A Corpus Study of Verbal Multiword Expressions in Brazilian Portuguese

Ramisch, Carlos; Ramisch, Renata; Zilio, Leonardo; Villavicencio, Aline; Cordeiro, Silvio

doi:10.1007/978-3-319-99722-3_3

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Most errors are due to the previous modules of the processing pipeline, particularly the PoS tagger and the statistical and rule-based disambiguator. Manual correction of these errors made it possible to achieve, in the strictest criterion, a recall of 91.9% and 93.6% for each type of examples, and an overall perfomance of 92.5% In the near future, we intend to integrate the corresponding lexicon-grammar of Brazilian Portuguese [19] and perform an extrinsic evaluation using (or adapting) the Portuguese corpus developed in-house [3] and that built for the PARSEME project 5 [15,16].…”

Section: Discussionmentioning

confidence: 99%

“…Processing multiword expressions, including verbal idioms, is essencial to represent the meaning of a text in a adequate way. The low frequency of many verbal idioms in corpora makes spotting them a difficult task [13], and much prior has been dedicated to identifying them in texts [14,15,16]. However, the focus of this paper will not be on identification (in a lexicographic perspective), but rather on the processing of an already built computational lexicon (a lexicon-grammar) of verbal idioms [2,3], particularly of transformationallyderived, equivalent sentence-forms (for lack of space, this lexicon-grammar will not be presented here).…”

Section: Introductionmentioning

confidence: 99%

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Processing European Portuguese Verbal Idioms: From the Lexicon-Grammar to a Rule-based Parser

Galvão¹,

Baptista²,

Mamede³

2019

Proceedings of the Third International Conference, Europhras 2019, Computational and Corpus-Based Phraseology

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Processing verbal idioms is a challenging task for Natural Language Processing systems because they are syntactically analysable strings, with a wellformed structure, identical to that of distributionally free sentences, but whose meaning is for the most part non-compositional. This paper presents recent advances in processing European Portuguese verbal idioms. From a lexicon-grammar matrix, containing +2,500 verbal idioms and +100 (structural, distributional and transformational) properties, parsing rules are automatically generated, within the framework of a rule-based incremental parser. They are then integrated in STRING, a fully-fledged natural language processing system for Portuguese. The system now identifies not only the idioms' base forms, but also the sentences resulting from some productive and very general transformations (passive, pronominalisation), admitted by some of these idioms. Other improvements include: a newly developed lexicon-grammar validator, a new generation module for transformations' examples, and a new, more granular, evaluation module. An intrinsic evaluation achieves an overall recall of 92.5%.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Processing European Portuguese Verbal Idioms: From the Lexicon-Grammar to a Rule-based Parser

Galvão¹,

Baptista²,

Mamede³

2019

Proceedings of the Third International Conference, Europhras 2019, Computational and Corpus-Based Phraseology

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“…Language-specific PARSEME corpus description papers not covered here can provide details, e.g. for Basque , Chinsese (Jiang et al, 2018), English , Irish , Italian (Monti and di Buono, 2019), Polish , Portuguese (Ramisch et al, 2018b), Romanian (Barbu Mititelu et al, 2019), Turkish (Berk et al, 2018b;Ozturk et al, 2022), among others. Domains Corpus domain may play an important role in MWEI.…”

Section: Corpus Constitution and Selectionmentioning

confidence: 99%

A Survey of MWE Identification Experiments: The Devil is in the Details

Ramisch,

Walsh,

Blanchard

et al. 2023

Proceedings of the 19th Workshop on Multiword Expressions (MWE 2023)

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Multiword expression (MWE) identification has been the focus of numerous research papers, especially in the context of the DiMSUM and PARSEME Shared Tasks (STs). This survey analyses 40 MWE identification papers with experiments on data from these STs. We look at corpus selection, pre-and post-processing, MWE encoding, evaluation metrics, statistical significance, and error analyses. We find that these aspects are usually considered minor and/or omitted in the literature. However, they may considerably impact the results and the conclusions drawn from them. Therefore, we advocate for more systematic descriptions of experimental conditions to reduce the risk of misleading conclusions drawn from poorly designed experimental setup.

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“…To overcome this limitation, data-driven approaches opt instead to make use of frequency statistics in the corpus to address both candidate generation and quality estimation [9], [30], [34], [7], [10], [23]. They do not rely on complex linguistic feature generation, domain-specific rules or extensive labeling efforts.…”

Section: Related Workmentioning

confidence: 99%

Automated Phrase Mining from Massive Text Corpora

Shang

Liu

Jiang

et al. 2018

IEEE Trans. Knowl. Data Eng.

274

199

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As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state-of-the-art models, even data-driven models, is fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, AutoPhrase, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, AutoPhrase has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, AutoPhrase can be extend to model single-word quality phrases.

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A Corpus Study of Verbal Multiword Expressions in Brazilian Portuguese

Cited by 5 publications

References 11 publications

Processing European Portuguese Verbal Idioms: From the Lexicon-Grammar to a Rule-based Parser

Processing European Portuguese Verbal Idioms: From the Lexicon-Grammar to a Rule-based Parser

A Survey of MWE Identification Experiments: The Devil is in the Details

Automated Phrase Mining from Massive Text Corpora

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