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Machine reading understanding model based on knowledge graph gain

A reading comprehension and knowledge graph technology, applied in the field of machine reading comprehension models, can solve problems such as unsatisfactory output results

Active Publication Date: 2021-05-18
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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  • Abstract
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Problems solved by technology

Then, when the traditional model encounters such documents, it will produce unsatisfactory output results

Method used

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  • Machine reading understanding model based on knowledge graph gain
  • Machine reading understanding model based on knowledge graph gain
  • Machine reading understanding model based on knowledge graph gain

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Embodiment Construction

[0016] In order to make the technical means and effects realized by the present invention easy to understand, the present invention will be described in detail below in conjunction with the embodiments and accompanying drawings.

[0017]

[0018] figure 1 is a structural block diagram of a machine reading comprehension model based on knowledge graph gain in an embodiment of the present invention, figure 2 It is a schematic flowchart of a machine reading comprehension model based on knowledge graph gain in an embodiment of the present invention.

[0019] like figure 1 and figure 2 As shown, a machine reading comprehension model 100 based on knowledge graph gain in this embodiment is used to receive a text data set including text documents and questions and a vocabulary generated by itself according to the text data set, and according to the text document The content gets answers to questions, including: document question arrangement module 10, named entity recognition mo...

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Abstract

The invention provides a machine reading understanding model based on knowledge graph gains, which is used for receiving a text data set including text documents and questions and a vocabulary automatically generated according to the text data set and obtaining answers to the questions according to the content of the text documents. The machine reading understanding model comprises a document question arrangement module, a named entity recognition module used for performing named entity recognition processing on the text data set; an ERNIE context language module; an external knowledge base which comprises a WordNet knowledge base and a ConceptNet knowledge base and is used for receiving the vocabulary and correspondingly generating a WordNet knowledge feature vector and a ConceptNet knowledge feature vector; a knowledge matching and connecting layer which is used for connecting corresponding word vectors with WordNet knowledge feature vectors and ConceptNet knowledge feature vectors for entities which are successfully matched in text documents and questions; an attention calculation unit which is used for performing bidirectional attention operation and self-attention operation on the vectors correspondingly to obtain answers; and a result generation unit which is used for receiving and judging the output answer.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a machine reading comprehension model based on knowledge map gain. Background technique [0002] Machine reading comprehension is a subtask under natural language understanding. Its goal is to give a piece of text data and a question related to the text, and the machine will analyze the text section and give the answer to the question. Compared with other traditional natural language processing tasks (such as part-of-speech judgment, entity recognition, grammatical analysis, etc.), machine reading comprehension not only requires the machine to learn to represent natural language, but also to understand, analyze, and finally generate output sentences. [0003] The most widespread application of machine reading comprehension is to enhance the performance of human-computer interaction question answering systems. In the most primitive question answering system, the...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/332G06F16/953G06F40/295
CPCG06F16/367G06F16/3329G06F16/953G06F40/295
Inventor 徐菲菲张文楷
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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