Expressive oral language development (EOLD) in adolescence is understudied and usuallyinvolves a ... more Expressive oral language development (EOLD) in adolescence is understudied and usuallyinvolves a small set of language parameters. To explore this development in more detail, weused automated language analyses of transcripts of narrative speech (fairy-tale retellings) of257 Portuguese-speaking 9-15-year-olds. We grouped multiple linguistic parameters intoword/lexical, sentence/grammar and inter-sentence/cohesion category levels entered asmultivariate factors in general linear models considering the effects of development, sex andparental schooling. We found substantial EOLD at all levels, with negligible effects (exceptin one level) of sex, and parental schooling (a measure of participants’ socioeconomic status).Age (months), pubertal status and school grade generally showed comparable EOLD effects,suggesting sexual maturation and participants’ grade are not more determinant factors thanage. In summary, automated scoring of linguistic features of transcribed fairy-tale retellings isa sensitive, inexpensive, and ecological way of identifying EOLD in adolescence whichminimizes socioeconomic effects on oral language.
This paper describes the method employed to build a machinereadable pronunciation dictionary for ... more This paper describes the method employed to build a machinereadable pronunciation dictionary for Brazilian Portuguese. The dictionary makes use of a hybrid approach for converting graphemes into phonemes, based on both manual transcription rules and machine learning algorithms. It makes use of a word list compiled from the Portuguese Wikipedia dump. Wikipedia articles were transformed into plain text, tokenized and word types were extracted. A language identification tool was developed to detect loanwords among data. Words’ syllable boundaries and stress were identified. The transcription task was carried out in a two-step process: i) words are submitted to a set of transcription rules, in which predictable graphemes (mostly consonants) are transcribed; ii) a machine learning classifier is used to predict the transcription of the remaining graphemes (mostly vowels). The method was evaluated through 5-fold cross-validation; results show a F1-score of 0.98. The dictionary and all the resources used to build it were made publicly available.
Automated discourse analysis aiming at the diagnosis of language impairing dementias already exis... more Automated discourse analysis aiming at the diagnosis of language impairing dementias already exist for the English language, but no such work had been done for Portuguese. Here, we describe the results of creating a unified environment, entitled Coh-Metrix-Dementia, based on a previous tool to analyze discourse, named Coh-Metrix-Port. After adding 25 new metrics for measuring syntactical complexity, idea density, and text cohesion through latent semantics, Coh-Metrix-Dementia extracts 73 features from narratives of normal aging (CTL), Alzheimer’s Disease (AD), and Mild Cognitive Impairment (MCI) patients. This paper presents initial experiments in automatically diagnosing CTL, AD, and MCI patients from a narrative language test based on sequenced pictures and textual analysis of the resulting transcriptions. In order to train regression and classification models, the large set of features in Coh-Metrix-Dementia must be reduced in size. Three feature selection methods are compared. In our experiments with classification, it was possible to separate CTL, AD, and MCI with 0.817 \(F_1\) score, and separate CTL and MCI with 0.900 \(F_1\) score. As for regression, the best results for MAE were 0.238 and 0.120 for scenarios with three and two classes, respectively.
Expressive oral language development (EOLD) in adolescence is understudied and usuallyinvolves a ... more Expressive oral language development (EOLD) in adolescence is understudied and usuallyinvolves a small set of language parameters. To explore this development in more detail, weused automated language analyses of transcripts of narrative speech (fairy-tale retellings) of257 Portuguese-speaking 9-15-year-olds. We grouped multiple linguistic parameters intoword/lexical, sentence/grammar and inter-sentence/cohesion category levels entered asmultivariate factors in general linear models considering the effects of development, sex andparental schooling. We found substantial EOLD at all levels, with negligible effects (exceptin one level) of sex, and parental schooling (a measure of participants’ socioeconomic status).Age (months), pubertal status and school grade generally showed comparable EOLD effects,suggesting sexual maturation and participants’ grade are not more determinant factors thanage. In summary, automated scoring of linguistic features of transcribed fairy-tale retellings isa sensitive, inexpensive, and ecological way of identifying EOLD in adolescence whichminimizes socioeconomic effects on oral language.
This paper describes the method employed to build a machinereadable pronunciation dictionary for ... more This paper describes the method employed to build a machinereadable pronunciation dictionary for Brazilian Portuguese. The dictionary makes use of a hybrid approach for converting graphemes into phonemes, based on both manual transcription rules and machine learning algorithms. It makes use of a word list compiled from the Portuguese Wikipedia dump. Wikipedia articles were transformed into plain text, tokenized and word types were extracted. A language identification tool was developed to detect loanwords among data. Words’ syllable boundaries and stress were identified. The transcription task was carried out in a two-step process: i) words are submitted to a set of transcription rules, in which predictable graphemes (mostly consonants) are transcribed; ii) a machine learning classifier is used to predict the transcription of the remaining graphemes (mostly vowels). The method was evaluated through 5-fold cross-validation; results show a F1-score of 0.98. The dictionary and all the resources used to build it were made publicly available.
Automated discourse analysis aiming at the diagnosis of language impairing dementias already exis... more Automated discourse analysis aiming at the diagnosis of language impairing dementias already exist for the English language, but no such work had been done for Portuguese. Here, we describe the results of creating a unified environment, entitled Coh-Metrix-Dementia, based on a previous tool to analyze discourse, named Coh-Metrix-Port. After adding 25 new metrics for measuring syntactical complexity, idea density, and text cohesion through latent semantics, Coh-Metrix-Dementia extracts 73 features from narratives of normal aging (CTL), Alzheimer’s Disease (AD), and Mild Cognitive Impairment (MCI) patients. This paper presents initial experiments in automatically diagnosing CTL, AD, and MCI patients from a narrative language test based on sequenced pictures and textual analysis of the resulting transcriptions. In order to train regression and classification models, the large set of features in Coh-Metrix-Dementia must be reduced in size. Three feature selection methods are compared. In our experiments with classification, it was possible to separate CTL, AD, and MCI with 0.817 \(F_1\) score, and separate CTL and MCI with 0.900 \(F_1\) score. As for regression, the best results for MAE were 0.238 and 0.120 for scenarios with three and two classes, respectively.
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