JJUG CCC 2019 Fall の発表資料になります。
OpenAPI Generator を使って小規模な Web API サーバーを開発したときの経験やノウハウをまとめたものです。
https://ccc2019fall.java-users.jp/
https://jjug-cfp.cfapps.io/submissions/92e3117f-d911-4674-b97b-581813cfa0dc
JJUG CCC 2019 Fall の発表資料になります。
OpenAPI Generator を使って小規模な Web API サーバーを開発したときの経験やノウハウをまとめたものです。
https://ccc2019fall.java-users.jp/
https://jjug-cfp.cfapps.io/submissions/92e3117f-d911-4674-b97b-581813cfa0dc
JavaScript Frameworks and Java EE – A Great MatchReza Rahman
The sea change in JavaScript frameworks is shifting the pendulum away from today's thin-client based server-side web frameworks like Spring MVC and JSF to JavaScript powered rich clients. With strong support for REST, WebSocket and JSON, Java EE is well positioned to adapt to this landscape.
In this heavily code driven session, we will show you how you can utilize today's most popular JavaScript frameworks like AngularJS and React to utilize the core strengths of Java EE using JAX-RS, WebSocket, JSON-P, JSON-B, CDI and Bean Validation.
快速生成FAQ Bot - 使用Azure Language Service LanguageService-03-FAQbot (微軟)(鐘祥仁)(20...AllenLi78
This document is a presentation slide about how to create a FAQ bot using Azure AI services. It covers the following topics:
- The goal and overview of the FAQ bot project, which is to turn a company's FAQ page into a chatbot interface that can answer user queries.
- The Azure AI services involved in the project, such as Language Service, Custom Question Answering, and Azure Bot Service.
- The steps to build the FAQ bot, from creating a Language Service resource, importing the FAQ data set, deploying to Bot Service, and connecting to Line channel.
- The demo and summary of the FAQ bot project, showing how it works and what benefits it can bring.
24. 文字探勘(Text Mining)
• 文字探勘意指從文字資訊中發掘出有用資
訊的程序
• Data Mining vs. Text Mining
– Data Mining 假設處理的資料為具結構的資料
– Text Mining 的資源輸入為自然語言型式的文件
資料
• Text Mining 可先將自然語言型式的文件資料
轉換成為具結構的資料,再套用 Data
Mining 方法處理
33. Vector Space Model (1/2)
• 每份文件中所含的詞,都是一個特徵
• 將每份文件轉成所有詞所構成之向量空間
中的向量
• 將文件轉換成為向量後即可處理文件的相
似度
34. Vector Space Model (2/2)
• 將文件表示為 n 維的向量
– n 即所有文字中的所有可能詞
– 若一文件被表示為向量 V,其中的第 i 個元素之
為 v(i) ,其值為詞 wi 的權重
– 詞 wi 的權重可以有不同的表示,例如
• 詞 wi 的出現次數
• 詞 wi 的 TF-IDF 值