Semantic logic processing method and system

Abstract

The invention discloses a semantic logic processing method and system. The method comprises the steps of obtaining information to be subjected to semantic analysis; recognizing the information to be subjected to the semantic analysis, and converting the information to be subjected to the semantic analysis into target text information; preprocessing the target text information, generating entity tags corresponding to entity words in the target text information, and adding the entity tags to the target text information to generate first text information; segmenting the first text information to obtain at least one sentence; processing the sentences obtained after segmentation to obtain the intention type, the intention logic relation and the semantic slot value of each sentence; analyzing the semantics of the information to be subjected to the semantic analysis based on the intention type, the intention logic relation and the semantic slot value. The semantic logic processing method and system can improve the precision of semantic understanding and user requirement understanding.

Description

technical field [0001] The invention relates to the technical field of semantic recognition, in particular to a semantic logic processing method and system. Background technique [0002] In recent years, with the rapid development of artificial intelligence, Internet of Things technology and the continuous improvement of big data processing and computing power, smart assistants, as a new product form that combines machine intelligence and conversation, to better connect people and services, people And information as the goal, through multiple rounds of interaction with users using natural language, it brings users a new humanized service experience and service convenience, such as Google Assistant, Amazon Alexa, Microsoft Cortana and Apple Siri. [0003] At present, intelligent assistants mainly use human-computer voice interaction. In the existing technology, the voice information input by the user is usually obtained, and the voice is converted into text through voice reco...

AI Technical Summary

Problems solved by technology

For example: for simple spoken sentences, the simple needs of users can be better understood through intent recognition and semantic slot filling technology, but for longer sentences and complex sentence patterns, the meaning of the sentence is often ignored when understanding the needs of users logic to understand the error

For example, if the user enters "I want to watch Japanese movies, not horror movies", the existing technology will ignore the requirement of "don't" and return the results containing horror movies to the user

In addition, the existing semantic understanding technology usually uses the result of speech recognition as the basic input. However, due to the influence of noise and other factors during speech recognition, there are problems of incorrect sentence segmentation and sentence boundary confusion for multiple sentences. For the same sentence output, it will bring a large error to the subsequent semantic understanding

For example, the sentence entered by the user is "I don't want to watch this, I want to watch Harry Potter", and the resulting sentence after speech recognition is "I don't want to watch this, I want to watch Harry Potter", because the existing semantic understanding technology usually relies on Dependent syntactic analysis is usually oriented to standardized written sentences, and the recognition accuracy is not high for irregular long spoken sentences

[0005] In addition, although the existing spoken language understanding technology can extract named entities through preprocessing technology and sequence tagging technology, and solve incoherent spoken language phenomena such as repetitions, pauses, and redundant filler words in spoken language, it does not solve the problem of long sentences in spoken language. The problem of boundary segmentation and sentence logic understanding makes it impossible to deeply understand the complex needs of users, which is not enough to support the application of intelligent assistants

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