University of Colorado
Abstract Meaning Representation Parsing with Rich Linguistic Features.
Degree: PhD, 2017, University of Colorado
Lexical and syntactic information have been shown to play important roles in semantic parsing. However, there is still no solid research on the relationship between semantic parsing and different types of linguistic knowledge that support this, e.g., lexical cues, dependency structures, semantic roles, etc. It is also known that dependency structures provide rich syntactic information for various NLP applications. Yet, few applications use dependency structures in an underlying neural network framework. This dissertation introduces a complete framework designed to parse Abstract Meaning Representations (AMRs), a semantic representation that expresses the meaning of a sentence as a directed acyclic graph. To enhance our AMR parser, we first develop a light verb construction (LVC) detector using a SVM. We also link input dependency parses to AMR concepts taking an EM-based approach to generate alignment pairs.
The main parser is split into three sub-components: a frame identifier, a concept identifier, and a transition action identifier. To support these components, we develop a Recursive Neural Network (RevNN) based model as the underlying framework of all three components. RevNN is based on dependency structures combined with distinct linguistic features. RevNN generates a corresponding vector representation for each dependency node, passing these vectors to the three identifiers as the underlying framework. By integrating all the above components, we design a transition-based parser which generates AMR graphs from input dependency parses.
Results show that our LVC detector surpasses comparable systems by 3 to 4% in F1
score, and that this LVC detector supports the AMR parser. Our aligner improves F1
score by 2 to 5% with LVCs information. Moreover, the resulting AMR parser achieves the best Smatch scores among other transition-based AMR parsers. We also show that the RevNN framework helps to integrate different linguistic features for improvement in accuracy of individual components.
Advisors/Committee Members: Martha Palmer, James H. Martin, Wayne Ward, Mans Hulden, Jinho D. Choi.
Subjects/Keywords: natural language processing; natural language understanding; neural network; semantic parsing; Artificial Intelligence and Robotics
to Zotero / EndNote / Reference
APA (6th Edition):
Chen, W. (2017). Abstract Meaning Representation Parsing with Rich Linguistic Features. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/155
Chicago Manual of Style (16th Edition):
Chen, Wei-Te. “Abstract Meaning Representation Parsing with Rich Linguistic Features.” 2017. Doctoral Dissertation, University of Colorado. Accessed September 23, 2019.
MLA Handbook (7th Edition):
Chen, Wei-Te. “Abstract Meaning Representation Parsing with Rich Linguistic Features.” 2017. Web. 23 Sep 2019.
Chen W. Abstract Meaning Representation Parsing with Rich Linguistic Features. [Internet] [Doctoral dissertation]. University of Colorado; 2017. [cited 2019 Sep 23].
Available from: https://scholar.colorado.edu/csci_gradetds/155.
Council of Science Editors:
Chen W. Abstract Meaning Representation Parsing with Rich Linguistic Features. [Doctoral Dissertation]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/csci_gradetds/155