Numerous state-of-the-art Named Entity Recognition (NER) systems use different classification schemas/ontologies. Comparisons and integration among NER systems, thus, becomes complex. In this paper, we propose a transfer-learning approach where we use supervised learning methods to automatically learn mappings between ontologies of NER systems, where an input probability distribution over a set of entity types defined in a source ontology is mapped to a target distribution over the entity types defined for a target ontology. Experiments conducted with benchmark data show valuable re-classification performance of entity mentions, suggesting our approach as a promising one for domain adaptation of NER systems.
Manchanda, P., Fersini, E., Palmonari, M., Nozza, D., Messina, V. (2017). Towards adaptation of Named Entity classification. In Proceedings of the ACM Symposium on Applied Computing (pp.155-157). Association for Computing Machinery [10.1145/3019612.3022188].
Towards adaptation of Named Entity classification
MANCHANDA, PIKAKSHI;FERSINI, ELISABETTA;PALMONARI, MATTEO LUIGI;NOZZA, DEBORA;MESSINA, VINCENZINA
2017
Abstract
Numerous state-of-the-art Named Entity Recognition (NER) systems use different classification schemas/ontologies. Comparisons and integration among NER systems, thus, becomes complex. In this paper, we propose a transfer-learning approach where we use supervised learning methods to automatically learn mappings between ontologies of NER systems, where an input probability distribution over a set of entity types defined in a source ontology is mapped to a target distribution over the entity types defined for a target ontology. Experiments conducted with benchmark data show valuable re-classification performance of entity mentions, suggesting our approach as a promising one for domain adaptation of NER systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.