Google NLP API parsing
使用google 提供的API做語意分析。 語意分析(syntactic analysis)能夠提取語言的訊息，把文章拆成句子，句子在拆成更小的每個分詞，做更進一步的分析，Goole NLP API 會給予每個字詞的詞性以及彼此的關係。 Analyzing syntax 進入GCP新增一個API Key 並確認NLP API狀態為enable；詳細的GCP申請操作步驟可以看官方文件。(或是以後有機會寫。) API Enabled 因為這次是介紹，所以使用google cloud shell；在平常使用下可以把某些步驟改成習慣的語言及IDE。 新增環境變數 export API_KEY=<YOUR_KEY> 確認輸入後，增加要丟進API的文字json檔 text.json { "document":{ "type":"PLAIN_TEXT", "content": "Beirut rescuers search the site for possible survivor 30 days after the explosion." }, "encodingType": "UTF8" } 標準的json檔輸入資訊：https://cloud.google.com/natural-language/docs 使用curl post資料 curl "https://language.googleapis.com/v1/documents:analyzeSyntax?key=${API_KEY}" \ -s -X POST -H "Content-Type: application/json" --data-binary @text.json 會得到解析出來的資訊 { "sentences": [ { "text": { "content": "Beirut rescuers search the site for possible survivor 30 days after the explosion.", "beginOffset": 0 } } ], "tokens": [ { "text": { "content": "Beirut", "beginOffset": 0 }, "partOfSpeech": { "tag": "NOUN", "aspect": "ASPECT_UNKNOWN", "case": "CASE_UNKNOWN", "form": "FORM_UNKNOWN", "gender": "GENDER_UNKNOWN", "mood": "MOOD_UNKNOWN", "number": "SINGULAR", "person": "PERSON_UNKNOWN", "proper": "PROPER", "reciprocity": "RECIPROCITY_UNKNOWN", "tense": "TENSE_UNKNOWN", "voice": "VOICE_UNKNOWN" }, "dependencyEdge": { "headTokenIndex": 1, "label": "NN" }, "lemma": "Beirut" }, { "text": { "content": "rescuers", "beginOffset": 7 }, "partOfSpeech": { "tag": "NOUN", "aspect": "ASPECT_UNKNOWN", "case": "CASE_UNKNOWN", "form": "FORM_UNKNOWN", "gender": "GENDER_UNKNOWN", "mood": "MOOD_UNKNOWN", "number": "PLURAL", "person": "PERSON_UNKNOWN", "proper": "PROPER_UNKNOWN", "reciprocity": "RECIPROCITY_UNKNOWN", "tense": "TENSE_UNKNOWN", "voice": "VOICE_UNKNOWN" }, "dependencyEdge": { "headTokenIndex": 2, "label": "NSUBJ" }, "lemma": "rescuer" }, { "text": { "content": "search", "beginOffset": 16 }, "partOfSpeech": { "tag": "VERB", "aspect": "ASPECT_UNKNOWN", "case": "CASE_UNKNOWN", "form": "FORM_UNKNOWN", "gender": "GENDER_UNKNOWN", "mood": "INDICATIVE", "number": "NUMBER_UNKNOWN", "person": "PERSON_UNKNOWN", "proper": "PROPER_UNKNOWN", "reciprocity": "RECIPROCITY_UNKNOWN", "tense": "PRESENT", "voice": "VOICE_UNKNOWN" } } ...... ], "language": "en" } 觀察一下上面的結果 ...
