# Syllable-, Bigram-, and Morphology-Driven Pseudoword Generation in Greek
## 3 Methods  
Our research focuses on constructing pseudonouns due to their diverse accentual patterns, which set them apart from other grammatical categories. We aim to build pseudonouns from various inflectional classes to be used in experimental tasks that explore how young and adult Greek speakers apply stress rules. Specifically, we strive to generate pseudowords from a specific lexicon, ensuring they are morphophonologically correct and distinct from existing words. Currently, specialized software tools for the large-scale generation of Greek pseudowords are lacking, and, as discussed above, existing tools present significant challenges when applied to Greek. To address this gap, we introduce SyBig-r-Morph. The Flask-based web application (v2.3.3) requires Python 3.7+ for implementation and integrates two primary lexical resources: ILSP's _Clean Corpus_ [41; 44] and _GreekLex2.1_ [45; 46]. GreekLex2.1 offers detailed insights into Greek word forms and linguistic patterns by providing part-of-speech information, syllabification, phonetic and orthographic details for each entry, and metrics for word similarity. It was developed using entries from the _Dictionary of Standard Modern Greek_ [47]. The database is also enhanced with a set of statistical metrics for these entries from the _Hellenic National Corpus_ (HNC), an extensive compilation (over 97 million words) of written materials, including newspapers, books, periodicals, and other textual sources, developed by the _Institute for Language and Speech Processing/R.C. Athena_ (ILSP) [48; 49]. GreekLex2.1 encompasses around 35,000 lemmas found in both the dictionary and the HNC. The Clean Corpus is an extensive collection of 217,664 word types and 29.6 million tokens from the HNC journalistic, legal, and literary texts. It provides essential information for each word, including syllabic structure, word length, stress pattern, and frequency. Moreover, it offers useful statistical information on metrics like average letters, phonemes, syllable frequencies, and counts of orthographic and phonological neighbors. Unfortunately, Clean lacks data on the morphological characteristics of words, which is particularly significant for studies focused on specific grammatical categories like nouns. In SyBigr-Morph, we use the \"Ignoring Stress\" version, which provides the relevant data, including complete tables of syllables and their associated type and token frequencies, without taking stress diacritics into account. To ensure optimal performance and access to the necessary linguistic data, both lexical resources are loaded during application startup. This initialization process, illustrated in Figure 1, involves loading the lexical data and accompanying information from Greek-Lex2.1, along with the Clean Corpus. Due to the substantial size of the Clean Corpus (217,664 word types), the loading process typically completes in under a minute, after which users can begin pseudoword generation with full access to the capabilities of both databases (Figure 1). Building on these lexical resources, the application's technical workflow consists of two main components, that is, a generation component and an evaluation component. The generation component includes lexicon analysis, syllable segmentation, and the strategic reassembly of syllables based on their original positions within words. On the other hand, the evaluation component applies similarity filtering to ensure that the generated pseudowords are sufficiently distinct from existing Greek lexical items. At the core of our application lies GreekLex2.1, which provides the words upon which the syllable decomposition mechanism will apply to create the syllable arrays for the pseudoword generation, along with essential morphosyntactic information and metrics for word frequency. However, any corpus, lexical database, or wordlist containing information on syllabic structure, word length, part of speech, and frequency can serve for this purpose.