Event atlas construction system and method based on multi-dimensional feature fusion and dependency syntax

Abstract

The invention discloses an event atlas construction system and method based on multi-dimensional feature fusion and dependency syntax. The event graph construction method based on multi-dimensional feature fusion and dependency syntax is realized through joint learning of event extraction, event correction and alignment based on multi-dimensional feature fusion, relationship extraction based on enhanced structured events, causal relationship extraction based on dependency syntax and graph attention network and an event graph generation module. According to the event graph construction method and device, the event graph is constructed through the quintuple information of the enhanced structured events and the relations between the events in four dimensions, and the defects that in the priorart, event representation is simple and depends on an NLP tool, the event relation is single, and the influence of the relations between the events on event graph construction is not considered at the same time are overcome. According to the event atlas construction method provided by the invention, the relationships among the events in four dimensions can be randomly combined according to different downstream tasks, and the structural characteristics of the event atlas are learned to be associated with potential knowledge, so that downstream application is assisted.

Description

【Technical field】 [0001] The invention belongs to the technical field of electronic information, and relates to a system and method for constructing event graphs based on multidimensional feature fusion and dependency syntax. 【Background technique】 [0002] The knowledge graph technology in the era of big data connects and stores massive amounts of information in structured "entity-relationship-entity" triples, so that massive unstructured and semi-structured information in the network can be effectively organized and constituted. It has achieved outstanding application effects in related fields such as justice, finance, and emergencies, and has become one of the important directions of artificial intelligence applications. However, with the growth of business needs, the entities and the relationship between entities described in traditional knowledge graphs can no longer meet the needs of different intelligent systems for knowledge reasoning and services. On the one hand, ...

AI Technical Summary

Problems solved by technology

[0005] Second, the relationship between events is single, and most studies only focus on causal or temporal relationships.

However, existing research often ignores the importance of spatial relationships and deep semantic relationships;

[0006] The third is that the influence of the relationship between events and events on the construction of event graphs is not considered at the same time, and the existing research often only considers the construction quality of event graphs from the extraction performance of structured meta-events or the extraction performance of the relationship between events. This evaluation method not perfect;

[0007] Fourth, due to the multiple sources of text and the uniqueness of expression, coreference events may exist within multiple texts or a single text, resulting in relatively redundant event graphs

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