Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning.

In this paper, we compare two annotation schemes, PropBank and VerbNet, in a task-independent, general way, analysing how well they fare in capturing the linguistic generalisations that are known to hold for semantic role labels , and consequently how well they grammaticalise aspects of meaning.

An observation that holds for all semantic role labelling schemes is that certain labels seem to be more similar than others, based on their ability to occur in the same syntactic environment and to be expressed by the same function words.

However, most annotation schemes in NLP and linguistics assume that semantic role labels are atomic.

Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning.

(2007) show that augmenting PropB ank labels with VerbNet labels increases generalisation of the less frequent labels, such as ARG2, to new verbs and new domains, they also show that PropBank labels perform better overall, in a semantic role labelling task.

First, the argument labels for which the VerbNet improvement was found are infrequent, and might therefore not have influenced the overall results enough to counterbalance new errors introduced by the finer-grained annotation scheme; second, the learning methods in both these experimental settings are largely based on syntactic information, thereby confounding learning and generalisation due to syntax — which would favour the more syntactically-driven PropBank annotation — with learning due to greater generality of the semantic role annotation; finally, task-specific learning-based experiments do not guarantee that the learners be sufficiently powerful to make use of the full generality of the semantic role labels .

Verbal predicates in the Penn Treebank (PTB) receive a label REL and their arguments are annotated with abstract semantic role labels A0-A5 or AA for those complements of the predicative verb that are considered arguments, while those complements of the verb labelled with a semantic functional label in the original PTB receive the composite semantic role label AM-X, where X stands for labels such as LOC, TMP or ADV, for locative, temporal and adverbial modifiers respectively.

We describe a semantic role labeling system that makes primary use of CCG-based features.

This analysis also suggests that simultaneous incremental parsing and semantic role labeling may lead to performance gains in both tasks.

Semantic Role Labeling (SRL) is the process of assigning semantic roles to strings of words in a sentence according to their relationship to the semantic predicates expressed in the sentence.

An effective semantic role labeling system must recognize the differences between different configurations:

The results for gold standard parses are comparable to the winning system of the CoNLL 2005 shared task on semantic role labeling (Punyakanok et al., 2008).

This paper addresses two areas of work in event semantics, narrative event chains and semantic role labeling .

2.2 Semantic Role Labeling

Most work on semantic role labeling , however, is supervised, using Propbank (Palmer et al., 2005), FrameNet (Baker et al., 1998) or VerbNet (Kipper et al., 2000) as gold standard roles and training data.

Our argument learning algorithm not only performs unsupervised induction of situation-specific role classes, but the resulting roles and linking structures may also offer the possibility of (unsupervised) FrameNet-style semantic role labeling .

Most previous work on unsupervised semantic role labeling assumes that the set of possible

A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank.

We confirmed that modeling the role generalization at feature level was better than the conventional approach that replaces semantic role labels .

Semantic Role Labeling (SRL) is a task of analyzing predicate-argument structures in texts.