The LODIE team (University of Sheffield) participation at the TAC2015 Entity Discovery Task of the Cold Start KBP Track
Year: 2015
Type: book-chapter
Abstract: This paper describes the LODIE team (from the OAK lab of the University of Sheffield) participation at TAC-KBP 2015 for the Entity Discovery task in the Cold Start KBP track.We have taken a cross-document coreference resolution approach that starts with Named Entity Recognition to locate and classify mentions of named entities, followed by a clustering procedure that groups mentions referring to the same entity.Our primary interest was studying different features and their effect on the clustering process, as well as scalable methods to cope with very large data.We experimented with several feature combinations and conclude that the best results are obtained using features based on entity surface forms and distributed word embeddings.To cope with large scale data, the clustering process takes a two-step approach to break data to smaller batches.Our method on the 2015 evaluation dataset obtains a best CEAF mention F-measure of 63.2 1 .
Authors: Ziqi Zhang, Jie Gao, Anna Lisa Gentile
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2598057095