2005 SemanticRoleLabellingwithTreeCo

(Cohn et al., 2005) ⇒ Trevor Cohn, and Philip Blunsom. (2005). “Semantic Role Labelling with Tree Conditional Random Fields.” In: Proceedings of the Ninth Conference on Computational Natural Language Learning.

Subject Headings: Semantic Role Labelling Algorithm, Tree-Structured CRF Model.

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Abstract

In this paper we apply conditional random fields (CRFs) to the semantic role labelling task. We define a random field over the structure of each sentence's syntactic parse tree. For each node of the tree, the model must predict a semantic role label, which is interpreted as the labelling for the corresponding syntactic constituent. We show how modelling the task as a tree labelling problem allows for the use of efficient CRF inference algorithms, while also increasing generalisation performance when compared to the equivalent maximum entropy classifier. We have participated in the CoNLL-2005 shared task closed challenge with full syntactic information.

1 Introduction

The semantic role labelling task (SRL) involves identifying which groups of words act as arguments to a given predicate. These arguments must be labelled with their role with respect to the predicate, indicating how the proposition should be semantically interpreted.

We apply conditional random fields (CRFs) to the task of SRL proposed by the CoNLL shared task 2005 (Carreras and M`arquez, 2005). CRFs are undirected graphical models which define a conditional distribution over labellings given an observation (Lafferty et al., 2001). These models allow for the use of very large sets of arbitrary, overlapping and non-independent features. CRFs have been applied with impressive empirical results to the tasks of named entity recognition (McCallum and Li, 2003; Cohn et al., 2005), part-of-speech (PoS) tagging (Lafferty et al., 2001), noun phrase chunking (Sha and Pereira, 2003) and extraction of table data (Pinto et al., 2003), among other tasks.

While CRFs have not been used to date for SRL, their close cousin, the maximum entropy model has been, with strong generalisation performance (Xue and Palmer, 2004; Lim et al., 2004). Most CRF implementations have been specialised to work with chain structures, where the labels and observations form a linear sequence. Framing SRL as a linear tagging task is awkward, as there is no easy model of adjacency between the candidate constituent phrases.

Our approach simultaneously performs both constituent selection and labelling, by defining an undirected random field over the parse tree. This allows the modelling of interactions between parent and child constituents, and the prediction of an optimal argument labelling for all constituents in one pass. The parse tree forms an acyclic graph, meaning that efficient exact inference in a CRF is possible using belief propagation.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 SemanticRoleLabellingwithTreeCo	Trevor Cohn Philip Blunsom			Semantic Role Labelling with Tree Conditional Random Fields