Graph-ranking collective Chinese entity linking algorithm

Xie, Tao; Wu, Bin; Jia, Bingjing; Wei, Bai

doi:10.1007/s11704-018-7175-0

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Shengchen et al [10] proposed a domain-integrated entity linking method based on relation index and representation learning, to address the problem that existing entity linking methods cannot combine text information and knowledge base information well. Xie et al [11] proposed a GRCCEL (graph-ranking collective Chinese entity linking) algorithm, aiming at the problem of ignoring the entity semantic association relationship and being limited by the size of knowledge graph, using the structural relationship between entities in the knowledge graph and the additional background information provided by external sources of knowledge base, to obtain more semantic and structural information; the purpose is to obtain stronger ability to distinguish similar entities. Xia et al [12] proposed an integrated entity link algorithm that uses topic distribution to represent knowledge and generate candidate entities.…”

Section: Introductionmentioning

confidence: 99%

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

Wang

Zhou

et al. 2022

Journal of Sensors

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To solve the problem of unclear entity boundaries and low recognition accuracy in Chinese text, we construct the crop dataset and propose a Bert-binary-based entity link method. Candidate entity sets are generated through entity matching in multiple data sources. The Bert-binary model is called to calculate the correct probability of the candidate entity, and the entity with the highest score is screened for linking. In comparative experiments with three models on the crop dataset, the F 1 value is increased by 2.5% on the best method or by 8.8% on average. The experimental results show the effectiveness of Bert-binary method in this paper.

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Section: Introductionmentioning

confidence: 99%

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

Wang

Zhou

et al. 2022

Journal of Sensors

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“…The size of each vector dimension is the same. The concatenated vectors are then fed into a multi-layer perceptron, and which is also used as the input of a softmax to predict the similarity between the mention m and the entity e. The details of this process are given in Equations ( 6) and (7).…”

Section: Entity Selector With Multiple Featuresmentioning

confidence: 99%

“…Therefore, the EL problem can be cast as an entity ranking problem, and the entity with the highest score is predicted as the correct match. The PageRank and Random Walk algorithm [6][7][8] usually tend to capture topic consistency between the mentions in the text. With ABACO [9], a sub-graph is extracted from a KB and pruned based on nodes' degree of centrality.…”

Section: Introductionmentioning

confidence: 99%

An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance

et al. 2022

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In the era of big data, a large amount of unstructured text data springs up every day. Entity linking involves relating the mentions found in the texts to the corresponding entities, which stand for objective things in the real world, in a knowledge base. This task can help computers understand semantics in the texts correctly. Although there have been numerous approaches employed in research such as this, some challenges are still unresolved. Most current approaches utilize neural models to learn important features of the entity and mention context. However, the topic coherence among the referred entities is frequently ignored, which leads to a clear preference for popular entities but poor accuracy for less popular ones. Moreover, the graph-based models face much noise information and high computational complexity. To solve the problems above, the paper puts forward an entity linking algorithm derived from the asymmetric graph convolutional network and the contextualized semantic relevance, which can make full use of the neighboring node information as well as deal with unnecessary noise in the graph. The semantic vector of the candidate entity is obtained by continuously iterating and aggregating the information from neighboring nodes. The contextualized relevance model is a symmetrical structure that is designed to realize the deep semantic measurement between the mentions and the entities. The experimental results show that the proposed algorithm can fully explore the topology information of the graph and dramatically improve the effect of entity linking compared with the baselines.

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Enriching Context Information for Entity Linking with Web Data

Wang

Shen

et al. 2020

J. Comput. Sci. Technol.

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Graph-ranking collective Chinese entity linking algorithm

Cited by 8 publications

References 7 publications

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance

Enriching Context Information for Entity Linking with Web Data

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