## 5 Discussion  
In the present study, we present a new software that analyzes and simplifies everyday German texts according to the \"Easy Language\" standard. The software implements a rule-based algorithm, which parses given sentences and decomposes them into their grammatical components. Additionally, a user can enter arbitrary sentences, and the software shows several grammatical characteristics, such as word classes, main- and sub-clauses, or the cases of the nouns, which can be useful for language learners. The parser provides a rule-based analysis of the given sentence and builds several interpretations for every grammatical ambiguity found in the sentence. According to [PERSON] et al. [27], linguistic ambiguity has always been one of the main challenges in natural language processing. Most of these ambiguities, whether lexical, syntactic, or semantic, can be solved using the approach presented in our previous work [13], where we introduced a novel rule-based algorithm that overcomes the problem of ambiguity in language. The method builds a search space of all ambiguity combinations and then sorts out the incorrect interpretations of a sentence in order to obtain the remaining correct interpretation at the end. The correct interpretation is always present among the proposed interpretations, which is inherent in our approach because the software analyzes the whole search space of all ambiguity combinations. To control the explosion in the number of interpretations and prevent the formation of incomputable many interpretations, several contradiction tests are carried out after each parsing step. The parsing steps handle ambiguities one after the other and reduce the number of false interpretations. In the case where the software is not able to exclude all false interpretations and ends up with more than one proposed interpretation, the user can review the remaining interpretations to see which ambiguities were not resolved. Our approach combines word groups, which grammatically belong together, to a single new linguistic object, which occurs in the sentence at the same position as the former word group. In this way, the new linguistic object possesses an internal and external logic. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parser** & **Human Generated Data** & **Training** & **Inference/Parsing Cost p. Sentence** \\ \hline Bubble Parser & no data & no training & 0.1–1.0 s single core CPU \\ Stanford parser & TuBa-D/Z (25,000 ann. sent.) & 3–7 days GPU & 0.1–1.0 s single core CPU \\ SpaCy & UD Treebank (15,000 ann. sent.) & 3–7 days GPU & 0.01–0.3 s single core CPU \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the computational effort (abbreviations: ann. sent. = annotated sentences). The inner logic describes the cohesion of the elements of this new linguistic object and the outer logic describes its function in the regarded sentence. For example, the internal logic of a noun phrase implies that all elements must have the same grammatical case. The external logic describes how this noun phrase interacts with other noun phrases and verbs in the sentence. For example, if the considered noun phrase has permissible nominative and accusative cases but the nominative case is already occupied by another noun phrase, it automatically obtains the accusative case since an interpretation with two nominative cases must be excluded. In the present study, we compare our parser with two concurrent parsers, the dependency parser SpaCy and the constituent parser from Stanford, by formulating three grammar tasks using ten identical example sentences for all parsers. Ten sentences are selected specifically to cover as many different grammatical rules as possible. The parsers are requested to determine word classes, main- and sub-clauses, and the cases of the nouns. The proposed bubble parser provides more detailed grammatical information compared to the state-of-the-art dependency parser SpaCy and Stanford parser.