Named Entity recognition without gazetteers
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In this paper we describe SIEMÊS, a named-entity recognition system for Portuguese that relies on a set of similarity rules to base the classification procedure. These rules try to obtain soft matches between candidate entities found in text and instances contained in a wide-scope gazetteer, and avoid the need for coding large sets of rules by exploiting lexical similarities. Using this matching procedure, SIEMÊS generates a set of classification hypotheses based solely on internal evidence, which may be disambiguated in a later step by relatively simple rules based on contextual clues. We explain SIEMÊS architecture and its named-entity identification and classification procedure. We also briefly discuss the results of the participation of SIEMÊS in HAREM, the named-entity evaluation contest for Portuguese, and describe future work.