Chinese-Vietnamese Cross-Language News Event Retrieval Incorporating Event Entity Knowledge

Abstract

The existing Chinese-Vietnamese cross-language news event retrieval methods are not sufficiently integrated into the knowledge of event entities in the news field.Furthermore, when there are multiple events in the candidate document, events unrelated to the query sentence interfere with the matching accuracy between the query sentence and the candidate documents, which affects retrieval performance.This study proposes a Chinese-Vietnamese cross-language news event retrieval model incorporating event entity knowledge.The query translation method is used to translate Chinese event query sentences into Vietnamese event query sentences, and the cross-language news event retrieval problem is transformed into a monolingual news event retrieval problem.Considering that there is only a single event in the query sentence, the coexistence of multiple events in the candidate document affects the exact match between the query sentence and the document.The event trigger word is used to divide the event range of the candidate document and to reduce the interference of events unrelated to the query in the document.On this basis, the knowledge graph and event trigger words are used to obtain the rich knowledge representation of event entities.Through the interaction between the query sentence and the document event scope, the ranking features between the knowledge representation of event entities and the knowledge representation of words and event entities are extracted.The experimental results on the Chinese-Vietnamese bilingual news dataset show that compared with baseline models such as BM25, Conv-KNRM, and ATER, the proposed model achieves better cross-language news event retrieval performance;furthermore, using the proposed model, the NDCG and MAP indicators can be improved by up to 0.712 2 and 0.587 2.