Unsupervised Semantic Frame Induction using Triclustering

Ustalov, Dmitry; Panchenko, Alexander; Kutuzov, Andrey; Biemann, Chris; Ponzetto, Simone Paolo

doi:10.18653/v1/p18-2010

Cited by 23 publications

(23 citation statements)

References 25 publications

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“…Concerning frame induction, Ustalov et al (2018a) proposed a graph-based approach for the triclustering of Subject-Verb-Object (SVO) triples extracted using a dependency parser. Each vertex in the graph is the SVO triple, represented by the concatenation of word embeddings for the three elements.…”

Section: Related Workmentioning

confidence: 99%

“…Considering the subtasks we are approaching, we must use an approach that is able to induce not only semantic roles, but also semantic frames and its slots. In this sense, of the approaches described in the previous section, the triclustering approach proposed by Ustalov et al (2018a) is the only one able to induce frames. However, in the context of our task, it has two major flaws.…”

Section: Induction Approachmentioning

confidence: 99%

“…To deal with the second flaw, we replace the perword embeddings used by Ustalov et al (2018a) with contextualized word representations. These include information concerning the context in which a word appears and, thus, the position of a word in the embedding space varies according to that context.…”

Section: Induction Approachmentioning

confidence: 99%

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L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations

Ribeiro¹,

Mendonça²,

Ribeiro³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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Building large datasets annotated with semantic information, such as FrameNet, is an expensive process. Consequently, such resources are unavailable for many languages and specific domains. This problem can be alleviated by using unsupervised approaches to induce the frames evoked by a collection of documents. That is the objective of the second task of SemEval 2019, which comprises three subtasks: clustering of verbs that evoke the same frame and clustering of arguments into both frame-specific slots and semantic roles. We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments. Using such representations is appropriate in the context of this task, since they provide cues for word-sense disambiguation. Thus, they can be used to identify different frames evoked by the same words. Using this approach we were able to outperform all of the baselines reported for the task on the test set in terms of Purity F 1 , as well as in terms of BCubed F 1 in most cases.

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Section: Related Workmentioning

confidence: 99%

Section: Induction Approachmentioning

confidence: 99%

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L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations

Ribeiro¹,

Mendonça²,

Ribeiro³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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“…Woodsend and Lap- Figure 2: Description of ditransitive Cx ata (2015) use distributional representations to induce embeddings for predicates and their arguments. Ustalov et al (2018) propose a different methodology for unsupervised semantic frame induction. They build embeddings as the concatenations of subject-verb-object triples and identify frames as clustered triples.…”

Section: Framesmentioning

confidence: 99%

“…As for frames, there has been some work on using distributional similarity between vectors for their unsupervised induction (Ustalov et al, 2018), for comparing frames across languages (Sikos and Padó, 2018), and even for the automatic identification of the semantic relations holding between them (Botschen et al, 2017).…”

Section: Some Other Arguments In Favor Of a Distributional Cxgmentioning

confidence: 99%

Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning

Rambelli¹,

Chersoni²,

Blache³

et al. 2019

Proceedings of the First International Workshop on Designing Meaning Representations

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In this paper, we propose a new type of semantic representation of Construction Grammar that combines constructions with the vector representations used in Distributional Semantics. We introduce a new framework, Distributional Construction Grammar, where grammar and meaning are systematically modeled from language use, and finally, we discuss the kind of contributions that distributional models can provide to CxG representation from a linguistic and cognitive perspective.

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Open‐domain event schema induction via weighted attentive hypergraph neural network

Qin,

Wang,

Luo

2024

Concurrency and Computation

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SummaryEvent schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.

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Unsupervised Semantic Frame Induction using Triclustering

Cited by 23 publications

References 25 publications

L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations

L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations

Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning

Open‐domain event schema induction via weighted attentive hypergraph neural network

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