## 4 Results
### Comparison of the Performance of the Methods  
Table 3 presents a comparative analysis of the performance of the neuronal dependency parser SpaCy, the constituent parser from Stanford, GPT-4, its predecessor GPT-3.5, and our bubble parser, based on the number of fully correct answers. For the LLMs, an answer is considered correct only if it is consistently accurate across all 20 requests. In the case of the neural dependency parser, which is limited to determining word classes, we count an answer as correct if all word classes in a sentence are accurately identified. The same applies to the Stanford parser with the additional possibility to identify clauses. For the bubble parser, an interpretation is deemed absolutely correct if it results in a single, unambiguous interpretation. In the first task, determining word classes, both GPT-4 and the (rule-based) bubble parser deliver near-perfect results. Sentence 1 presents a unique challenge due to the presence of a verb that is syntactically identical to a common article in German. GPT-4 correctly identifies the word as a verb, resisting the statistically more frequent interpretation of it being an article. In contrast, GPT-3.5 is misled by this unusual word class and demonstrates further weaknesses in classifying words in sentences 2, 3, and 5. In sentences 3 and 5, GPT-4 occasionally misclassifies relative pronouns as articles, while the bubble parser consistently produces the correct classification. Misinterpreting these pronouns as articles would result in a contradictory sentence structure, leading to the elimination of the corresponding interpretation by the bubble parser. Sentence 6 presents multiple concurrent interpretations for word classes, which the bubble parser is unable to fully resolve. However, these ambiguities do not disrupt the overall sentence structure, making them more challenging for the parser to detect and eliminate. LLMs like GPT-4 use correlations between word pairs in the input to increase the likelihood of correctly interpreting unusual word classes based on the surrounding context. On the other hand, the bubble parser identifies rare word classes by consulting a lexicon (Morphy) that lists all potential word classes and analyzes each possibility systematically. In comparison, the dependency parser SpaCy, which is capable of addressing only the first grammatical question, performs accurately across all sentences except sentence 1, where it fails to identify the atypical word class. The Stanford parser, which also relies on probabilistic likelihoods, not only misclassifies the unusual word class in sentence 1 but also confuses pronouns and articles in several cases. This reliance on selecting the most likely word class undermines performance, as both parsers struggle to accurately process sentences containing less frequent or unexpected linguistic elements. The second task, the identification of clause types, is handled flawlessly by the bubble parser. A false clause linking, which immediately destroys the sentence structure, is excluded by the bubble parser in all ten sentences. In contrast, GPT-4, which focuses on word-pair relationships, misidentifies clause types in five out of ten sentences. GPT-4 does not evaluate whether the identified clauses and their types form a coherent interpretation when considering the sentence as a whole. Despite these errors, GPT-4 still outperforms its predecessor, GPT-3.5, which fails to 100% correctly identify the clause types in any of the ten sentences. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & **SpaCy** & **Stanford** & **GPT-3.5** & **GPT-4** & **Bubble Parser** \\ \hline word classes & 9 & 5 & 6 & 8 & 9 \\ clause types & N/A & 6 & 0 & 5 & 10 \\ noun cases & N/A & N/A & 0 & 1 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of total correct answers to ten sentences (abbreviations: N/A = not applicable).