Lightweight and accurate system for entity extraction and linking

Entity extraction and linking components in dialogue assistants should meet the requirements of low resource consumption and high accuracy. In this paper we present lightweight system which extracts entity mentions from the text and finds corresponding Wikidata ids and Wikipedia pages links. Entity extraction and linking is performed into the following steps: extraction of entity substrings from the text, retrieval of candidate entities from Wikidata knowledge base and entity disambiguation. Entity extraction is based on RoBERTa-tiny model for token classification. Extracted substrings are classified into 42 fine-grained tags for filtering of candidate entities. Candidate entities are ranked by number of connections of candidate entities in the text in Wikidata knowledge graph. The proposed system outperforms on WNED-WIKI other lightweight solutions, such as REL and OpenTapioca. The system supports easy adding new Wikidata entities to the database and using other knowledge bases for entity linking.