Analysis on Function Orientation and Development Countermeasures of New Agricultural Business Entities

Along with the rapid advance of industrialization and urbanization process, fostering new agricultural business entities become inevitable for agricultural transformation and the construction of agricultural modernization in China. The status of the new agricultural business entities determines the level of modern agricultural development. In recent years, new agricultural business entities have grew rapidly. However, there are still many problems including the difficulties in financing loans, inadequate agricultural insurance system, bad implementation of agricultural subsidies, jagged agricultural talents and so on. In order to foster new agricultural business entities, countermeasures should be carried out to ensure financial support, perfect the agricultural insurance, strengthen the level of agricultural subsidies, strive to develop the degree of specialization agricultural operators and so on.

Improving entity linking with two adaptive features

Entity linking(EL)is a fundamental task in natural language processing.Based on neural networks,existing systems pay more attention to the construction of the global model,but ignore latent semantic information in the local model and the acquisition of effective entity type information.In this paper,we propose two adaptive features,in which the first adaptive feature enables the local and global models to capture latent information,and the second adaptive feature describes effective information for entity type embeddings.These adaptive features can work together naturally to handle some uncertain entity type information for EL.Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets,and the best average performance on out-domain datasets.These results indicate that the proposed adaptive features,which are based on their own diverse contexts,can capture information that is conducive for EL.

CN-DBpedia2: An Extraction and Verification Framework for Enriching Chinese Encyclopedia Knowledge Base

Knowledge base plays an important role in machine understanding and has been widely used in various applications, such as search engine, recommendation system and question answering. However, most knowledge bases are incomplete, which can cause many downstream applications to perform poorly because they cannot find the corresponding facts in the knowledge bases. In this paper, we propose an extraction and verification framework to enrich the knowledge bases. Specifically, based on the existing knowledge base, we first extract new facts from the description texts of entities. But not all newly-formed facts can be added directly to the knowledge base because the errors might be involved by the extraction. Then we propose a novel crowd-sourcing based verification step to verify the candidate facts. Finally, we apply this framework to the existing knowledge base CN-DBpedia and construct a new version of knowledge base CN-DBpedia2, which additionally contains the high confidence facts extracted from the description texts of entities.