SIEMêS - A Named-Entity Recognizer for Portuguese Relying on Similarity Rules

摘要

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.