Phong Minh Le

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In this paper, we address the problem of how to use semantics to improve syntactic parsing, by using a hybrid reranking method: a k-best list generated by a symbolic parser is reranked based on parse-correctness scores given by a composi-tional, connectionist classifier. This classi-fier uses a recursive neural network to construct vector representations(More)
BACKGROUND Dendritic cell (DC) therapy is a promising therapy for cancer-targeting treatments. Recently, DCs have been used for treatment of some cancers. We aimed to develop an in vitro assay to evaluate DC therapy in cancer treatment using a breast cancer model. METHODS DCs were induced from murine bone marrow mononuclear cells in Roswell Park Memorial(More)
BACKGROUND Breast cancer (BC) is one of the leading cancers in women. Recent progress has enabled BC to be cured with high efficiency. However, late detection or metastatic disease often renders the disease untreatable. Additionally, relapse is the main cause of death in BC patients. Breast cancer stem cells (BCSCs) are considered to cause the development(More)
In this thesis a compositional distributional semantic approach, the Recursive Neural Network, is used to syntactically-semantically compose non-symbolic representations of words. Unlike previous Recursive Neural Network models which use either no linguistic enrichment or significant symbolic syntactic enrichment, I propose minimal linguistic enrichments(More)
  • Shauna Marvin, Marvin, Shauna, Shauna A Marvin Chicago, Il, Ed Campbell +8 others
  • 2015
This Dissertation is brought to you for free and open access by the Theses and Dissertations at Loyola eCommons. It has been accepted for inclusion in Dissertations by an authorized administrator of Loyola eCommons. For more information, please contact ecommons@luc.edu. iii ACKNOWLEDGMENTS I would like to thank my wonderful mentor, Dr. Chris Wiethoff, for(More)
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