Abstract
Entity Linking (EL) is the task of mapping mentions in natural-language text to their corresponding entities in a knowledge base (KB). Type modeling for mention and entity could be beneficial for entity linking. In this paper, we propose a type-guided semantic embedding approach to boost collective entity linking. We use Bidirectional Long Short-Term Memory (BiLSTM) and dynamic convolutional neural network (DCNN) to model the mention and the entity respectively. Then, we build a graph with the semantic relatedness of mentions and entities for the collective entity linking. Finally, we evaluate our approach by comparing the state-of-the-art entity linking approaches over a wide range of very different data sets, such as TAC-KBP from 2009 to 2013, AIDA, DBPediaSpotlight, N3-Reuters-128, and N3-RSS-500. Besides, we also evaluate our approach with a Chinese Corpora. The experiments reveal that the modeling for entity type can be very beneficial to the entity linking.
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References
Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., Trani, S.: Dexter: an open source framework for entity linking. In: Proceedings of the Sixth International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR 2013, pp. 17–20. ACM, New York (2013). http://doi.acm.org/10.1145/2513204.2513212
Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., Trani, S.: Learning relatedness measures for entity linking. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 139–148. ACM (2013)
Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: EMNLP-CoNLL (2007)
Dojchinovski, M., Kliegr, T.: Entityclassifier.eu: real-time classification of entities in text with Wikipedia. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 654–658. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40994-3_48
Globerson, A., Lazic, N., Chakrabarti, S., Subramanya, A., Ringaard, M., Pereira, F.: Collective entity resolution with multi-focal attention. In: ACL (2016)
Han, X., Sun, L.: A generative entity-mention model for linking entities with knowledge base. In: ACL (2011)
Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 765–774. ACM, New York (2011). http://doi.acm.org/10.1145/2009916.2010019
Han, X., Zhao, J.: NLPR_KBP in TAC 2009 KBP track: a two-stage method to entity linking. In: TAC (2009)
He, Z., Liu, S., Li, M., Zhou, M., Zhang, L., Wang, H.: Learning entity representation for entity disambiguation. In: ACL (2013)
He, Z., Liu, S., Song, Y., Li, M., Zhou, M., Wang, H.: Efficient collective entity linking with stacking. In: EMNLP, pp. 426–435 (2013)
Hoffart, J., Yosef, M.A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., Weikum, G.: Robust disambiguation of named entities in text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 782–792. Association for Computational Linguistics (2011)
Huang, H., Cao, Y., Huang, X., Ji, H., Lin, C.Y.: Collective tweet wikification based on semi-supervised graph regularization. In: ACL (2014)
Huang, H., Heck, L., Ji, H.: Leveraging deep neural networks and knowledge graphs for entity disambiguation. CoRR abs/1504.07678 (2015)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)
Lazic, N., Subramanya, A., Ringgaard, M., Pereira, F.: Plato: a selective context model for entity resolution. TACL 3, 503–515 (2015)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, vol. 14, pp. 1188–1196 (2014)
Mendes, P.N., Jakob, M., Garcła-silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems (I-Semantics) (2011)
Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. Trans. Assoc. Comput. Linguist. 2, 231–244 (2014)
Piccinno, F., Ferragina, P.: From TagME to WAT: a new entity annotator. In: Proceedings of the First International Workshop on Entity Recognition & Disambiguation, pp. 55–62. ACM (2014)
Shen, W., Wang, J., Han, J.: Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27, 443–460 (2015)
Shen, W., Wang, J., Luo, P., Wang, M.: LINDEN: linking named entities with knowledge base via semantic knowledge. In: WWW (2012)
Sil, A., Florian, R.: One for all: towards language independent named entity linking. In: ACL (2016)
Sun, Y., Lin, L., Tang, D., Yang, N., Ji, Z., Wang, X.: Modeling mention, context and entity with neural networks for entity disambiguation. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1333–1339 (2015)
Tong, H., Faloutsos, C., Pan, J.Y.: Random walk with restart: fast solutions and applications. Knowl. Inf. Syst. 14, 327–346 (2008)
Van Erp, M., Rizzo, G., Troncy, R.: Learning with the web: Spotting named entities on the intersection of NERD and machine learning. In: # MSM, pp. 27–30 (2013)
Waitelonis, J., Sack, H.: Named Entity Linking in# Tweets with KEA (2016)
Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. In: CoNLL (2016)
Zhang, L., Rettinger, A.: X-LiSA: cross-lingual semantic annotation. Proc. VLDB Endow. 7(13), 1693–1696 (2014). https://doi.org/10.14778/2733004.2733063
Zheng, Z., Li, F., Huang, M., Zhu, X.: Learning to link entities with knowledge base. In: HLT-NAACL (2010)
Zwicklbauer, S., Seifert, C., Granitzer, M.: Robust and collective entity disambiguation through semantic embeddings. In: SIGIR (2016)
Acknowledgements
This work is supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY17F020015), the Chinese Knowledge Center of Engineering Science and Technology (CKCEST), and the Fundamental Research Funds for the Central Universities (No. 2017FZA5016).
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Lu, W., Zhou, Y., Lu, H., Ma, P., Zhang, Z., Wei, B. (2018). Boosting Collective Entity Linking via Type-Guided Semantic Embedding. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_45
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