2 results about "Bilingual corpus" patented technology

The application discloses a kind of fusion bilingual knowledge's green-washing behavior knowledge graph construction method, belong to natural language processing and knowledge graph field.The method includes: S1, from enterprise English environmental protection report, Chinese social responsibility report and so on multi-source heterogeneous data collection bilingual corpus constructs bilingual green-washing corpus;S2, based on bilingual pre-training language model carries out named entity recognition and cross-language entity alignment;S3, based on bilingual pre-training language model and green-washing behavior mode constraint rule carries out relationship extraction and cross-language consistency verification;S4, bilingual knowledge is fused, conflict resolution strategy is handled contradictory information, and knowledge reasoning is completed;S5, bilingual green-washing knowledge is stored to graph database in the form of knowledge graph, and based on knowledge graph carries out green-washing behavior evaluation.The application breaks through the language barrier limit, realizes the integrated identification and structured knowledge representation of bilingual green-washing behavior of multinational enterprise.

The application belongs to the technical field of natural language processing, and relates to a Tibetan-Chinese bilingual corpus collaborative labeling and versioned publishing system and method. Through the system architecture formed by the front-end interaction module, the business processing module, the data storage module and the basic management and control module, relying on the Tibetan-Chinese bilingual labeling, intelligent collaborative management and control, corpus version management and standardized publishing core units integrated by the business processing module, cooperating with the distributed correlation index storage mechanism of the data storage module, the fine-grained permission and operation log management and control function of the basic management and control module, the core technical problems of the Tibetan language characteristics adaptation deficiency, the low efficiency and frequent conflicts of multi-person collaborative labeling, the lack of version tracing and quality grading control of the corpus, the non-standard publishing process and the disconnection of the corpus and the downstream model in the prior art are solved. The accuracy of Tibetan-Chinese bilingual corpus labeling and storage is ensured through exclusive Tibetan adaptation processing, and the orderly promotion of multi-person labeling is realized through modular collaborative management and control.