Semantic Role Labeling (SRL) has become one of the standard tasks of natural language processing and proven useful as a source of information for a number of other applications.

Semantic role labeling has proven useful in many natural language processing tasks, such as question answering (Shen and Lapata, 2007; Kaisser and Webber, 2007), textual entailment (Sammons et al., 2009), machine translation (Wu and Fung, 2009; Liu and Gildea, 2010; Gao and Vogel, 2011) and dialogue systems (Basili et al., 2009; van der Plas et al., 2009).

A number of approaches to the construction of semantic role labeling models for new languages

In this work we construct a shared feature representation for a pair of languages, employing cross-lingual representations of syntactic and lexical information, train a semantic role labeling model on one language and apply it to the other one.

We have considered the cross-lingual model transfer approach as applied to the task of semantic role labeling and observed that for closely related languages it performs comparably to annotation projection approaches.

Unsupervised semantic role labeling methods (Lang and Lapata, 2010; Lang and Lapata, 2011; Titov and Klementiev, 2012a; Lorenzo and Cerisara, 2012) also constitute an alternative to cross-lingual model transfer.

The purpose of the study is not to develop a yet another semantic role labeling system — any existing SRL system can (after some modification) be used in this setup — but to assess the practical applicability of cross-lingual model transfer to this problem, compare it against the alternatives and identify its strong/weak points depending on a particular setup.

2.1 Semantic Role Labeling Model

We consider the dependency-based version of semantic role labeling as described in Hajic et al.

In our experiments, using the NLP tasks of semantic role labeling and entity-relation extraction, we demonstrate that with the margin-based algorithm, we need to call the inference engine only for a third of the test examples.

We show via experiments that these methods individually give a reduction in the number of calls made to an inference engine for semantic role labeling and entity-relation extraction.

We report the performance of inference on two NLP tasks: semantic role labeling and the task of extracting entities and relations from text.

Semantic Role Labeling (SRL) Our first task is that of identifying arguments of verbs in a sentence and annotating them with semantic roles (Gildea and Jurafsky, 2002; Palmer et al., 2010) .

For the semantic role labeling task, we need to call the solver only for one in six examples while for the entity-relations task, only one in four examples require a solver call.

We evaluate the two schemes and their combination on two NLP tasks where the output is encoded as a structure: PropBank semantic role labeling (Punyakanok et al., 2008) and the problem of recognizing entities and relations in text (Roth and Yih, 2007; Kate and Mooney, 2010).

This paper presents a novel deterministic algorithm for implicit Semantic Role Labeling .

In this work we have presented a robust deterministic approach for implicit Semantic Role Labeling .

As input it only needs the document with explicit semantic role labeling and Super-Sense annotations.

Traditionally, Semantic Role Labeling (SRL) systems have focused in searching the fillers of those explicit roles appearing within sentence boundaries (Gildea and Jurafsky, 2000, 2002; Carreras and Marquez, 2005; Surdeanu et al., 2008; Hajic et al., 2009).

for Implicit Semantic Role Labelling

SEMAFOR (Chen et al., 2010) is a supervised system that extended an existing semantic role labeler to enlarge the search window to other sentences, replacing the features defined for regular arguments with two new semantic features.

Documents are processed by a full NLP pipeline, including token and sentence segmentation, parsing, semantic role labeling , and an information extraction pipeline consisting of mention detection, NP coreference, cross-document resolution, and relation detection (Florian et al., 2004; Luo et al., 2004; Luo and Zitouni, 2005).

Cross-document coreference resolution, semantic role labeling and relation extraction are accomplished Via the methods described in Section 5.