓人感受到台北舊城區不一樣的魅力。"result = articut.parse(inputSTR)with open("articutResult.json", "w", encoding="utf-8") as resultFile:    json.dump(result, resultFile, ensure_ascii=False)graphQLResult = articut.graphQL.query(    filePath="articutResult.json",    query="""{  meta {    lang    description  }  doc {    text    ents {      persons {        text        pos_        tag_      }    }  }}""")pprint(graphQLResult)

回傳結果