Open-Set Named Entity Recognition: A Preliminary Study

In Natural Language Processing, Named Entity Recognition (NER) is a critical task that aims to identify entities of interest in a given text. NER is typically solved by discerning entity tokens from non-entity ones via multi-class classifiers. However, training such models may be challenging due to the prevalence of non-entity tokens. To address this issue, in this paper, we investigated the effectiveness of an open-set recognizer, a machine learning model that, generalizing a multi-class classifier, recognizes only entity tokens and rejects non-entity ones. This paper demonstrates that open-set recognizers are an effective approach to address the token recognition problem. Indeed, we compared a traditional token recognizer based on Conditional Random Field with a state-of-the-art instance-based open-set recognizer, and our evaluation shows that the open-set recognizer outperforms the traditional token recognizer.