Event atlas construction system and method based on multi-dimensional feature fusion and dependency syntax
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A multi-dimensional feature and dependency syntax technology, applied in the field of electronic information, can solve problems such as ignoring the importance of spatial relationships, imperfect evaluation methods, and single relationships between events
Active Publication Date: 2020-08-25
XI AN JIAOTONG UNIV
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[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 o
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Embodiment 1
[0165] At around 14:28 on May 12, 2008, an earthquake with a magnitude of 7.8 occurred in Wenchuan County.
[0166] After module 1 event extraction, a structured event is obtained, and the time element, location element, and trigger word of the event can be obtained, while The elements of time and place are the basis of temporal relationship extraction and spatial relationship extraction between events. Due to the uniqueness of news expression, the elements of time and place may be incomplete or inconsistent. Time serialization and locations are standardized by provinces and municipalities through event correction, which facilitates the extraction of temporal and spatial relations. In this embodiment, after event correction Enhanced structured event .
Embodiment 2
[0168] After the earthquake in Wenchuan County, Sichuan Province on May 12, 2008, it had a certain impact on Zhaotong, Yunnan and other places, and some houses have collapsed.
[0169]Events are obtained after module 1 event extraction, event1, event2.
[0170] Event1, event2. The event in Embodiment 1 can be aligned with event1 in Embodiment 2 through event alignment.
[0171] After module 3, the timing relationship can be extracted, .
[0172] After passing module 4, the first-order implicit causality can be extracted, .
[0173] Through the enhanced structured events obtained by the above modules and the multi-dimensional relationship between events, the event graph can be constructed. Such as Figure 4 and Figure 5 .
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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, ...
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