Abstract | 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. |
Background and Motivation | 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). |
Background and Motivation | A number of approaches to the construction of semantic role labeling models for new languages |
Background and Motivation | 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. |
Conclusion | 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. |
Related Work | 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. |
Setup | 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. |
Setup | 2.1 Semantic Role Labeling Model |
Setup | We consider the dependency-based version of semantic role labeling as described in Hajic et al. |
Abstract | 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. |
Conclusion | 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. |
Experiments and Results | We report the performance of inference on two NLP tasks: semantic role labeling and the task of extracting entities and relations from text. |
Experiments and Results | 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) . |
Experiments and Results | 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. |
Introduction | 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). |
Abstract | This paper presents a novel deterministic algorithm for implicit Semantic Role Labeling . |
Conclusions and Future Work | In this work we have presented a robust deterministic approach for implicit Semantic Role Labeling . |
Conclusions and Future Work | As input it only needs the document with explicit semantic role labeling and Super-Sense annotations. |
Introduction | 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). |
Introduction | for Implicit Semantic Role Labelling |
Related Work | 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. |
Abstract | We model the problem as a joint dependency parsing and semantic role labeling task. |
Introduction | We model our problem as a joint dependency parsing and role labeling task, assuming a Bayesian generative process. |
Problem Formulation | We formalize the learning problem as a dependency parsing and role labeling problem. |
Problem Formulation | In addition, the role labeling problem is to assign a tag to each noun phrase in a specification tree, indicating whether the phrase is a key phrase or a background phrase. |
Experimental Setup | 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). |
The Framework | Cross-document coreference resolution, semantic role labeling and relation extraction are accomplished Via the methods described in Section 5. |
The Framework | semantic role label |