Abstract
Named entity disambiguation (NED) is the task of linking ambiguous mentions in text to their corresponding entities in a given knowledge base, such as Wikipedia. State-of-the-art NED solutions harness neural networks to generate abstract representations, i.e., embeddings, of mentions and entities, based on which the disambiguation process can be achieved by finding entity with the most similar representation to mention. Nevertheless, the coherence among mentions, and their corresponding entities, is yet neglected. To fill this gap, in this work, we put forward intra, an approach effectively integrating embedding features into a collective disambiguation framework, i.e., probabilistic graphical model. Markov Chain Monte Carlo sampling and SampleRank algorithm are implemented for model parameters learning and inference. We evaluate intra on existing dataset against several state-of-the-art NED systems, which validates the effectiveness of our proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andrieu, C., de Freitas, N., Doucet, A., Jordan, M.I.: An introduction to MCMC for machine learning. Mach. Learn. 50(1–2), 5–43 (2003)
Ferragina, P., Scaiella, U.: Fast and accurate annotation of short texts with Wikipedia pages. IEEE Softw. 29(1), 70–75 (2012)
Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. TACL 2, 231–244 (2014)
Dredze, M., McNamee, P., Rao, D., Gerber, A., Finin, T.: Entity disambiguation for knowledge base population. In: COLING 2010, Beijing, China, pp. 277–285 (2010)
Ganea, O., Ganea, M., Lucchi, A., Eickhoff, C., Hofmann, T.: Probabilistic bag of hyperlinks model for entity linking. In: WWW 2016, Montreal, Canada, pp. 927–938 (2016)
Gupta, N., Singh, S., Roth, D.: Entity linking via joint encoding of types, descriptions, and context. In: EMNLP 2017, Copenhagen, Denmark, pp. 2681–2690 (2017)
Hakimov, S., ter Horst, H., Jebbara, S., Hartung, M., Cimiano, P.: Combining textual and graph-based features for named entity disambiguation using undirected probabilistic graphical models. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 288–302. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_19
Hoffart, J., et al.: Robust disambiguation of named entities in text. In: EMNLP 2011, Edinburgh, UK, pp. 782–792 (2011)
Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In: CIKM 2007, Lisbon, Portugal, pp. 233–242 (2007)
Moro, A., Cecconi, F., Navigli, R.: Multilingual word sense disambiguation and entity linking for everybody. In: ISWC 2014, Riva del Garda, Italy, pp. 25–28 (2014)
Pershina, M., He, Y., Grishman, R.: Personalized page rank for named entity disambiguation. In: NAACL HLT 2015, Denver, Colorado, pp. 238–243 (2015)
Phan, M.C., Sun, A., Tay, Y., Han, J., Li, C.: NeuPL: attention-based semantic matching and pair-linking for entity disambiguation. In: CIKM 2017, Singapore, pp. 1667–1676 (2017)
Piccinno, F., Ferragina, P.: From TagME to WAT: a new entity annotator. In: ERD 2014, Queensland, Australia, pp. 55–62 (2014)
Wick, M.L., Rohanimanesh, K., Bellare, K., Culotta, A., McCallum, A.: SampleRank: training factor graphs with atomic gradients. In: ICML 2011, Washington, pp. 777–784 (2011)
Zwicklbauer, S., Seifert, C., Granitzer, M.: Robust and collective entity disambiguation through semantic embeddings. In: SIGIR 2016, Pisa, Italy, pp. 425–434 (2016)
Acknowledgments
This work was partially supported by NSFC under grants Nos. 61872446, 71690233 and 71331008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zeng, W., Tang, J., Zhao, X., Ge, B., Xiao, W. (2018). Named Entity Disambiguation via Probabilistic Graphical Model with Embedding Features. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-04182-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04181-6
Online ISBN: 978-3-030-04182-3
eBook Packages: Computer ScienceComputer Science (R0)