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Martha Stone Palmer
Martha Stone Palmer Education University of Edinburgh: 1985 - PhD, Artificial Intelligence Driving Semantics for Limited Domains, Alan Bundy, Adviser University of Texas: 1976 - MA, Computer Science, 1972 - BA, Philosophy, Honors Positions Held University of Colorado, Boulder, CO, 80309 2005-now Associate Professor: Institute of Cognitive Science Faculty, Departments of Linguistics & Computer Science Co-Director, CLEAR, Center for Computational Language and Education Research, 2008 - now University of Pennsylvania, Philadelphia, PA 19104 1993-2005 Associate Professor: Department of Computer and Information Science 1999 – 2005 Graduate Group Member, Linguistics Department (tenure awarded, June, 2004) Visiting Associate Professor: Department of Computer and Information Science (98/99) Director of Technology: Institute for Research in Cognitive Science (96/98) Adjunct Associate Prof: Department of Computer and Information Science (93/98) University of Delaware, Newark, DE 1993 – 1994 Associate Research Prof: Department of Computer and Information Science National University of Singapore, Singapore 1990 - 1993 Visiting Senior Fellow: Department of Information Systems and Computer Science Adjunct Senior Lecturer: Institute of System Science (1993) UNISYS Corporation, Philadelphia, PA 1983 - 1990 Sr. Research Scientist: Logic-Based Systems Group (Artificial Intelligence) Staff Scientist (83/84), Manager (84/85) University of Pennsylvania, Philadelphia, PA 1980 - 1987 Sloan Research Fellow: Department of Computer and Information Science (80/81) -
Martha Stone Palmer
Martha Stone Palmer Education University of Edinburgh: 1985 - PhD, Artificial Intelligence Driving Semantics for Limited Domains, Alan Bundy, Adviser University of Texas: 1976 - MA, Computer Science, 1972 - BA, Philosophy, Honors Positions Held University of Colorado, Boulder, CO, 80309 Fall 2005-now Professor, Linguistics, Computer Science, Institute of Cognitive Science – Fall 2009 - now Associate Professor: Institute of Cognitive Science Faculty, – 2005-Spring 2009 Departments of Linguistics & Computer Science Co-Director, CLEAR, Center for Computational Language and Education Research, 2008 - now University of Pennsylvania, Philadelphia, PA 19104 1993-2005 Associate Professor: Department of Computer and Information Science 1999 – 2005 Graduate Group Member, Linguistics Department (tenure awarded, June, 2004) Visiting Associate Professor: Department of Computer and Information Science (98/99) Director of Technology: Institute for Research in Cognitive Science (96/98) Adjunct Associate Prof: Department of Computer and Information Science (93/98) University of Delaware, Newark, DE 1993 – 1994 Associate Research Prof: Department of Computer and Information Science National University of Singapore, Singapore 1990 - 1993 Visiting Senior Fellow: Department of Information Systems and Computer Science Adjunct Senior Lecturer: Institute of System Science (1993) UNISYS Corporation, Philadelphia, PA 1983 - 1990 Sr. Research Scientist: Logic-Based Systems Group (Artificial Intelligence) Staff Scientist (83/84), Manager (84/85) University of Pennsylvania, -
Predicate Representations and Polysemy in Verbnet Semantic Parsing
Predicate Representations and Polysemy in VerbNet Semantic Parsing James Gung∗ Martha Palmer University of Colorado Boulder University of Colorado Boulder [email protected] [email protected] Abstract Billy consoled the puppy PB Arg0 console.01 Arg1 Despite recent advances in semantic role la- beling propelled by pre-trained text encoders VN Stimulus amuse-31.1 Experiencer like BERT, performance lags behind when ap- plied to predicates observed infrequently dur- Billy walked the puppy ing training or to sentences in new domains. PB Arg0 walk.01 Arg1 In this work, we investigate how semantic role labeling performance on low-frequency VN Agent run-51.3.2-2-1 Theme predicates and out-of-domain data can be im- Table 1: Comparison of PropBank (PB) and VerbNet proved by using VerbNet, a verb lexicon that (VN) roles for predicates console and walk. VerbNet’s groups verbs into hierarchical classes based on thematic role assignments (e.g. Stimulus vs. Agent shared syntactic and semantic behavior and de- and Experiencer vs. Theme) are more dependent on fines semantic representations describing rela- the predicate than PropBank’s numbered arguments. tions between arguments. We find that Verb- Net classes provide an effective level of ab- straction, improving generalization on low- frequency predicates by allowing them to learn large gains in performance through the use of pre- from the training examples of other predicates trained text encoders like ELMo and BERT (Peters belonging to the same class. We also find et al., 2018; Devlin et al., 2019). Despite these ad- that joint training of VerbNet role labeling and vances, performance on low-frequency predicates predicate disambiguation of VerbNet classes and out-of-domain data remains low relative to for polysemous verbs leads to improvements in-domain performance on higher frequency predi- in both tasks, naturally supporting the extrac- cates.