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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 long-tailed, we only need to solve most of the cat.

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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Edit distance matching Application: spelling correction and intelligent completion. For example, user question Q, and the existing editing distance of Q QN, select Answer corresponding to Qi with a small editing distance as a reply. Python in the string type, the default UTF-8 encoding, a Chinese character is represented by three bytes. Use unicode.

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What books do you like and what movies do you like. The editing distance is 3, which is very small.

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But it is a sentence with 2 meanings. I think it is more meaningful to treat each word than to treat it equally.

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Word meaning matching: For example, what kind of information do you like; what kind of documentation do you like. It is believed that etxta information and documents are similar.

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Solution: word vector NLTK wordnet library: list of synonyms. Determine which tezta relationships are close Build your own table of synonyms: Use word2vec to learn Chinese after word segmentation.

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The N-dimensional vector is used to compare the similarity between vocabulary. Scene matching: Give a sentence to determine which category it belongs to.

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Determine what scenario the question asked by the user belongs to. Matching by scene can speed up the matching speed.

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Chatterbot chat robot application Each part is deed with a different "Adapter" Adapter 1. 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. Text matches.

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Meaning match. Time Logic Adapter: Handle time-related questions. Mathematical Evaluation Adapter: involves mathematical operations.

The conversation data is stored in Json format. 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.

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You can also use Naive Bayes for adapter selection. For chatterbots with short conversations, users only answer questions based on the sentence.

Not related to context, Intelligent Recommendation.

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