PhonItalia: a phonological lexicon for Italian

Goslin, Jeremy; Galluzzi, Claudia; Romani, Cristina

doi:10.3758/s13428-013-0400-8

Cited by 63 publications

(39 citation statements)

References 26 publications

(23 reference statements)

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“…This identified 293 admissible responses, with 32 pictures having more than one admissible response, and 1 picture having no admissible responses. We then removed responses for words that did not appear in the PhonItalia database (Goslin et al, 2014). These included multiple words (e.g., pupazzo di nevesnowman) and words borrowed from other languages (e.g., clown), and lead to the exclusion of an additional 25 (8.53%) responses.…”

Section: Response Scoringmentioning

confidence: 99%

“…The values of Levenshtein distance considered here are the mean of the 20 smallest distances found (Goslin et al, 2014; see also Yarkoni, Balota, & Yap, 2008). This measure provides a subtle measure of orthographic similarities.…”

Section: Orthographic/phonological Word-form Level Variablesmentioning

confidence: 99%

“…Each variable is described below. Values for each variable were drawn from the PhonItalia Database 1.10 (Goslin, Galluzzi, & Romani, 2014) except where noted.…”

Section: Predictor Variablesmentioning

confidence: 99%

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Typing pictures: Linguistic processing cascades into finger movements

et al. 2016

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Highlights1. We investigated response onset and response execution in typing 2. Lexical-semantic variables influenced response onset and interkeystroke intervals 3. Orthographic variables were found to affect only response execution 4. Results seem coherent with cascaded flow of information between linguistic and motor processes Running Head: TYPING PICTURES 3 AbstractThe present study investigated the effect of psycholinguistic variables on measures of response latency and mean interkeystroke interval in a typewritten picture naming task, with the aim to outline the functional organization of the stages of cognitive processing and response execution associated with typewritten word production. Onset latencies were modulated by lexical and semantic variables traditionally linked to lexical retrieval, such as word frequency, age of acquisition, and naming agreement. Orthographic variables, both at the lexical and sublexical level, appear to influence just within-word interkeystroke intervals, suggesting that orthographic information may play a relevant role in controlling actual response execution. Lexical-semantic variables also influenced speed of execution. This points towards cascaded flow of activation between stages of lexical access and response execution.

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Section: Response Scoringmentioning

confidence: 99%

Section: Orthographic/phonological Word-form Level Variablesmentioning

confidence: 99%

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Typing pictures: Linguistic processing cascades into finger movements

et al. 2016

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“…The basis for the first (adult) part of Q2Stress is phonItalia (Goslin, Galluzzi, & Romani, 2014), an open access lexical database providing orthographic and phonological information for 120,000 word forms extracted from CoLFIS (Bertinetto et al, 2005;Laudanna et al, 1995). The choice of phonItalia instead of CoLFIS was motivated by the fact that the former, but not the latter, contains information about the stress pattern of word forms.…”

Section: Methodsmentioning

confidence: 99%

Q2Stress: A database for multiple cues to stress assignment in Italian

2016

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In languages where the position of lexical stress within a word is not predictable from print, readers rely on distributional information extracted from the lexicon in order to assign stress. Lexical databases are thus especially important for researchers willing to address stress assignment in those languages. Here we present Q2Stress, a new database aimed to fill the lack of such a resource for Italian. Q2Stress includes multiple cues readers may use in assigning stress, such as type and token frequency of stress patterns as well as their distribution with respect to number of syllables, grammatical category, word beginnings, word endings, and consonant-vowel structures. Furthermore, for the first time, data for both adults and children are available. Q2Stress may help researchers to answer empirical as well as theoretical questions about stress assignment and stress-related issues, and more in general, to explore the orthography-tophonology relation in reading. Q2Stress is designed as a user-friendly resource, as it comes with scripts allowing researchers to explore and select their own stimuli according to several criteria as well as summary tables for overall data analysis.

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“…An exception is represented by PhonItalia [8], an Italian lexicon designed for researchers working in the psycholinguistic domain. Besides the orthographic forms and their lemmas, PhonItalia also reports the phonological encoding of words with the stress placement.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method for Stress Prediction Applied to GLAFF-IT, a Large-Scale Italian Lexicon

Calderone

Pascoli

Sajous

et al. 2017

Lecture Notes in Computer Science

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International audienceThis paper presents a hybrid method for automatic stress prediction that we apply to GLAFF-IT, a large-scale Italian lexicon we extracted from GLAW-IT, a Machine-Readable Dictionary grounded on Wikizionario. Our approach combines heuristic rules and a logistic model trained on the words' sets of phono-logical features. This model reaches a 98.1% accuracy. The resulting resource is a large lexicon for the Italian language that we release under a free licence. It includes morphological and phonological information for each of its 457, 702 entries. As of today, it is the only Italian lexicon featuring both large coverage and indication of stress position

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PhonItalia: a phonological lexicon for Italian

Cited by 63 publications

References 26 publications

Typing pictures: Linguistic processing cascades into finger movements

Typing pictures: Linguistic processing cascades into finger movements

Q2Stress: A database for multiple cues to stress assignment in Italian

Hybrid Method for Stress Prediction Applied to GLAFF-IT, a Large-Scale Italian Lexicon

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