Boyd-Meredith and Connor MS Project Presentations Monday
The SymSys Forum presents M.S. Project Presentations by Jonathan Tyler Boyd-Meredith and Miriam Connor. Join them on Monday, 5/13 in the Greenberg Room from 12:15-1:05 to hear the following talks:
Detecting Long-Term, Autobiographical Memories Using fMRI (Jonathan Tyler Boyd-Meredith, advised by Anthony Wagner, Psychology)
Machine learning techniques are being applied increasingly to the field of neuroscience to interpret and make use of the data sets generated by fMRI experiments. This has led to striking results for both basic and applied research in many subfields of neuroscience, including learning and memory. In particular, there has been preliminary success in classifying previously encountered and novel stimuli as either remembered by a subject, or perceived as novel by a subject. However, these experiments have frequently been limited to memory for stimuli encountered in the lab shortly before the memory test. This study uses images collected at three time intervals (6 months, 3 months, and 2 weeks) using a wearable camera that regularly takes still photographs to investigate the performance of similar classifiers on memories for events that happen outside the lab, at multiple time intervals before the memory test.
Unsupervised Disambiguation of Preposition Senses with an LDA Topic Model (Miriam Connor, advised by Beth Levin, Linguistics)
Though it has received relatively little attention in the sense disambiguation literature, preposition sense disambiguation (PSD) represents a challenging task with important applications in machine translation and relation extraction. Most work on PSD has involved on supervised systems, but only a small amount of reliable annotated data is available for preposition sense. I present an unsupervised model for PSD, which performs sense discrimination using a Latent Dirichlet Allocation model and discovers semantic relations among the prepositions with group average agglomerative clustering. I compare my system’s performance with previous work on both supervised and unsupervised PSD models and suggest future directions and applications for PSD.