期刊文献+
共找到4篇文章
< 1 >
每页显示 20 50 100
Topological Features Based Entity Disambiguation 被引量:1
1
作者 Chen-Chen Sun De-Rong Shen Yue Kou Tie-Zheng Nie Ge Yu 《Journal of Computer Science & Technology》 SCIE EI CSCD 2016年第5期1053-1068,共16页
This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is n... This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy. 展开更多
关键词 entity disambiguation topological feature CLUSTERING random walk with restarts
原文传递
An easy-to-use evaluation framework for benchmarking entity recognition and disambiguation systems 被引量:1
2
作者 Hui CHEN Bao-gang WEI +2 位作者 Yi-ming LI Yong-huai LIU Wen-hao ZHU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第2期195-205,共11页
Entity recognition and disambiguation (ERD) is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD syst... Entity recognition and disambiguation (ERD) is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework (EUEF), which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis. 展开更多
关键词 entity recognition and disambiguation (ERD) Evaluation framework Information extraction
原文传递
Integrating Manifold Knowledge for Global Entity Linking with Heterogeneous Graphs 被引量:2
3
作者 Zhibin Chen Yuting Wu +1 位作者 Yansong Feng Dongyan Zhao 《Data Intelligence》 EI 2022年第1期20-40,共21页
Entity Linking(EL)aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base(KB),which has recently been dominated by global models.Although many global EL methods ... Entity Linking(EL)aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base(KB),which has recently been dominated by global models.Although many global EL methods attempt to model the topical coherence among all linked entities,most of them failed in exploiting the correlations among manifold knowledge helpful for linking,such as the semantics of mentions and their candidates,the neighborhood information of candidate entities in KB and the fine-grained type information of entities.As we will show in the paper,interactions among these types of information are very useful for better characterizing the topic features of entities and more accurately estimating the topical coherence among all the referred entities within the same document.In this paper,we present a novel HEterogeneous Graph-based Entity Linker(HEGEL)for global entity linking,which builds an informative heterogeneous graph for every document to collect various linking clues.Then HEGEL utilizes a novel heterogeneous graph neural network(HGNN)to integrate the different types of manifold information and model the interactions among them.Experiments on the standard benchmark datasets demonstrate that HEGEL can well capture the global coherence and outperforms the prior state-of-the-art EL methods. 展开更多
关键词 entity linking Heterogeneous graph Graph neural network entity disambiguation Knowledge base
原文传递
Disambiguating named entities with deep supervised learning via crowd labels
4
作者 Le-kui ZHOU Si-liang TANG +2 位作者 Jun XIAO Fei WU Yue-ting ZHUANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第1期97-106,共10页
Named entity disambiguation (NED) is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discr... Named entity disambiguation (NED) is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom (via human-labeled crowd labels) and deep learning (via human-generated data) for the NED task. In particular, we devise a crowd model to elicit the underlying features (crowd features) from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network (DCNN). The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge (via crowd labels) into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered. 展开更多
关键词 Named entity disambiguation Crowdsourcing Deep learning
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部