Towards Robust Semantic Role Labeling

Pradhan, S. Sandeep; Ward, Wayne H.; Martin, James H.

doi:10.1162/coli.2008.34.2.289

Cited by 62 publications

(41 citation statements)

References 27 publications

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“…We thus see that all systems suffer severely from domain sensitivity, but we also see that the dependency-based systems are more resilient -the difference between MST and C&J is statistically significant at the 97.5% level and corresponds to an error reduction of 2%. The experiment reconfirms previous results (Carreras and Màrquez, 2005) that the argument classification part of SRL systems is sensitive to domain changes, and Pradhan et al (2008) Table 6: Classification accuracy on the NTI texts. Dependency-based systems make 2% fewer errors.…”

Section: Out-of-domain Test Setssupporting

confidence: 87%

The effect of syntactic representation on semantic role labeling

Johansson

Nugues

2008

Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08

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Almost all automatic semantic role labeling (SRL) systems rely on a preliminary parsing step that derives a syntactic structure from the sentence being analyzed. This makes the choice of syntactic representation an essential design decision. In this paper, we study the influence of syntactic representation on the performance of SRL systems. Specifically, we compare constituent-based and dependencybased representations for SRL of English in the FrameNet paradigm.Contrary to previous claims, our results demonstrate that the systems based on dependencies perform roughly as well as those based on constituents: For the argument classification task, dependencybased systems perform slightly higher on average, while the opposite holds for the argument identification task. This is remarkable because dependency parsers are still in their infancy while constituent parsing is more mature. Furthermore, the results show that dependency-based semantic role classifiers rely less on lexicalized features, which makes them more robust to domain changes and makes them learn more efficiently with respect to the amount of training data.

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Section: Out-of-domain Test Setssupporting

confidence: 87%

The effect of syntactic representation on semantic role labeling

Johansson

Nugues

2008

Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08

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“…Later on, studies by for instance Toutanova et al (2008), Pradhan et al (2008), Johansson and Nugues (2008), Punyakanok et al (2008), Surdeanu et al (2007) and Moschitti et al (2008) have investigated the details of the topic further. Also the CoNLL shared tasks of (Carreras and Màrques 2004Carreras and Màrquez 2005) have resulted in a large number of systems for English.…”

Section: Related Workmentioning

confidence: 99%

The Finnish Proposition Bank

Haverinen

Kanerva

Kohonen

et al. 2015

Lang Resources & Evaluation

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We present the Finnish PropBank, a resource for semantic role labeling (SRL) of Finnish based on the Turku Dependency Treebank whose syntax is annotated in the well-known Stanford Dependency (SD) scheme. The contribution of this paper consists of the lexicon of the verbs and their arguments present in the treebank, as well as the predicate-argument annotation of all verb occurrences in the treebank text. We demonstrate that the annotation is of high quality, that the SD scheme is highly compatible with PropBank annotation, and further that the additional dependencies present in the Turku Dependency Treebank are clearly beneficial for PropBank annotation. Further, we also use the PropBank to provide a strong baseline for automated Finnish & Filip Ginter

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“…On the other hand, in order to enrich the meta-information network, we extract more coarse-grained salient fact types based on Semantic Role Labeling (SRL) (Pradhan et al, 2008). For example, given the sentence "In North Carolina, 10 counties are being evacuated.…”

Section: Information Extraction From Textsmentioning

confidence: 99%

Proceedings of the Third Workshop on Vision and Language

2014

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PrefaceThe Workshop on Vision and Language 2014 (VL'14) took place in Dublin on 23rd July 2014, as part of COLING'14. It was the joint 3rd meeting of the EPSRC Network On Vision and Language and 1st technical meeting of the new European Network on Integrating Vision and Language which is funded as a European COST Action. The VL workshops have the general aims:1. to provide a forum for reporting and discussing planned, ongoing and completed research that involves both language and vision; and 2. to enable NLP and computer vision researchers to meet, exchange ideas, expertise and technology, and form new research partnerships.As funding for the V&L EPSRC Network (EP/H018557) ends and funding for the iV&L Net European COST Action (IC1307) starts, the focus of the VL workshops will shift onto integration and joint modelling of language and vision. iV&L Net will take over the organisation of annual VL workshops for the next four years as the flagship workshop of this new COST Action.The call for papers for VL'14 was issued in May 2014 and elicited a good number of highquality submissions, each of which was peer-reviewed by three members of the programme committee. The interest in the workshop from leading NLP and computer vision researchers and the quality of submissions was high, so we aimed to be as inclusive as possible within the practical constraints of the workshop. In the end we accepted 14 submissions as long papers, and eight as short papers.The resulting workshop programme packed a lot of exciting content into one day. We were delighted to be able to include in the programme a keynote presentation by Alex Jaimes of Yahoo! Inc., an internationally leading vision researcher. Our technical programme combined seven oral papers, seven long poster papers and seven short poster papers. Some thematic clusters emerged: combined text and image processing (Nguyen et al., Sakaki et al., Jones et al., Zhang et al., HaCohen-Kerner et al.), image description, annotation and labelling (Elliott, Liparas et al., Wang et al., Jokinen and Wilcock), data set creation (Weiland et al., Le et al., McGuinness et al.), situated dialogue (Summers-Stay et al., Schütte et al.), video analysis (Bhat and Olszewska, Shrestha et al.), aids for visually impaired people (Safi et al., Belz and Bharath), and visual analysis supported by text/speech features (Anbarjafari and Aabloo). The programme also included a discussion session on future directions for the VL community and workshops, including plans for shared task competitions.We would like to thank all the people who have contributed to the organisation and delivery of this workshop: the authors who submitted such high quality papers; the programme committee for their prompt and effective reviewing; our keynote speaker, Alex Jaimes; the COLING 2014 organising committee, especially the workshops chairs, Jennifer Foster, Dan Gildea, and Tim Baldwin; the participants in the workshop; and future readers of these proceedings for your shared interest in this exciting new area of research. Aug...

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Towards Robust Semantic Role Labeling

Cited by 62 publications

References 27 publications

The effect of syntactic representation on semantic role labeling

The effect of syntactic representation on semantic role labeling

The Finnish Proposition Bank

Proceedings of the Third Workshop on Vision and Language

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