Semantic Role Labeling of Implicit Arguments for Nominal Predicates

Gerber, Matthew S.; Chai, Joyce

doi:10.1162/coli_a_00110

Cited by 76 publications

(110 citation statements)

References 31 publications

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“…The results obtained by Gerber and Chai (2012) are not directly comparable, since their approach was hand-tailored towards nominal implicit SRL in the newswire domain. It incorporates a large number of detailed linguistic resources (Penn Treebank, Penn Discourse Bank, NomBank, FrameNet) and assumes gold standard information on all levels.…”

Section: Methodsmentioning

confidence: 94%

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Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2015

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will take place in Denver on June 4 and 5 and is colocated with SemEval and NAACL. As in 2014 at COLING, also on this occasion *SEM and SemEval chose to coordinate their programs by featuring a joint invited talk. In this way, *SEM aims to bring together the ACL SIGLEX and ACL SIGSEM communities.The acceptance rate of *SEM 2015 was quite competitive: out of 98 submissions, we accepted 36 papers for an overall acceptance of 37%. The acceptance rate of long paper that were accepted for oral presentation (18 out of 62) is 29%. The papers cover a wide range of topics including distributional semantics; lexical semantics and lexical acquisition; formal and linguistic semantics; discourse semantics; lexical resources, linked data and ontologies; semantics for applications; and extra-propositional semantics: sentiment and figurative meaning.The *SEM 2015 program consists of oral presentations for selected long papers and a poster session for long and short papers.Day One, June 4th:• Joint *SEM SemEval keynote talk by Marco Baroni;• Oral presentation sessions on distributional semantics, lexical semantics, and extra-propositional semantics;• Poster session.Day Two, June 5th:• Keynote talk by Preslav Natkov;• Oral presentation sessions on semantics for applications, lexical resources and ontologies, formal semantics, and discourse semantics;• *SEM Best Paper Award.We cannot finish without saying that *SEM 2015 would not have been possible without the considerable efforts of our area chairs, their reviewers, and the computational semantics community in general.We hope you will enjoy *SEM 2015, Distributional semantic methods have some a priori appeal as models of human meaning acquisition, because they induce word representations from contextual distributions naturally occurring in corpus data without need for supervision. However, learning the meaning of a (concrete) word also involves establishing a link between the word and its typical visual referents, which is beyond the scope of classic, text-based distributional semantics. Since recently several proposals have been put forward about how to induce multimodal word representations from linguistic and visual contexts, it is natural to ask if this line of work, besides its practical implications, can help us to develop more realistic, grounded models of human word learning within the distributional semantics framework.In my talk, I will report about two studies in which we used multimodal distributional semantics (MDS) to simulate human word learning. In one study, we first measured the ability of subjects to link a nonce word to relevant linguistic and visual associates when prompted only by exposure to minimal corpus evidence about it. We then simulated the same task with an MDS model, finding its behavior remarkably similar to that of subjects. In the second study, we constructed a corpus in which child-directed speech is aligned with real-life pictures of the objects mentioned by care-givers. We then trained our MDS model on these data, and inspected the generaliza...

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Section: Methodsmentioning

confidence: 94%

“…A study by Gerber and Chai (2012) investigated implicit arguments of NomBank nominalizations. They extended a part of the PropBank corpus with implicit roles for 10 nominal predicates, of which they annotated all instances.…”

Section: Annotated Corpora For Implicit Srlmentioning

confidence: 99%

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2015

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“…Gerber and Chai (2010) augment NomBank annotations with additional numbered arguments appearing in the same or previous sentences; posterior work obtained better results for the same task (Gerber and Chai, 2012;Laparra and Rigau, 2013). The SemEval-2010 Task 10: Linking Events and their Participants in Discourse (Ruppenhofer et al, 2009) targeted cross-sentence missing numbered arguments in PropBank and FrameNet.…”

Section: Related Workmentioning

confidence: 97%

Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles

Blanco¹,

Vempala²

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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This paper presents a framework to infer spatial knowledge from verbal semantic role representations. First, we generate potential spatial knowledge deterministically. Second, we determine whether it can be inferred and a degree of certainty. Inferences capture that something is located or is not located somewhere, and temporally anchor this information. An annotation effort shows that inferences are ubiquitous and intuitive to humans.

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“…FrameNet (Ruppenhofer et al, 2016) covers all these, but also covers relational nouns that do not evoke an event, such as "president". Other lines of work address semantic arguments that appear outside sentence boundaries, or that do not explicitly appear anywhere in the text (Gerber and Chai, 2010;Roth and Frank, 2015).…”

Section: Semantic Contentmentioning

confidence: 99%

The State of the Art in Semantic Representation

Abend¹,

Rappoport²

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarify the general goals of research on semantic representation. We address these gaps by critically surveying the state of the art in the field.

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Semantic Role Labeling of Implicit Arguments for Nominal Predicates

Cited by 76 publications

References 31 publications

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles

The State of the Art in Semantic Representation

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