# Syllable-, Bigram-, and Morphology-Driven Pseudoword Generation in Greek
## 3 Methods
SyBig-r-Morph's pseudoword generation process begins by letting users specify the morphosyntactic category of the pseudowords they want, such as nouns, verbs, adjectives, adverbs, or prepositions, as shown in Figure 2. Users can even select all categories if they wish. Additionally, users can opt to apply a frequency filter, using metrics such as _Zipf frequency_, which is included in GreekLex2.1. Operating within a range of 1.327 to 7.533 (based on GreekLex2.1 items), this parameter regulates the proximity of generated words to low- and high-frequency words. Lower values indicate low Zipf frequency, while higher values indicate high Zipf frequency. Figure 1: Loading the lexical resources. In the present version of SyBig-r-Morph, we have set the default Zipf frequency value to 5 because we would like our hypothetical experimental materials to have an equal distance between existing low and high-frequency words. Raising this threshold entails fewer words passing the filter, resulting in a smaller pool of syllables available for word generation. Our testing showed that a high Zipf frequency setting resulted in a smaller set of word-final syllables. This led to the exclusion of some less productive inflectional classes. This feature is optional; users who do not wish to control the pseudoword frequency of existing words can choose not to apply the frequency filter. Once frequency parameters are established (or bypassed), users can then define the structural characteristics of their pseudowords by specifying the desired word size and number for the generated pseudowords. In its current settings, the application can create up to 1000 pseudowords. Our application carefully filters the input lexicon and creates a dictionary by keeping only those words that match the specified syllable count. An innovation of SyBig-r-Morph is that it includes a syllabification component that breaks words into their respective syllables (sequences of onset(s) - nucleus - coda(s)). The syllabification stage is crucial because each word from the filtered list is deconstructed into constituent syllables. These syllables are then categorized based on their position in the word, such as first, second, third, etc., thus creating a foundation for constructing plausible pseudowords. Table 1 illustrates a compilation of syllables per position for trisyllabic words:  
The syllable compilation shown in Table 1 reveals an important design feature of SyBig-r-Morph: all syllables are delivered without stress diacritics. This reflects the application's systematic approach to stress handling. As a direct result, all words generated by SyBig-r-Morph are stressless. While this feature works well for studies like our hypothetical scenario that investigates speakers' stress assignment patterns, it may pose challenges for other research that requires stressed outputs. To address this need, we have incorporated an option for assigning stress to the generated pseudowords. This feature will be discussed in more detail below. Figure 2: Pseudoword generation interface. Interface for configuring pseudoword generation parameters, including part of speech selection (Preposition shown selected), syllable count (3), frequency threshold (5.0), and similarity threshold (0.8). Following the creation of the syllable inventory, SyBig-r-Morph systematically explores all potential syllable combinations to generate pseudoword candidates. However, not all theoretically possible combinations yield linguistically valid or experimentally useful pseudowords. To ensure the quality and appropriateness of generated items, all candidates must undergo rigorous evaluation through the application's second major component: the evaluation system. The first evaluation phase is _criteria-based filtering,_ which is carried out in several steps. This evaluation is designed to work with any available database, wordlist, or corpus. The current version of SyBig-r-Morph uses both GreekLex2.1 and the Clean Corpus [41]. They are collectively called the \"Lexicon\" in the remainder of the article.