The paper describes an annotation scheme for English based on Panini’s concept of karakas. We describe how the scheme handles certain constructions in English. By extending the karaka scheme for a fixed word order language, we hope to bring out its advantages as a concept that incorporates some ‘local semantics’. Our comparison with PTB-II and PropBank brings out its intermediary status between...

In this work, we propose a new method to integrate two recent lines of work: unsupervised induction of shallow semantics (e.g., semantic roles) and factorization of relations in text and knowledge bases. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction com...

Coreference resolution plays a critical role in discourse analysis. This paper focuses on exploiting zero pronouns to improve Chinese coreference resolution. In particular, a simplified semantic role labeling framework is proposed to identify clauses and to detect zero pronouns effectively, and two effective methods (refining syntactic parser and refining learning example generation) are employ...

We propose a method for labelling prepositional phrases according to two different semantic role classifications, as contained in the Penn treebank and the CoNLL 2004 Semantic Role Labelling data set. Our results illustrate the difficulties in determining preposition semantics, but also demonstrate the potential for PP semantic role labelling to improve the performance of a holistic semantic ro...

Extending a machine learning based coreference resolution system with a feature capturing automatically generated information about semantic roles improves its performance.

We introduce a new approach to unsupervised estimation of feature-rich semantic role labeling models. Our model consists of two components: (1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction component: a tensor factorization model which relies on roles to predict argument fillers. When the compo...

A linking theory explains how verbs’ semantic arguments are mapped to their syntactic arguments—the inverse of the semantic role labeling task from the shallow semantic parsing literature. In this paper, we develop the computational linking theory framework as a method for implementing and testing linking theories proposed in the theoretical literature. We deploy this framework to assess two cr...

This paper presents an application of Chinese syntactic knowledge for semantic role labeling (SRL). Besides basic morphological information, syntactic structures are crucial in SRL. However, it is difficult to learn such information from limited, small-scale, manually annotated training data. Instead of manually increasing the size of annotated data, we use a large amount of automatically extra...

This paper explores Chinese semantic role labeling (SRL) for nominal predicates. Besides those widely used features in verbal SRL, various nominal SRL-specific features are first included. Then, we improve the performance of nominal SRL by integrating useful features derived from a state-of-the-art verbal SRL system. Finally, we address the issue of automatic predicate recognition, which is ess...

In this paper we present a semantic role labeling system submitted to the task Multilevel Semantic Annotation of Catalan and Spanish in the context of SemEval–2007. The core of the system is a memory–based classifier that makes use of full syntactic information. Building on standard features, we train two classifiers to predict separately the semantic class of the verb and the semantic roles.