The Brazilian Portuguese Lexicon: An Instrument for Psycholinguistic Research

Abstract

In this article, we present the Brazilian Portuguese Lexicon, a new word-based corpus for psycholinguistic and computational linguistic research in Brazilian Portuguese. We describe the corpus development, the specific characteristics on the internet site and database for user access. We also perform distributional analyses of the corpus and comparisons to other current databases. Our main objective was to provide a large, reliable, and useful word-based corpus with a dynamic, easy-to-use, and intuitive interface with free internet access for word and word-criteria searches. We used the Núcleo Interinstitucional de Linguística Computacional’s corpus as the basic data source and developed the Brazilian Portuguese Lexicon by deriving and adding metalinguistic and psycholinguistic information about Brazilian Portuguese words. We obtained a final corpus with more than 30 million word tokens, 215 thousand word types and 25 categories of information about each word. This corpus was made available on the internet via a free-access site with two search engines: a simple search and a complex search. The simple engine basically searches for a list of words, while the complex engine accepts all types of criteria in the corpus categories. The output result presents all entries found in the corpus with the criteria specified in the input search and can be downloaded as a.csv file. We created a module in the results that delivers basic statistics about each search. The Brazilian Portuguese Lexicon also provides a pseudoword engine and specific tools for linguistic and statistical analysis. Therefore, the Brazilian Portuguese Lexicon is a convenient instrument for stimulus search, selection, control, and manipulation in psycholinguistic experiments, as also it is a powerful database for computational linguistics research and language modeling related to lexicon distribution, functioning, and behavior.

When statistics collide: The use of transitional and phonotactic probability cues to word boundaries

“…The calculator had two parts: the database and the search engine (algorithm based on Vitevitch & Luce, 2004). The database was built from a comprehensive Brazilian-Portuguese corpus (Estivalet & Meunier, 2015) in five steps.…”

“…To ensure that both sets were very similar to actual words from Brazilian-Portuguese, we calculated the mean number of insertions, deletions, and substitutions needed to transform PP+ and PP-words into their closest 20 phonetic neighbors (i.e., Levenshtein Distance; Yarkoni et al, 2008). We used the package vwr (Keuleers, 2013) for R (R Core Team, 2017) and the same Brazilian-Portuguese corpus in these calculations (Estivalet & Meunier, 2015; Table 1). The analysis indicated that both sets were very close to actual Brazilian-Portuguese words (1.26 for PP+, 1.52 for PP-; the smaller the number of operations, the closer the words were to Brazilian-Portuguese) and to each otherdifference of 0.26 mean operations.…”

A cross‐linguistic study of spatial location descriptions in New Zealand English and Brazilian Portuguese natural language

“…Kettunen (2014) compares a number of European languages using different complexity metrics, particularly focusing on morphology, and finds that English (0.05) and EP (0.06) (and several other romance languages) are similar in their type–token ratios, as well as several other measures. Again, this analysis does not include BP, but other work provides type–token ratios for BP that are much lower (0.01) (Estivalet & Meunier, 2015), and given the large disparity (none of the languages included in the Kettunen study had values lower than 0.05), is likely to be explained by a different methodology for determining type–token ratio.…”

“…It is not clear why this might be, and while there has been significant work on the salience of geographic features for wayfinding (e.g., Caduff & Timpf, 2008; Quesnot & Roche, 2015; Raubal & Winter, 2002), we are not aware of any cross‐linguistic studies that address the issue. Vocabulary studies show that the most frequently used word class in English is nouns (Kang & Yu, 2011; Tardif, Gelman, & Xu, 1999), whereas in BP verbs are used most frequently (Estivalet & Meunier, 2015), which might make English speakers more attuned to objects in their environment, but more studies are required to determine the cause of this variation in relata and locata selection frequency.…”

StimulStat: A lexical database for Russian

“…Such databases have been created for several languages and are available in the form of a web application or computer software. Among them are the English lexicon project (Balota et al, 2007), eDom (Armstrong, Tokowicz, & Plaut, 2012), N-Watch (Davis, 2005) and MRC database (Coltheart, 1981) for English; DlexDB for German (Heister et al, 2011); Lexique (New, Pallier, Brysbaert, and Ferrand, 2004) for French; EsPal (Duchon, Perea, Sebastián-Gallés, Martí, & Carreiras, 2013) and BuscaPalabras (Davis & Perea, 2005) for Spanish; EHME (Acha, Laka, Landa, & Salaburu, 2014) and E-Hitz (Perea et al, 2006) for Basque; GreekLex (Ktori, van Heuven, & Pitchford, 2008) and GreekLex2 (Kyparissiadis et al, 2017) for Modern Greek; Aralex (Boudelaa & Marslen-Wilson, 2010) for Modern Standard Arabic; the Malay Lexicon Project (Yap, Liow, Jalil, & Faizal, 2010) for Malay; KelemetriK (Erten, Bozsahin, & Zeyrek, 2014) for Turkish; the Brazilian Portuguese Lexicon (Estivalet & Meunier, 2015) for Brazilian Portuguese, etc. All these databases are equipped with effective search and filtering tools.…”