Question and answering (QA) system realization method based on deep learning and topic model

Abstract

The invention discloses a question and answering (QA) system realization method based on deep learning and a topic model. The method comprises the steps of: S1, inputting a question sentence to the Twitter LDA topic model to obtain a topic type of the question sentence, extracting a corresponding topic word, and indicating the input question sentence and the topic word as word vectors; S2, inputting word vectors of the input question sentence to a recurrent neural network (RNN) for encoding to obtain an encoded hidden-layer state vector of the question sentence; S3, using a joint attention mechanism and combining local and global hybrid semantic vectors of the question sentence by a decoding recurrent neural network for decoding to generate words; S4, using a large-scale conversation corpus to train a deep-learning topic question and answering model based on an encoding-decoding framework; and S5, using the trained question and answering model to predict an answer to the input questionsentence, and generating answers related to a question sentence topic. The method makes up for the lack of exogenous knowledge of question and answering models, and increases richness and diversity of answers.

Description

technical field [0001] The present invention relates to the technical field of question answering systems in natural language processing, in particular to a question answering system implementation method based on deep learning and topic models. Background technique [0002] Human-machine dialogue is a challenging task in Natural Language Processing (NLP) and real artificial intelligence. Existing question and answering (QA) systems include task-specific question answering systems and open domain question answering system. The question answering system is designed to help humans complete specific tasks, such as completing instructions issued by people and guiding people to complete a certain task. At the same time, the question answering system is designed to complete the process of imitating natural human chatting in different chat contexts. A lot of research has focused on dialogue systems before. With the explosive growth of social media data on the Internet, a large amo...

AI Technical Summary

Problems solved by technology

The first is the template matching model. We can design rules to let the dialogue model know that when it encounters questions in different languages, it will reply with different content. This method needs to design multiple rules and consider the order of priority among the rules. The better the content of the answer, but the model is less transferable

2. In addition, the retrieval model is similar to a search engine. The difference is that the retrieval model gives us answers. This model mainly matches question-answer pairs, depending on the similarity between the input question sentence and the answer candidate set, but generates The content depends on the data set and cannot create new answer content

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