一种易用的实体识别消歧系统评测框架

Abstract

实体识别消歧是知识库扩充和信息抽取的重要技术之一。近些年该领域诞生了很多研究成果，提出了许多实体识别消歧系统。但由于缺乏对这些系统的完善评测对比，该领域依然处于良莠淆杂的状态。因此很有必要设计一个评测框架对各个系统进行统一评测。本文提出一个实体识别消歧系统的统一评测框架，用于公平地比较各个实体识别消歧系统的效果。该框架代码开源，可以采用新的系统、数据集、评测机制扩展。通过该框架评测实体系统，可以分析得到系统各个模块的优劣之处。本文分析对比了几个公开的实体识别消歧系统，并总结出了一些有用的结论。

Keywords

实体识别消歧 / 评测框架 / 信息抽取

References

[1] Bizer , C., Lehmann , J., Kobilarov , G., , 2009. DBpedia—a crystallization point for the Web of Data. Web Semant.Sci. Serv. Agents World Wide Web, 7(3):154–165.

[2] Carletta , J., 1996. Assessing agreement on classification tasks: the kappa statistic.Comput. Ling., 22(2):249–254.

[3] Cornolti , M., Ferragina , P., Ciaramita , M., 2013. A framework for benchmarking entity-annotation systems. Proc. 22nd Int. Conf. on World Wide Web, p.249–260.

[4] Finkel , J.R., Grenager , T., Manning , C., 2005. Incorporating non-local information into information extraction systems by Gibbs sampling. Proc. 43rd Annual Meeting on Association for Computational Linguistics, p.363–370.

[5] Hachey , B., Nothman , J., Radford , W., 2014. Cheap and easy entity evaluation. Proc. 52nd Annual Meeting of the Association for Computational Linguistics, p.464–469.

[6] Hoffart , J., Yosef , M.A., Bordino , I., , 2011. Robust disambiguation of named entities in text. Proc. Conf. on Empirical Methods in Natural Language Processing, p.782–792.

[7] Ji , H., Nothman , J., Hachey , B., , 2014. Overview of TAC-KBP2014 entity discovery and linking tasks. Proc. Text Analysis Conf.

[8] Ji , H., Nothman , J., Hachey , B., , 2015. Overview of TAC-KBP2015 tri-lingual entity discovery and linking. Proc. Text Analysis Conf.

[9] Ling , X., Singh , S., Weld , D.S., 2015. Design challenges for entity linking.Trans. Assoc. Comput. Ling., 3:315–328.

[10] Milne , D., Witten , I.H., 2008. Learning to link with Wikipedia. Proc. 17th ACM Conf. on Information and Knowledge Management, p.509–518.

[11] Milne , D., Witten , I.H., 2013. An open-source toolkit for mining Wikipedia.Artif. Intell., 194:222–239.

[12] Ratinov , L., Roth , D., 2009. Design challenges and misconceptions in named entity recognition. Proc. 13th Conf. on Computational Natural Language Learning, p.147–155.

[13] Ratinov , L., Roth , D., Downey , D., , 2011. Local and global algorithms for disambiguation to Wikipedia. Proc. 49th Annual Meeting of the Association for Computational Linguistics: Human Language, p.1375–1384.

[14] Ristad , E.S., Yianilos , P.N., 1998. Learning string-edit distance.IEEE Trans. Patt. Anal. Mach. Intell., 20(5):522–532.

[15] Rizzo , G., van Erp , M., Troncy , R., 2014. Benchmarking the extraction and disambiguation of named entities on the semantic web. Proc. 9th Int. Conf. on Language Resources and Evaluation.

[16] Shen , W., Wang , J., Han , J., 2015. Entity linking with a knowledge base: issues, techniques, and solutions.IEEE Trans. Knowl. Data Eng., 27(2):443–460.

[17] Spitkovsky , V.I., Chang , A.X., 2012. A cross-lingual dictionary for English Wikipedia concepts. 8th Int. Conf. on Language Resources and Evaluation, p.3168–3175.

[18] Usbeck , R., Röder , M., Ngonga Ngomo , A.C., , 2015. GERBIL: general entity annotator benchmarking framework. Proc. 24th Int. Conf. on World Wide Web, p.1133–1143.

RIGHTS & PERMISSIONS

Zhejiang University and Springer-Verlag Berlin Heidelberg