# Syllable-, Bigram-, and Morphology-Driven Pseudoword Generation in Greek
## 4 Evaluation of Pseudowords  
We added several steps for phonological evaluation to assess further the suitability of the pseudonouns generated by SyBig-r-Morph for our experimental purposes. These steps aim to ensure that our constructed words not only comply with the syllabic and phonotactic rules of Greek but also meet the specific objectives of our experiment. These objectives include creating pseudowords that neither closely resemble native words nor are too distant from them. To assess the phonological well-formedness of our pseudonouns, we harnessed the power of the online _Num Tool_[41; 51] and a manually annotated subset of the Clean Corpus compiled by [PERSON] [20], called the _Annotated Clean Corpus_ (A-Clean), which encompasses 71,105 words. Figure 6: SyBig-r-Morph’s pseudoword generation pipeline. The Num Tool operates online and provides data equivalent to that found in the Clean Corpus for any word submitted to the platform. It furnishes quantitative metrics for each entered letter string, permitting 20 submissions per session. Users can adapt their preferred metrics by selecting the corresponding checkboxes. The results are presented in a tab-separated format, accessible within the user's web browser or downloadable with software that manages text columns, such as MS Excel. The range of available metrics encompasses length (letters, phones, syllables), frequency, uniqueness/recognition point, cumulative syllable and bigram frequency, count and frequency of orthographic and phonological neighbors, cohort, stress, and Levenshtein distance neighbors, and indices of orthographic transparency. Moreover, the Num Tool helps evaluate pseudowords based on statistical measurements of real words documented within the Clean Corpus. Specific lexical and sub-lexical parameters can be pivotal for developing Greek pseudowords within this toolkit. For our study, we selected the following ones:  
1. _Log-mean bigram token frequency (phonemes only)_ (BGtokfreqPho) calculates the logarithmic mean of the frequency of appearance of neighboring phonemes across all samples in the corpus. For example, the combination of the phonemes \(\tau\) [t] + \(o\) [o] results in \(\tau o\) [to] with a log-mean frequency of 3.976 per million samples. 2. _Log-mean bigram type frequency (phonemes only)_ (BGtypfreqPho) computes the logarithmic mean of neighboring phoneme frequency within the entire set of word types in the corpus. For example, the combination of the phonemes \(\tau\) [t] + \(o\) [o] = \(\tau o\) [to] has a log-mean frequency of 1.709 per million types. 3. _N phonological neighbors (standard: replace only)_ (nNeiPho) provides the count of words that emerge when a phoneme is substituted within a word. For instance, the word \(\tau ovo_{\zeta}\) ['tonos]'stress' has phonological neighbors created through substitution: \(\tau ovo_{\zeta}\) ['tonos] \(\to\)\(\varphi ovo_{\zeta}\) ['tonos]'murder' (\(\tau\) //\(\to\) \(\varphi\) /\(\ell\)/) and \(\tau ovo_{\zeta}\) ['tonos] \(\to\)\(\tau ovo_{\zeta}\) ['tosos] 'interest' (\(\
u\) /n/\(\to\) \(\kappa\)/\(\kappa\)/). 4. _N phonological neighbors (replace, delete, insert, transpose)_ (nNeiRDITPho) offers the count of words resulting from phoneme replacement, deletion, insertion, or transposition within a word.