Study on Multi-granularity Embeddings of Training and Evaluation in the Bond Field
The bond market is flooded with massive and complex information,while the key to fully utilizing this information and implementing the aim that fintech enables businesses is to construct a digital dictionary(namely,pre-trained word embeddings),which can describe complex semantics in the bond market.So far,there has been a lack of pre-trained bond-specific embeddings,and their evaluation has also been a big challenge.On the basis of joint infor-mation of components,characters and words,this study proposed a multi-granularity word embeddings training frame-work for the bond field,named BondJWE.Moreover,to evaluate these embeddings scientifically,this study designed a downstream task,text classification,according to intrinsic features of data.This study makes up for the blank of re-search on pre-trained bond-specific embeddings.And results show that the performance of BondJWE is better than that of other baseline models,which indicates that these multi-granularity word embeddings can better express seman-tics and are more robust.
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