Time: Friday November 27th, 2009 1:30 p.m.
Place: TASC1 9204 West
Title: SEMANTIC ROLE LABELING USING LEXICALIZED TREE ADJOINING GRAMMARS
For a natural language sentence, its meaning is typically conveyed through the event/action and its involved participants; the event is expressed as a verb (predicate), and the participants involved are expressed as the arguments of the verb. The task of semantic role labeling (SRL) is to identify the predicate-argument structures (PAS) and label the relations between the predicate and each of its arguments. It is an important intermediate step towards many natural language processing (NLP) applications, such as text summarization, question answering, and machine translation. Lexicalized Tree Adjoining Grammars (LTAGs), a tree rewriting formalism, has been made desirable for the SRL task by its property of Extended Domain of Locality (EDL).
Our work in this thesis is mainly focused on the development and learning of the state of the art discriminative SRL systems with LTAGs. Our contributions include: (1)We proposed the use of LTAG formalism as an important additional source of features for the semantic role labeling task. Our experiments show that compared with the best known set of features that are used in state of the art SRL systems, LTAG-based features can improve SRL performance significantly. (2)We explored a novel LTAG formalism – LTAG-spinal and its treebank for SRL task and demonstrated the utility of this new resource for SRL. Deep linguistic information such as predicate-argument relationships that are either implicit or absent from the original Penn Treebank are made explicit and accessible in the LTAG-spinal Treebank, which we show to be a useful resource for semantic role labeling. (3)We applied a novel learning framework - Latent Support Vector Machines (LSVMs) to SRL task by treating LTAG derivation trees as latent structures of Penn Treebank derived trees and further improvement has been gained in the SRL accuracy. Our work widens the possibility for the general applicability of LSVMs to other NLP tasks. In addition, the empirical success of our work is theoretically intriguing in terms of the advantage that the online learning presented under our LSVM framework over the batch learning.
Keywords: semantic role labeling (SRL), Lexicalized Tree Adjoining Grammars (LTAG), features, Latent Support Vector Machines (LSVM)