Using Cross-Linguistic Knowledge to Build VerbNet-Style Lexicons: Results for a (Brazilian) Portuguese VerbNet

Scarton, Carolina; Duran, Magali Sanches; Aluísio, Sandra Maria

doi:10.1007/978-3-319-09761-9_15

Cited by 11 publications

(10 citation statements)

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“…The ones related to verbs include PropBank-Br [35] (based on PropBank), FrameNet Brasil [36] and FrameCorp [37] (based on FrameNet), WordNet.Br [38], WordNet.Pt [39,40], one of the Wordnets in the MultiWordNet Project [41] (based on WordNet) as well as VerbNet.Br [42] based on English VerbNet. The latter project, which is most closely related to our work, provides alignments between English VerbNet, WordNet and WordNet.Br, and enables semi-automatically inferring Levin classes for Br.…”

Section: Related Workmentioning

confidence: 99%

“…Because many of the verbs in the resulting gold standard were quite low in frequency in our corpus, we supplemented the resource with additional member verbs from VerbNet.Br -the resource recently developed by Scarton and Aluísio [42]. As the classifications in this resource are noisy, we used a native language expert to validate the class memberships according to the criteria we had developed during the translation of the first version of the gold standard.…”

Section: Gold Standard For Brazilian Portuguesementioning

confidence: 99%

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Verb Clustering for Brazilian Portuguese

Scarton

Sun

Kipper-Schuler

et al. 2014

Computational Linguistics and Intelligent Text Processing

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Abstract. Levin-style classes which capture the shared syntax and semantics of verbs have proven useful for many Natural Language Processing (NLP) tasks and applications. However, lexical resources which provide information about such classes are only available for a handful of worlds languages. Because manual development of such resources is extremely time consuming and cannot reliably capture domain variation in classification, methods for automatic induction of verb classes from texts have gained popularity. However, to date such methods have been applied to English and a handful of other, mainly resource-rich languages. In this paper, we apply the methods to Brazilian Portuguese -a language for which no VerbNet or automatic class induction work exists yet. Since Levinstyle classification is said to have a strong cross-linguistic component, we use unsupervised clustering techniques similar to those developed for English without language-specific feature engineering. This yields interesting results which line up well with those obtained for other languages, demonstrating the crosslinguistic nature of this type of classification. However, we also discover and discuss issues which require specific consideration when aiming to optimise the performance of verb clustering for Brazilian Portuguese and other less-resourced languages.

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Section: Related Workmentioning

confidence: 99%

Section: Gold Standard For Brazilian Portuguesementioning

confidence: 99%

Verb Clustering for Brazilian Portuguese

Scarton

Sun

Kipper-Schuler

et al. 2014

Computational Linguistics and Intelligent Text Processing

Self Cite

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“…Some of these have been developed manually from scratch, aiming to capture properties specific to the language in question, e.g., resources for Spanish and Catalan (Aparicio et al 2008), Czech (Pala and Horák 2008), and Mandarin (Liu and Chiang 2008). Others have been created (semi-)automatically, using already existing resources and aiming to reduce the time-expense involved, e.g., for Brazilian Portuguese (Scarton and Aluısio 2012), French (Pradet et al 2014), and Croatian (Mikelić Preradović and Boras 2013). Also fully automatic methods have been used, e.g., for French (Sun et al 2010) and Brazilian Portuguese (Scarton et al 2014).…”

Section: Levin Classes and Verbnetmentioning

confidence: 99%

Investigating the cross-lingual translatability of VerbNet-style classification

Majewska

Vulić

McCarthy

et al. 2017

Lang Resources & Evaluation

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VerbNet-the most extensive online verb lexicon currently available for English-has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We

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“…In the future, we plan to use Verbnet classes, an information brought from the equivalent verb sense in Propbank, to find in Verbnet-Br (Scarton et al, 2014) verb senses that are not in Verbo-Brasil.…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

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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Using Cross-Linguistic Knowledge to Build VerbNet-Style Lexicons: Results for a (Brazilian) Portuguese VerbNet

Cited by 11 publications

References 11 publications

Verb Clustering for Brazilian Portuguese

Verb Clustering for Brazilian Portuguese

Investigating the cross-lingual translatability of VerbNet-style classification

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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