Abstract
Sci-tech compound phrase entity (e.g., the names of projects, books and patents) recognition is a fundamental task of science and technology data processing and discovery. However, much less work have been done on the problem. In this paper, we first give the characteristics of sci-tech entities that are different from personal name and other traditional entities. Then we introduce a self-learning rule-based approach to address the problem. The approach consists of three stages, namely rule-based text truncation, BlackPOS-based text split and WhiteKey-based confirmation. Constructing the best WhiteKey list is a NP-hard problem. We further design dynamic programming and greedy algorithms to address the problem. Experimental results show that our approach achieves 94.78% precision rate, 89.19% recall rate and 91.9% F 1 measure in average. Moreover, our approach is universal and orthogonal to prior named entity recognition work.
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References
Bikel, D.M., Miller, S., Schwartz, R., Weischedel, R.: Nymble: A High-performance Learning Name-finder. In: Proc. of ANLC, pp. 194–201 (1997)
Borthwick, A., Sterling, J., Agichtein, E., Grishman, R.: NYU: Description of the MENE Named Entity System as Used in MUC-7. In: Proc. of MUC (1998)
Chiticariu, L., Krishnamurthy, R., Li, Y., Reiss, F., Vaithyanathan, S.: Domain Adaptation of Rule-based Annotators for Named-Entity Recognition Tasks. In: Proc. of EMNLP, pp. 1002–1012 (2010)
Cucerzan, S.: Large-Scale Named Entity Disambiguation Based on Wikipedia Data. In: Proc. of EMNLP-CoNLL 2007, pp. 708–716 (2007)
Cucerzan, S., Yarowsky, D.: Language independent named entity recognition combining morphological and contextual evidence. In: Proc. of EMNLP and VLC, pp. 90–99 (1999)
Farmakiotou, D., Karkaletsis, V., Koutsias, J., Sigletos, G., Spyropoulos, C.D., Stamatopoulos, P.: Rule-Based Named Entity Recognition For Greek Financial Texts. In: Proc. of COMLEX, pp. 75–78 (2000)
Zhang, H.: NLPIR/ICTCLAS (2012), http://ictclas.nlpir.org/
Krishnan, V., Manning, C.D.: An Effective Two-stage Model for Exploiting Non-local Dependencies in Named Entity Recognition. In: Proc. of ACL, pp. 1121–1128 (2006)
Mann, G.S., Yarowsky, D.: Unsupervised Personal Name Disambiguation. In: Proc. of CONLL at HLT-NAACL 2003, vol. 4, pp. 33–40 (2003)
McCallum, A., Li, W.: Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-enhanced Lexicons. In: Proc. of CONLL at HLT-NAACL 2003, vol. 4, pp. 188–191 (2003)
Mikheev, A., Moens, M., Grover, C.: Named Entity Recognition without Gazetteers. In: Proc. of EACL, pp. 1–8 (1999)
Nadeau, D., Sekine, S.: A Survey of Named Entity Recognition and Classification (2007), http://brown.cl.uni-heidelberg.de/~sourjiko/NER_Literatur/survey.pdf
Sekine, S.: NYU: Description of the Japanese NE system used for MET-2. In: Proc. of MUC (1998)
Sogou Labs: Sogou Text Classification Corpus (2008), http://www.sogou.com/labs/dl/c.html/
Takeuchi, K., Collier, N.: Use of Support Vector Machines in Extended Named Entity Recognition. In: Proc. of COLING, vol. 20, pp. 1–7 (2002)
Viggo Kann: Minimum Set Cover (2000), http://perso.ensta-paristech.fr/~diam/ro/online/viggo_wwwcompendium/node146.html#6062
Wikipedia: Intellectual Property Protection, en.wikipedia.org/wiki/Intellectual_property
Wikipedia: Open-Source Intelligence, en.wikipedia.org/wiki/Open-source_intelligence
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Liu, T., Zhang, Y., Yan, Y., Shi, J., Guo, L. (2015). A Self-learning Rule-Based Approach for Sci-tech Compound Phrase Entity Recognition. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_60
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DOI: https://doi.org/10.1007/978-3-319-25255-1_60
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