短语列表功能 - 从文本中识别产品 [英] Phrase list features - identifying products from text
问题描述
Hello MS，
Hello MS,
LUIS是一个很棒的工具。我们开始着手编写聊天机器人，我们想要使用LUIS服务。我们希望LUIS从给定文本中识别各种Microsoft产品。让我们说，我希望LUIS识别"sharepoint 2010"，"sharepoint
2013"，"visual studio 2013"，visual studio 2010"等作为技术产品。我们尝试添加"产品"功能和添加的昏迷分隔值，如上所示。然而，该模型仅坚持"共享点"，"视觉"和"视觉"。
- 基本上是单个单词。它无法识别"短语"。在用一些数据训练之后，它能够识别"shaerpoint"。和"视觉"然而，作为技术（实体），根据需要的整个短语不起作用。
The LUIS has been a great tool. We started to set out to write a chatbot and we wanted to consume LUIS services. We want LUIS to identify various Microsoft Products from a given text. Let's say, I want LUIS to identify "sharepoint 2010", "sharepoint 2013", "visual studio 2013", visual studio 2010" etc as Technology Products. We tried adding "Products" feature and added coma separated values as shown above. However the model sticks to only "sharepoint", "visual" - basically single word. It is not able to identify the "phrase". After training with some data, it is able to identify "shaerpoint" and "visual" as Technology (entity) however, the whole phrase as needed is not working.
然后我们使用此"（SharePoint）\ * *（2013 | 2007 | 2010）"切换到简单的RegEx。很直接。它仍然无法将其识别为单个短语。
Then we switched over to simple RegEx also with this "(SharePoint)\s*(2013|2007|2010)" very straight forward. it is still not able to identify it as a single phrase.
我在Azure订阅中购买了这项服务，但我们正在浪费时间。
I bought the service on my Azure Subscription and we are losing time.
有人可以帮我这个吗？
Can somebody help me with this ?
感谢您的时间。
干杯，
Thank you for your time.
Cheers,
Krish
推荐答案
快速更新...我是能够获得正确的逻辑，使模型能够理解"SharePoint 2013"。在整体上是一个产品。但是，在给出相同的上下文之后，它无法预测任何其他不在"Phrase
list features"中的产品。
Quick update...I am able to get the logic right to make the model understand that "SharePoint 2013" on its whole is a Product. However, after giving the same context it is not able to predict any other Products that are not in the "Phrase list Features".
我一直在训练"我在BizTalk上工作"，"我在SQL上工作"。 ....（所有单字母单词），一旦开始预测单个字母"技术/产品"，正常。我将话语称为"我在2016年的Sharepoint上工作"，然后它只预测了"SharePoint"。作为产品。然后我添加了所有成功的单字产品，并且我还提供了"SharePoint 2016"。和宾果游戏......它奏效了。
I have been training with "I worked on BizTalk", 'I worked on SQL" .... (all single letter words), once it starts predicting the single letter "Technology/Product" properly. I gave the Utterance as "I worked on Sharepoint 2016", then it predicted only "SharePoint" as Product. Then I added all the success-single-word Products and along with them I gave "SharePoint 2016" and bingo... it worked.
到目前为止一直这么好......
So far so good...
这是一个棘手的问题，现在该模型对模式非常稳定在上下文以及各种产品的命名方式中，我希望它能够预测"我在Exchange 2016上工作"。不幸的是它没有用。它只预测
"交易所"单独作为产品。 然后我用相同的上下文（"我工作< product> YYYY"）训练了很多项目。至少现在，模式匹配算法应该理解模式。将话语作为
给出"我在Visual Studio 2016上工作"它无法预测 ""Visual Studio 2016"是产品。它只预测了"Visual Studio"。作为产品。现在请注意，在短语中我还添加了"Visual
Studio"，"Visual Studio 2013"，"Visual Studio 2010"等。并经过培训并成功预测。如果它现在无法预测"Visual Studio 2016"是产品???? 我应该在短语列表中添加多少这样的产品
？现在AI是怎么回事？
Here is the tricky thing, now that the model is pretty much stabilized about the pattern of the context and also the way various Products are named, I wanted it to predict "I worked on Exchange 2016" unfortunately it didn't work. It only predicted "Exchange" alone as a Product. Then I trained a lot with the same context ("I worked on <product> YYYY") with a few items. At least now, the pattern matching algo should have understood the pattern. When gave the Utterance as "I worked on Visual Studio 2016" it failed to predict that "Visual Studio 2016" is the product. It only predicted "Visual Studio" as a Product. Now please note that in the phrases I also added "Visual Studio", "Visual Studio 2013", "Visual Studio 2010" and trained and are being predicted successfully. Howcome it is now not able to predict "Visual Studio 2016" is a Product ???? How many such products should I add to the Phrase list ? How is it AI now ?
想法是基于上下文的，只有一些模式，它应该能够轻松地预测其余的"产品"。我错过了什么???
Idea is based on the context and with a few patterns it should be able to predict rest of the 'Products' with ease. What am I missing ???
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