Russian semantic role labeling method, system and device and storage medium

Abstract

The invention provides a Russian semantic role labeling method, system and device and a storage medium. Aiming at the characteristics of Russian, the relation between predicates and arguments is reasonably utilized, the semantic role annotation of the Russian can be well realized, the semantic role annotation accuracy is improved, and higher annotation performance is obtained. The method comprisesthe following steps: 1, preprocessing corpora, extracting classification characteristics, and converting the classification characteristics into characteristic vectors; 2, constructing classificationmodels based on neural networks of different architectures, and inputting classification features into the classification models for training to obtain trained classification models; 3, based on a voting fusion mechanism, fusing the trained classification models according to the principle that a small number obeys a majority to obtain a fusion model; 4, inputting the preprocessed corpus into a fusion model, identifying a semantic role, attaching a prediction label, and performing performance evaluation on an obtained semantic role prediction result.

Description

technical field [0001] The invention relates to the field of natural language processing in computational linguistics, in particular to a method, system, device and storage medium for marking Russian semantic roles. Background technique [0002] Semantic role labeling is an important intermediate step in many natural language understanding tasks (such as information extraction, discourse analysis, and in-depth question answering), and is an important aspect of knowledge graph construction. , machine translation, automatic summarization, information extraction and other tasks to produce direct and powerful help. Moreover, semantic role labeling is a shallow semantic analysis technology, and its development will surely drive the progress of other deep semantic tasks. [0003] In recent years, deep learning has made great progress in the field of machine learning and has been widely used in many fields of natural language processing. However, the Russian language has not been...

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But this method does not make good use of the self-learning abilit

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