Methods for Recognizing Nested Terms
This work addresses the challenge of nested term extraction for natural language processing tasks, particularly in Russian, but appears incremental as it adapts an existing method to a new domain.
The authors tackled the problem of extracting nested terms by applying the Binder model, previously used for nested named entities, to the RuTermEval competition, achieving the best results in all three tracks. They also studied recognizing nested terms from flat training data without nested annotations, concluding that their approaches can effectively retrieve nested terms without nested labeling.
In this paper, we describe our participation in the RuTermEval competition devoted to extracting nested terms. We apply the Binder model, which was previously successfully applied to the recognition of nested named entities, to extract nested terms. We obtained the best results of term recognition in all three tracks of the RuTermEval competition. In addition, we study the new task of recognition of nested terms from flat training data annotated with terms without nestedness. We can conclude that several approaches we proposed in this work are viable enough to retrieve nested terms effectively without nested labeling of them.