## 5 Discussion
In contrast, our parser directly identifies grammatical relations between words without relying on the distance between words. This integration of rule-based parsing with LLM can improve accuracy and reliability by combining the strengths of both approaches. The presented bubble parser was originally developed to convert standard written German into \"Easy Language\" by transforming the recognized grammatical structure of the sentences, but its potential applications extends to various areas within natural language processing (NLP). For instance, parsing millions of German sentences from the \"Wortschatz Leipzig\" corpus facilitates the extraction of relationships between entities within sentences, contributing to the well-established field of relation extraction in NLP [35]. Our parser offers a precise solution by resolving ambiguities prior to extraction. Additionally, when parsing different corpora of human- and machine-generated texts, the parser can create a grammatical \"fingerprint\", identifying any distinctive features or differences in the grammar used, which can be potentially useful for distinguishing the authority of the written text. The new linguistic theory presented in the work of [PERSON] et al. [13] is demonstrated in this paper for German sentences. However, it can be easily adapted to any natural language, such as French or English, keeping in view the peculiarities of certain language. For example, in German, our syntactic analyzer assumes that the substantive is the final element in a collocation, whereas in French, adjectives usually follow the substantive. Such language variations must be accounted for by constructing a syntactic analyzer for another language based on the theory presented. An important point of discussion in language analysis is the natural temporal change of any living language. While LLM-based applications require a constant training phase, our parser, which is based on the standard Morphy grammar lexicon [19], does not need to be adapted, as all changes in the language are constantly incorporated into the lexicon. One of the limitations of the current study is the small number of test sentences. However, since our bubble parser is not designed to be statistical, we do not need to demonstrate a large number of tests but rather a few typical cases. Reasonably, we show only ten example sentences to be able to go into the grammatical details of these sentences by analyzing, for example, every noun phrase of all sentences. In fact, the parser addresses the German language as a whole and is able, in its current development stage, to parse roughly 50% of all sentences found in the \"Wortschatz Leipzig\" [21], which contains millions of sentences taken from Wikipedia or news articles. Despite the demonstrated progress, the proposed parser still suffers from the presence of false assumptions, resulting in a 50% parsing failure rate of sentences. This is the current limitation of the method. For example, the parser assumes that verbs stand at the end of a sub-clause or in the second position in a main clause. This assumption seems reasonable, but in some sentences, it may be violated because a prepositional phrase can be added behind these verbs. These systematic false assumptions can be corrected without changing the overall structure of the parser since the parser solves all problems at the level of word classes rather than for individual words. As for the combinatorics of these word classes, it turns out that only a small fraction of them constitutes a grammatically correct sentence.