News emotion entity extraction method based on remote supervision

Abstract

The invention discloses a news emotion entity extraction method based on remote supervision. The method comprises the steps: crawling official news website news corpus and caching the corpus to a local warehouse; preprocessing the crawled news corpus to obtain news corpus segmented into sentences; constructing a key entity knowledge base, and automatically labeling the news corpus segmented into sentences according to the knowledge base; training an emotion sentence extraction model by using the labeled news corpus to enable the model to have the capability of performing automatic emotion judgment on the input sentences; using the extracted sentiment sentences, and taking the sentiment sentences as a training set of a sentiment entity extraction model for training; and crawling the news corpus, segmenting the news corpus into sentences, inputting the news corpus segmented into sentences into the trained emotion sentence extraction model to extract emotion sentences, and inputting the extracted emotion sentences into the trained emotion entity extraction model to obtain emotion entities. According to the method, a data set with noise is generated for a large number of samples in a remote supervision mode for model training, and the model training efficiency is improved.

Description

technical field [0001] The invention belongs to the field of computer artificial intelligence, specifically a method for extracting news emotional entities based on remote supervision. Background technique [0002] Due to the unique application background and text expression method of named entity recognition in the field of news, researchers have explored it. Feng Yuntian et al. proposed the classification principles of entities such as personnel, military ranks, military positions, military institutions, and facilities, and constructed a corpus based on standardized texts such as combat documents, duty documents, and military documents. It uses a small amount of manually labeled training corpus to train the CRF model, and the trained model performs entity recognition on the unlabeled test corpus, and the model obtains a recognition effect with an F value of 90.9% on the test corpus. You Fei et al. established a DNN-based weapon entity recognition model for weapon named en...

AI Technical Summary

Problems solved by technology

The social media data expression represented by Weibo is not standardized, there are a large number of spoken expressions, there are no specific rules, and it is difficult to identify entities

[0008] There are currently no publicly available corpus datasets and entity taxonomies for journalism, hampering OSINT research efforts

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