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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01v692t8641
Title: Tensor Decomposition and Memory Networks for SAT Reading Comprehension
Authors: Madge, Saahil
Advisors: Fellbaum, Christiane
Department: Computer Science
Class Year: 2016
Abstract: Abstract We present a general approach for machine comprehension tasks by converting the text to a knowledge graph and the questions to queries on the graph. We extend [19] and use the Stanford NLP Toolkit’s Dependency Parser [17, 9] to transform each sentence into a set of entity-relation triples. We use word2vec [18] to convert the questions into queries on the graph. We present a tensor decomposition approach to answering queries by adding Semantically Smooth Embedding [11] to RESCAL [20]. We also generalize the Memory Networks [28, 25] architecture to take any knowledge graph as input. We evaluate these models on three full SAT reading comprehension tests. The models presented here outperform their respective baselines. Both models demonstrate the ability to capture the semantic and structural information in the text and answer questions using that information.
Extent: 57
URI: http://arks.princeton.edu/ark:/88435/dsp01v692t8641
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Computer Science, 1988-2016

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