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Engineering considerations: 1 The structure de is clear and modular 2 Functional analysis, decoupling without mutual interferencepluggable and expandable components 2.

Mathematical Evaluation Adapter: involves mathematical operations.

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We do hope to see you there, whether you are an experienced tweeter or someone who just wants to put a toe in the water - Monday at 7. Scene matching: Give a sentence chzt determine which category it belongs to. Solution: word vector NLTK wordnet library: list of synonyms.

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But it is a sentence with 2 meanings. Json is generally not used in a production environment because the speed is too slow Mongo Database Adapter: MongoDB database to store conversation data 4. We are going to be using the questions on the left to prompt conversation.

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If you'd like to in but are unsure how to our website content xhat, Bev Humphrey, has prepared a how to sheet to help you, PDF below. Determine which word relationships are close Build your own table of synonyms: Use word2vec to learn Chinese after word segmentation.

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The conversation data is stored in Json format. Chatterbot chat robot application Each part is deed with a different "Adapter" Adapter 1. Use unicode.

What books do you like and what movies do you like. The N-dimensional vector is used to compare the similarity between vocabulary.

Edit distance matching Application: spelling correction and intelligent completion. Meaning: Fexta is a chat robot engine based on machine learning, built on Python, the main feature is that it can learn memorize and learn match from existing conversations. For example, user question Q, and the existing editing distance of Q Second, preliminary knowledge Match Q with Q and compare the similarity of two sentences.

It is believed that the information and documents are similar. The hashtag for the chat will be ukslachat. Word meaning matching: For example, what kind of information do you like; what kind of documentation do you like. Text matches. The editing distance is 3, which is very small.

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Meaning match. Time Logic Adapter: Handle time-related questions.

Determine what scenario the question asked by the user belongs to. I think it is more meaningful to treat each word than to treat it equally. Search and match 1 Knowledge base stored questions and answers 2 Retrieval: Chay related issues 3 Match: sort the 2. QN, select Answer corresponding to Qi with a small editing distance as a reply.

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The algorithm and machine learning perspective: 1 Algorithm brief answer, data feature drive 2 Sceneization and vertical field Customer service question and answer questions are very long-tailed, we only need to solve most of the problems. Python in the string type, the default UTF-8 encoding, a Chinese character is represented by three bytes. You can txeta use Naive Bayes for adapter selection.

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In deep learning, you can use Q to match A because of long-term memory. Engineering considerations: 1 The structure de is clear and modular 2 Functional analysis, decoupling without mutual interferencepluggable and expandable components 2. Matching by scene can speed up the matching speed.

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For chatterbots with short conversations, users only answer questions based on the sentence. Not related to context, Intelligent Recommendation.

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