FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs
This work addresses a domain-specific problem for financial technology applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackled hypernym detection in the financial services domain by developing the FinMatcher system, which used knowledge graphs to generate features for a neural classifier, achieving results for the FinSim-2 shared task.
This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conjunction with The Web Conference. The FinSim-2 shared task consists of a set of concept labels from the financial services domain. The goal is to find the most relevant top-level concept from a given set of concepts. The FinMatcher system exploits three publicly available knowledge graphs, namely WordNet, Wikidata, and WebIsALOD. The graphs are used to generate explicit features as well as latent features which are fed into a neural classifier to predict the closest hypernym.