Daniel Edmiston


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Unsupervised Discovery of Firm-Level Variables in Earnings Call Transcript Embeddings
Daniel Edmiston | Ziho Park
Proceedings of the Second Workshop on Financial Technology and Natural Language Processing


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Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments
Daniel Edmiston | Karl Stratos
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

This work introduces a novel, linguistically motivated architecture for composing morphemes to derive word embeddings. The principal novelty in the work is to treat stems as vectors and affixes as functions over vectors. In this way, our model’s architecture more closely resembles the compositionality of morphemes in natural language. Such a model stands in opposition to models which treat morphemes uniformly, making no distinction between stem and affix. We run this new architecture on a dependency parsing task in Korean—a language rich in derivational morphology—and compare it against a lexical baseline,along with other sub-word architectures. StAffNet, the name of our architecture, shows competitive performance with the state-of-the-art on this task.