Semantic Role Labeling with CRFs

Citation

Trevor Cohn, Philip Blunsom, "Semantic Role Labeling with Conditional Random Fields", CoNLL 2005

Online version

Click here to download

Introduction

This paper aims at Semantic Role Labeling or SRL of sentences using Conditional Random Fields. This was the first attempt of solving the problem of SRL using CRF. The authors defined CRF over the tree structure of the syntactic parse tree of the sentence, rather than defining it on the linear sentence structure as is usually done for the tasks of Named Entity Recognition or Part-of-Speech tagging. The motivation behind this came from the very nature of semantic role labeling which is the task of labeling phrases with their semantic labels with respect to a particular constituent of the sentence, the predicate or the verb. The authors conjectured that for this reason, modeling linear chain CRF was not intuitive for SRL. The problem of SRL is usually broken into two parts: identifying candidate phrases for assigning semantic roles, and predicting the semantic role to be assigned to the identified phrase. The approach in this paper does both these things in a single pass over the syntactic tree structure.

Dataset Used

The dataset used was the Propbank corpus, which is the Penn Treebank corpus with semantic role annotation.

CRF Model

The CRF was defined over the tree structure of the sentence as:

where $C$ is the set of cliques in the observation tree, $\lambda {_{k}}$ are model's parameters, and $f$ is the function that maps label for a clique to a vector of scalar values.
The cliques considered were single-node (just one node in the syntactic tree), and two-node (parent and child nodes) ones. The CRF model can thus be restated as

Results for Rating Prediction