Semantic Role Labelling with Tree Conditional Random Fields

By Trevor Cohn, Phil Blunsom. 2005

117 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Extraction

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Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, methodology): “CRFs have been widely used in the NLP world, such as for POS tagging (Lafferty et al., 2001), chunking (Sha and Pereira, 2003), NER (McCallum and Li, 2003) or SRL (Cohn and Blunsom, 2005).”
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields (2015, methodology): “, 2003], semantic role labeling [Cohn and Blunsom, 2005], topic modeling [Zhu and Xing, 2010], information extraction [Peng and McCallum, 2006], shallow parsing [Sha and Pereira, 2003], named-entity recognition [Settles, 2004], as well as a host of other applications in natural language processing a”
Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling (2005, methodology): “…shared tasks, we ﬁnd Relevant Vector Machine (RVM), which is a kernel–based linear discriminant inside the framework of Sparse Bayesian Learning (Johansson and Nugues, 2005) and Tree Conditional Random Fields (T-CRF) (Cohn and Blunsom, 2005), that extend the sequential CRF model to tree structures.”
Special Issue Introduction: Semantic Role Labeling: An Introduction to the Special Issue (2008, unknown): “…probabilistic models have also been applied to produce the structured output, for example, generative models (Thompson, Levy, and Manning 2003), sequence tagging with classiﬁers (M`arquez et al. 2005; Pradhan et al. 2005b), and Conditional Random Fields on tree structures (Cohn and Blunsom 2005).”
Deep Learning for Efficient Discriminative Parsing (2011, methodology): “Since the introduction of lexicalized probabilistic context-free grammar (PCFGs) parsers (Magerman, 1995; Collins, 1996), improvements have been achieved over the years, but generative PCFGs parsers of the last decade from Collins (1999) and Charniak (2000) still remain standard benchmarks.”