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1	Relation Classification using Semantically-Enhanced Syntactic Dependency Paths : Combining Semantic and Syntactic Dependencies for Relation Classification using Long Short-Term Memory Networks Capshaw, Riley January 2018 (has links) Many approaches to solving tasks in the field of Natural Language Processing (NLP) use syntactic dependency trees (SDTs) as a feature to represent the latent nonlinear structure within sentences. Recently, work in parsing sentences to graph-based structures which encode semantic relationships between words—called semantic dependency graphs (SDGs)—has gained interest. This thesis seeks to explore the use of SDGs in place of and alongside SDTs within a relation classification system based on long short-term memory (LSTM) neural networks. Two methods for handling the information in these graphs are presented and compared between two SDG formalisms. Three new relation extraction system architectures have been created based on these methods and are compared to a recent state-of-the-art LSTM-based system, showing comparable results when semantic dependencies are used to enhance syntactic dependencies, but with significantly fewer training parameters. Natural language processing NLP computational linguistics syntactic dependency trees semantic dependency graphs relation classification relation extraction artificial intelligence machine learning deep learning neural networks long short-term memory LSTM