Title: GLARF and the 2nd Stage of Parsing: Combining Parsing, SRL, NE tagging, Temporal Tagging, ... Speaker: Adam Meyers (NYU) Time: 2:15pm-3:30pm, Friday, March 25 Place: Room 4102, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Abstract: Over the last decade, we have built a system for producing second stage parses that combine the output of parsers, NE taggers, semantic role labelers and other transducers to produce a single theoretically-consistent representation. The resulting output includes predicate argument dependencies that connect all the words in the sentence in a single graph. This approach contrasts with that of Semantic Role Labeling programs which link verb predicates to their arguments, but (typically) ignore relations anchored by other parts of speech (nouns, adjectives, etc.). Other systems that combine different types of annotation (Ontonotes, MASC, etc.) take an approach that links annotation at the token and/or character level, without changing any of the input. In contrast, GLARF imposes the consistency of a GLARF-based theory on the data, making tokenization and constituent boundary decisions when the data sources conflict -- errors in the data are corrected in the process. English GLARF achieves F-scores ranging from 77% for spoken language telephone transcripts to 88% for correspondence and news text, only slightly lower than parsing scores for the same types of data. This talk will discuss how GLARF can be used by the NLP community; the theoretical context in which GLARF was created; GLARF systems that process Chinese and Japanese; as well as new features we are adding to GLARF to support temporal and causational analysis. The first public release of the English version of GLARF software occurred on January 13, 2011. The GLARF website: http://nlp.cs.nyu.edu/meyers/GLARF.html includes more information on GLARF as well as instructions for downloading and using English GLARF. Speaker's Bio: Adam Meyers is a research assistant professor at New York University, specializing in several sub-fields of computational linguistics including: the manual and automatic creation of linguistic resources (lexicons, corpus annotation); machine translation; noun phrase processing and knowledge-based methods.