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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. 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 chwt, we only need to solve most of the problems. Second, preliminary knowledge Match Q with Q and compare the similarity of two sentences. In deep learning, you can use Q to match A because of long-term memory. Search and match 1 Knowledge base stored questions and answers 2 Retrieval: Search related issues 3 Match: sort the 2.

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Use unicode. Meaning: ChatterBot is a chat robot engine based on machine twxta, built on Python, the main feature is that it can learn memorize and learn match from existing conversations. The N-dimensional vector is used to compare the similarity between vocabulary.

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Not related to context, Intelligent Recommendation. You can also use Naive Bayes for adapter selection. The editing distance is 3, which is very small. The editing distance is 3, which is very small.

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Json is generally not used in a production environment because the speed is too slow Mongo Textz Adapter: MongoDB database to store conversation data 4. Word meaning matching: For example, what kind of information do you like; what kind of documentation do you like. Time Logic Adapter: Handle time-related questions. Search and match 1 Knowledge base stored questions and chat or bait 2 Retrieval: Search related issues 3 Match: sort the 2.

You can also use Naive Bayes for adapter selection.

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QN, select Answer corresponding to Qi with a small editing distance as a reply. In deep learning, you can use Q to match A because of long-term memory.

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The N-dimensional vector is used to compare the similarity between vocabulary. Json is generally not textx in a production environment because the speed is too slow Mongo Database Adapter: MongoDB database to store conversation textz 4. Engineering considerations: 1 The structure de is clear and modular 2 Functional analysis, decoupling without mutual interferencepluggable and expandable components 2. Chatterbot chat robot application Each part is deed with a different "Adapter" Adapter 1.

QN, select Answer corresponding to Qi with a small editing distance as a reply. Determine what scenario the question asked by the user belongs to. Meaning match.

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Python in the string type, the default UTF-8 encoding, a Chinese character is represented by three bytes. 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. Scene matching: Give a sentence to determine which category it belongs to.

Matching by scene can speed up the matching speed. Edit distance matching Application: spelling correction and intelligent completion. Second, preliminary knowledge Match Q with Q and compare the similarity of two sentences.

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Meaning match. What books do you like and what movies do you like.

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It is believed that the information and documents are similar. For example, user question Q, and the existing editing distance of Caht Word meaning matching: For example, what kind of information do you like; what kind of documentation do you like. Text matches.

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For chatterbots with short conversations, users only answer questions based on the sentence. Edit distance matching Application: spelling correction and intelligent completion.

Solution: word vector NLTK wordnet library: list of synonyms. The conversation data is stored in Json format. Text matches.

Meaning: ChatterBot 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. Second, preliminary knowledge Match Q with Q and compare the similarity of two sentences. Determine what scenario the question asked by the user belongs to. In deep learning, you can use Q to match A because of long-term memory.

Python in the string type, the default UTF-8 encoding, a Chinese character is represented by three bytes. 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 world wide chat rooms solve most of the problems.

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Mathematical Evaluation Adapter: involves mathematical operations. For chatterbots with short conversations, users only answer questions based on the sentence. Engineering considerations: 1 The structure de is clear and modular 2 Functional analysis, decoupling without mutual interferencepluggable and expandable components 2. But it is a sentence with 2 meanings.

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The conversation data is stored in Json format.

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