[hal-01076157] Pattern Mining for Named Entity Recognition

Many evaluation campaigns have shown that knowledge-based and data-driven approaches remain equally competitive for Named Entity Recognition Our re-search team has developed CasEN a symbolic system based on finite state tran-ducers which achieved promising results during the Ester2 French-speaking eval-uation campaign Despite these encouraging results manually extending the cov-erage 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 sys-tem'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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