SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems
This addresses challenges in preprocessing for user intent understanding in open domain dialogue systems, such as coarse-grained labels and lack of useful ontologies, but appears incremental as it builds on existing NER/NEL work for a specific application.
The paper tackles the problem of named entity recognition and linking in open domain dialogue systems, where traditional methods fail, by introducing SlugNERDS, a tool optimized for this context, along with new resources like SlugEntityDB and SchemaActuator.
In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we cannot rely on domain-specific inference. UCSC's effort as one of the funded teams in the 2017 Amazon Alexa Prize Contest has yielded Slugbot, an open domain social bot, aimed at casual conversation. We discovered several challenges specifically associated with both NER and NEL when building Slugbot, such as that the NE labels are too coarse-grained or the entity types are not linked to a useful ontology. Moreover, we have discovered that traditional approaches do not perform well in our context: even systems designed to operate on tweets or other social media data do not work well in dialogue systems. In this paper, we introduce Slugbot's Named Entity Recognition for dialogue Systems (SlugNERDS), a NER and NEL tool which is optimized to address these issues. We describe two new resources that we are building as part of this work: SlugEntityDB and SchemaActuator. We believe these resources will be useful for the research community.