Abstract
Many evaluation campaigns have shown that knowledge-based and data-driven approaches remain equally competitive for Named Entity Recognition. Our research team has developed CasEN, a symbolic system based on finite state transducers, which achieved promising results during the Ester2 French-speaking evaluation campaign. Despite these encouraging results, manually extending the coverage of such a hand-crafted system is a difficult task. In this paper, we present a novel approach based on pattern mining for NER and to supplement our system’s knowledge base. The system, mXS, exhaustively searches for hierarchical sequential patterns, that aim at detecting Named Entity boundaries. We assess their efficiency by using such patterns in a standalone mode and in combination with our existing system.
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Notes
- 1.
Each set of markers is mapped to a predetermined number of corresponding markers sequences, e.g. \(P(\{m_1, m_2\}) = P(<m_1, m_2>) = P(<m_2, m_1>) = P(<m_1, m_2, m_1>)\).
- 2.
It limits the search space by considering at any position N most probable solutions.
- 3.
With regularization parameter C = 4.
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Nouvel, D., Antoine, JY., Friburger, N. (2014). Pattern Mining for Named Entity Recognition. In: Vetulani, Z., Mariani, J. (eds) Human Language Technology Challenges for Computer Science and Linguistics. LTC 2011. Lecture Notes in Computer Science(), vol 8387. Springer, Cham. https://doi.org/10.1007/978-3-319-08958-4_19
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