Fine-grained numerical information extraction method based on joint learning model

Abstract

The invention discloses a fine-grained numerical information extraction method based on a joint learning model. The fine-grained numerical information extraction method comprises the following steps:preprocessing an input text; candidate numerical values are recognized according to the numerical value correlation regular expression, and the numerical values serve as trigger words and are splicedwith front and back lexical examples of the trigger words to serve as input of a joint learning model; accessing the bottom layer of the joint learning model to a word embedding layer, and adding theposition information of a numerical trigger word to the output of the word embedding layer to obtain the final distributed representation of each lexical example; accessing a subsequent feature extraction network based on distributed representation, and respectively accessing networks of specific tasks at a high level: accessing a classification network to obtain a semantic type of numerical information, and accessing a sequence labeling network to obtain a plurality of semantic roles related to numerical trigger words; combining outputs of specific tasks to obtain a numerical information unit; for a plurality of numerical information units, the composite relationship between the numerical information units is judged based on a statistical method. The method does not need artificial designfeatures, and is high in accuracy and strong in generalization ability.

Description

technical field [0001] The invention belongs to the technical field of information extraction in the field of natural language processing, and in particular relates to a method for extracting fine-grained numerical information based on a joint learning model. Background technique [0002] With the rapid growth of World Wide Web data, people expect to obtain information more efficiently and comprehensively from all kinds of data, so as to use it in various applications such as intelligent question answering and search recommendation. Among them, text data has the characteristics of a large amount of data and a large amount of information, and has become a very important data source in information extraction. Scientific researchers and the industry are very concerned about the research of related technologies, and have defined some classic information extraction tasks, such as: named entity recognition, relationship extraction, event extraction, etc. [0003] In addition to t...

AI Technical Summary

Problems solved by technology

Rule- and semantic-analysis-based methods leverage expert knowledge to provide good interpretability, but their limited flexibility and adaptability somewhat constrain their performance on diverse real texts

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