LUIS实体未被识别 [英] LUIS entity not recognised
问题描述
我训练了luis模型以识别诸如"What does BLANK mean"或"Can you explain BLANK to me?"这样的话语，称为"getDefinition"的意图.它可以正确识别意图.我还添加了一个名为"topic"的实体，并对其进行了训练，以识别用户所询问的主题.问题在于luis仅能识别出用户正在询问的确切主题，而我之前是否曾在其中一种说法中使用过该特定术语.
I trained my luis model to recognize an intent called "getDefinition" with example utterances such as: "What does BLANK mean" or "Can you explain BLANK to me?". It recognizes the intent correctly. I also added an entity called "topic" and trained it to recognize what topic the user is asking about. The problem is that luis only recognizes the exact topic the user is asking about if I used that specific term in one of the utterances before.
这是否意味着我必须用用户可能会问到的所有可能的术语来训练它，或者是否有某种方法使其能够识别它?
Does this mean I have to train it with all the possible terms a user can ask about or is there some way to have it recognize it anyway?
例如，当我问"What does blockchain mean"时，它正确地将entity(主题)标识为区块链，因为话语中包含了区块链"一词.但是，如果我对另一个主题(例如"what does mining mean"")提出相同版本的问题，则不会将其识别为entity.
For example when I ask "What does blockchain mean" it correctly identifies the entity (topic) as blockchain because the word blockchain is in the utterance. But if I ask the same version of the question about another topic such as "what does mining mean", it doesn't recognize that as the entity.
使用列表或短语列表似乎无法解决问题.我最终希望机器人能够响应数千个主题，因此将每个主题输入列表中既繁琐又不便. LUIS是否可以仅从上下文中识别出它是一个主题?
Using a list or phrase list doesn't seem to be solving the problem. I want to eventually have thousands of topics the bot responds to, entering each topic in a list is tedious and inconvenient. Is there a way LUIS can recognize that its a topic just from the context?
最好的方法是什么?
Same Doubt, Bit Modified. Sorry for Reposting this here.
推荐答案
目前，LUIS不能仅基于意图提取实体.短语列表将帮助LUIS提取没有明确训练数据的令牌.例如，对发声"What does blockchain mean?"的LUIS进行培训并不意味着它将从"What does mining mean?"中提取"mining"，除非在短语列表或列表实体中包括了"mining".除了Nicolas R关于标记不同值的说法外，还要考虑的另一件事是，使用LUIS用于每种文化的语料库中不常见(或根本找不到)的单词可能会导致LUIS在没有帮助的情况下不提取单词(通过词组列表或列表实体).
At the moment LUIS cannot extract an entity just based on the the intent. Phrase lists will help LUIS extract tokens that don't have explicit training data. For example training LUIS with the utterance "What does blockchain mean?" does not mean that it will extract "mining" from "What does mining mean?" unless "mining" was either included in a phrase list, or a list entity. In addition to what Nicolas R said about tagging different values, another thing to consider is that using words not commonly found (or found at all) in the corpuses that LUIS uses for each culture will likely result in LUIS not extracting the words without assistance (either via Phrase list or list entity).
例如，如果您创建了一个以度量单位为单位的LUIS应用程序，则可能不需要以英寸，米，公里或盎司为单位来训练它；您可能必须使用Milliradian，parsec等字词，甚至公里数之类的其他文化拼写来训练它.否则，这些单词很可能不会被LUIS提取.如果用户提供了令牌"Planck unit"，则LUIS可能会提供错误的提取结果，并返回"unit"作为度量实体，而不是"Planck unit".
For example, if you created a LUIS application that dealt with units of measurement, while you might not be required to train it with units such as inch, meter, kilometer or ounce; you would probably have to train it with words like milliradian, parsec, and even other cultural spellings like kilometre. Otherwise these words would most likely not be extracted by LUIS. If a user provided the tokens "Planck unit", LUIS might provide a faulty extraction where it returns "unit" as the measurement entity instead of "Planck unit".
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