Semantic Role Labeling of NomBank: A Maximum Entropy Approach
نویسندگان
چکیده
This paper describes our attempt at NomBank-based automatic Semantic Role Labeling (SRL). NomBank is a project at New York University to annotate the argument structures for common nouns in the Penn Treebank II corpus. We treat the NomBank SRL task as a classification problem and explore the possibility of adapting features previously shown useful in PropBank-based SRL systems. Various NomBank-specific features are explored. On test section 23, our best system achieves F1 score of 72.73 (69.14) when correct (automatic) syntactic parse trees are used. To our knowledge, this is the first reported automatic NomBank SRL system.
منابع مشابه
Learning Predictive Structures for Semantic Role Labeling of NomBank
This paper presents a novel application of Alternating Structure Optimization (ASO) to the task of Semantic Role Labeling (SRL) of noun predicates in NomBank. ASO is a recently proposed linear multi-task learning algorithm, which extracts the common structures of multiple tasks to improve accuracy, via the use of auxiliary problems. In this paper, we explore a number of different auxiliary prob...
متن کاملSemantic Role Labeling using Maximum Entropy Model
In this paper, we propose a semantic role labeling method using a maximum entropy model, which enables not only to exploit rich features but also to alleviate the data sparseness problem in a well-founded model. For applying the maximum entropy model to semantic role labeling, we take a incremental approach as follows: firstly, the semantic roles are assigned to the arguments in the immediate c...
متن کاملSemantic Dependency Parsing of NomBank and PropBank: An Efficient Integrated Approach via a Large-scale Feature Selection
We present an integrated dependencybased semantic role labeling system for English from both NomBank and PropBank. By introducing assistant argument labels and considering much more feature templates, two optimal feature template sets are obtained through an effective feature selection procedure and help construct a high performance single SRL system. From the evaluations on the date set of CoN...
متن کاملImproving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discourse. We introduce an approach to iSRL based on a predictive recurrent neural semantic frame model (PRNSFM) that uses a large unannotated corpus to learn the probability of a seq...
متن کاملSemantic Role Labeling of Implicit Arguments for Nominal Predicates
Nominal predicates often carry implicit arguments. Recent work on semantic role labeling has focused on identifying arguments within the local context of a predicate; implicit arguments, however, have not been systematically examined. To address this limitation, we have manually annotated a corpus of implicit arguments for ten predicates from NomBank. Through analysis of this corpus, we find th...
متن کامل