Specifically, we consider unsupervised induction of semantic roles from sentences annotated with automatically-predicted syntactic dependency representations and use a state-of-the-art generative Bayesian nonparametric model.

Semantic role labeling (SRL) (Gildea and Juraf-sky, 2002) involves predicting predicate argument structure, i.e.

and their assignment to underlying semantic roles .

Though syntactic representations are often predictive of semantic roles (Levin, 1993), the interface between syntactic and semantic representations is far from trivial.

In this section we describe one of the Bayesian models for semantic role induction proposed in (Titov and Klementiev, 2012).

As we mentioned in the introduction, in this work we focus on the labeling stage of semantic role labeling.

In the labeling stage, semantic roles are represented by clusters of arguments, and labeling a particular argument corresponds to deciding on its role cluster.

In sum, we treat the unsupervised semantic role labeling task as clustering of argument keys.

On the source side, the features include the verbal predicate, the semantic role of the argument, the head word and the boundary words of the argument.

its semantic role A7"

To train the proposed predicate translation model and argument reordering model, we first parsed all source sentences using the Berkeley Chinese parser (Petrov et al., 2006) and then ran the Chinese semantic role labeler6 (Li et al., 2010) on all source parse trees to annotate semantic roles for all verbal predicates.

After we obtained semantic roles on the source side, we extracted features as described in Section 3.2 and 4.2 and used these features to train our two models as described in Section 3.3 and 4.3.

tic window, we use its semantic role (i.e., ARGO, ARGM-TMP and so on) A3; and head word A?

In order to train the discriminative predicate translation model, we first parse source sentences and labeled semantic roles for all verbal predicates (see details in Section 6.1) in our word-aligned bilingual training data.

Therefore they either postpone the integration of target side PASs until the whole decoding procedure is completed (Wu and Fung, 2009b), or directly project semantic roles from the source side to the target side through word alignments during decoding (Liu and Gildea, 2010).

(2011) incorporate source language semantic role labels into a tree-to-string SMT system.

In addition we group Verbnet semantic roles as shown in Table 4.

From a practical perspective, they support factorisa—tion and have been shown to be effective in various NLP (Natural language Processing) tasks such as semantic role labelling (Swier and Stevenson, 2005) or word sense disambiguation (Dang, 2004).

Our aim is to accquire a classification which covers the core verbs of French, could be used to support semantic role labelling and is similar in spirit to the English Verbnet.