Identifying lexical relationships and entailments with distributional semantics

Many modern efforts in Natural Language Understanding depend on rich and powerful semantic representations of words. Systems for sophisticated logical and textual reasoning often depend heavily on lexical resources to provide critical information about relationships between words, but these lexical resources are expensive to create and maintain, and are never fully comprehensive. Distributional Semantics has long offered methods for automatically inducing meaning representations from large corpora, with little or no annotation efforts. The resulting representations are valuable proxies of semantic similarity, but simply knowing two words are similar cannot tell us their relationship, or whether one entails the other. In this thesis, we consider how methods from Distributional Semantics may be applied to the difficult task of lexical entailment, where one must predict whether one word implies another. We approach this by showing contributions in areas of hypernymy detection, lexical relationship prediction, lexical substitution, and textual entailment. We propose novel experimental setups, models, analysis, and interpretations, which ultimate provide us with a better understanding of both the nature of lexical entailment, as well as the information available within distributional representations.Computer Science

Financial Viability of Implementing Home-Based Primary Care Programs in Federally-Qualified Health Centers in the United States: An Empirical Assessment

Home-based primary care (HBPC) has been shown to be an effective method of delivering primary care services to high-risk, high-utilizing patients. Federally-Qualified health centers (FQHCs) have historically provided, and continue to provide, comprehensive primary care for a majority of the medically underserved patients across the United States. Often times, the patients that access primary care from FQHCs may experience socioeconomic, behavioral, or physical factors that would make an elderly or home-bound patient eligible for a HBPC program. With increased focus on population health management and a much anticipated transition from fee-for-service to value-based payments, the implementation of this delivery model would seem to be an innovative method of removing barriers for medically underserved populations. The purpose of this research is to analyze the financial viability of implementing this model of primary care delivery in FQHC settings to help improve access to care and improve outcomes in a cost-effective manner

Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing is-a relationships and to correct wrong extractions. Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks