Semantic Role Labeling with CRFs

Citation

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

Online version

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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

where the actual feature function $f$ is divided into single-node feature function $g$, and two-node feature function $h$.

Features Used

As the cliques considered are single-node and two-node cliques, the features were also defined for both single nodes and parent-child pairs. There were many syntactic features used; I will not be describing each of them, but will provide reference as also given in the paper for their description. The syntactic features or the feature types were made into binary functions $g$ and $h$ by combining (feature type, feature value) pairs with label (for a single node) or label pairs (for two-noded cliques), when such a feature-type, feature-value was seen at least once in the training data.
The different feature types used were:
Basic features: {Head word, head PoS, phrase syntactic category, phrase path, position relative to the predicate, surface distance to the predicate, predicate lemma, predicate token, predicate voice, predicate sub-categorisation, syntactic frame}. These features are common to many SRL systems and are described in Xue and Palmer (2004).
Context features: {Head word of first NP in preposition phrase, left and right sibling head words and syntactic categories, first and last word in phrase yield and their PoS, parent syntactic category and head word}. These features are described in Pradhan et al (2005).
Common ancestor of the verb: The syntactic category of the deepest shared ancestor of both the verb and node.
Feature conjunctions: The following features were conjoined: { predicate lemma + syntactic category, predicate lemma + relative position, syntactic category + first word of the phrase}.
Default feature: This feature is always on, which allows the classifier to model the prior probability distribution over the possible argument labels.
Joint features: These features were only defined over pair-wise cliques: {whether the parent and child head words do not match, parent syntactic category + and child syntactic category, parent relative position + child relative position, parent relative position + child relative position + predicate PoS + predicate lemma}.

Results for Rating Prediction