05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Tailoring language for user backgrounds : human and LLM evaluations of paraphrased texts(2025) Carlon, FrancescaIn an increasingly interdisciplinary world, the ability to tailor technical and field-specific language to diverse audiences has become essential. Both academic research and industry benefit from the knowledge and expertise of individuals from varied technical backgrounds, supporting progress in research and product development. Therefore, it has become essential to make domain-specific concepts accessible and understandable to different audiences. This study investigates how Large Language Models (LLMs) can generate effective paraphrases by tailoring language to specific target audiences. It also explores which linguistic elements enhance conceptual understanding and whether LLMs can produce high-quality synthetic data for evaluating text quality. This research specifically focuses on tailoring language from Linguistics, Computer Science, and Computational Linguistics texts for audiences with diverse academic backgrounds, using a variety of LLMs. Methods employed include Prompt Engineering, linguistic metrics, and evaluations conducted by both human judges and LLM-as-a-judge approaches. The experiments focus on assessing prompt quality, examining the relationship between human evaluations and linguistic features, and analyzing the correlation between human judgments and LLM-as-a-judge assessments. The results demonstrate that combining Role Prompting with Chain-of-Thought (CoT) prompting effectively produces high-quality paraphrased texts. Additionally, syntactic and lexical features play a central role in enhancing text accessibility and users’ understanding of unfamiliar concepts. Finally, while the correlation between LLM-as-a-judge evaluations and human judgments is promising, it varies across different user groups. Therefore, this research demonstrates the potential of Prompt Engineering techniques for paraphrasing and tailoring language to users with different backgrounds, explores the linguistic features that enhance text comprehension, and examines the extent to which LLM-as-a-judge correlates with human preferences and can support the automatic retrieval of high-quality data.Item Open Access Evaluating methods of improving the distribution of data across users in a corpus of tweets(2023) Milovanovic, MilanCorpora created from social network data often serve as the data source for tasks in natural language processing. Compared to other, more standardized corpora, social media corpora have idiosyncratic properties due to the fact that they consist of user-generated comments. These are, for example, the unbalanced distribution of the respective comments, a generally lower linguistic quality, and an inherently unstructured and noisy nature. Using a Twitter-generated corpus, I will investigate to what extent the unbalanced distribution of the data has an influence on two downstream tasks, relying on word embeddings. Word embeddings are a ubiquitous and frequently used concept in the field of natural language processing. The most common models are often the means to obtain semantic information about words and their usage by representing the words in an abstract word vector space. The basic idea is that semantically similar words in the mapped vector space have similar vectors. In doing so, these vectors serve as input for standard downstream tasks such as word similarity and semantic change detection. One of the most common models in current research is the use of word2vec, and more specifically, the Skip-gram architecture of this model. The Skip-gram architecture attempts to predict the surrounding words based on the current word. The data on which this architecture is trained greatly influences the resulting word vectors. In the context of this work, however, no significant improvement in the results to a fully preprocessed corpus could be found when filtering methods, widely used in the literature, without specific motivation, are used to select a subset of data according to defined criteria, neither for word similarity nor for semantic change detection. However, comparable results could be achieved with some filters, although the resulting models were trained using significantly fewer tokens as input.Item Open Access Kategorisierung der Zustandsveränderungen bei CoS-Verben auf Basis von Bild- und/oder Textdaten(2023) Godbersen, JuleSowohl textliche als auch visuelle Informationen können für das Verständnis einer Aktion relevant sein. In dieser Bachelorarbeit werden Aktionen betrachtet, die zu einer Veränderung im Zustand des beteiligten Objekts führen. Ziel dabei ist die Beantwortung der Forschungsfrage, welchen Beitrag die Modalitäten bei der Vorhersage von solchen Zustandsveränderungen haben. Die Vorhersage erfolgt mithilfe von Kategorien wie beispielsweise Farbe, Größe und Quantität. Ein wesentlicher Bestandteil dieser Bachelorarbeit ist die Erstellung eines Datensatzes, der Beispielvorkommen von Aktionen mit Zustandsveränderungen enthält. Eine weitere Aufgabe besteht darin, einiger dieser Datenpunkte mit Kategorien von Zustandsveränderungen annotieren zu lassen. Darüber hinaus wird ausgehend von einem visiolinguistischen Modell eine Ablationsstudie durchgeführt. Diese erlaubt mithilfe verschiedener Klassifikatoren, den Einfluss der verschiedenen Modalitäten auf die Leistungsfähigkeit eines Modells im Hinblick auf die Vorhersage von Zustandsveränderungen zu testen. Diese Bachelorarbeit veranschaulicht unter anderem Schwierigkeiten im Rahmen der Annotationen. Die Leistungsfähigkeit bezüglich der Vorhersage von Kategorien, gemessen mit der Akkuratheit, ist bei den Klassifikatoren ähnlich hoch wie bei einem Baseline Modell. Die verschiedenen Klassifikatoren treffen Vorhersagen mit ähnlicher Akkuratheit, sodass die Forschungsfrage mit den Ergebnissen dieser Bachelorarbeit nicht hinreichend beantwortet werden kann. Die Hypothese, dass die Kombination aus textlicher und visueller Modalität komplementäre Informationen liefert und dementsprechend die Kombination beider Modalitäten relevant ist, wird durch die Ergebnisse nicht bestätigt. Ergänzend wird durch diese Bachelorarbeit gezeigt, dass die trainierten Klassifikatoren es ermöglichen, in gewissem Maße auf ungesehene Datenpunkte, ungesehene Verben und ungesehene Domänen zu generalisieren.Item Open Access Cross-lingual word embeddings with multi-sense representations(2024) Shim, Soh-EunCross-lingual word embeddings have been found to be useful in aiding cross-lingual transfer, but work in this line of research has to date rarely addressed the monosemy constraint of static word embeddings in depth, where the collapse of multiple meanings into one form might arguably lead to subpar alignments. In this thesis, we address this gap by examining potential approaches towards the incorporation of sense information into cross-lingual alignment. We explore in specfic two variants of cross-lingual multi-sense alignment: one in which we employ the method of embedding the senses of each word as a Gaussian mixture (Athiwaratkun and Wilson, 2017), where the assumption is that multi-sense embeddings as a basis for alignment may help mitigate the meaning conflation deficiency (Camacho-Collados and Pilehvar, 2018), and in turn help improve isomorphism between vector spaces (Ruder et al., 2019). Our second method explores learning a cross-lingual multi-sense embedding space by reversing the order: we cross-lingually align uni-sense word embeddings, and attempt multi-sense enrichment as a postprocessing step by retrofitting (Pilehvar and Collier, 2016) the embedding on the Open Multilingual Wordnet (Bond et al., 2023). We observe that our model is capable of fine-grained cross-lingual semantic distinctions, where our model successfully identifies colexifications without cross-lingual supervision.Item Open Access An attribution method for classification tasks in Siamese models(2024) Liu, MindongExplaining the contribution of tokens on classification results in the classification task of two sentences is a challenging problem in natural language processing (NLP). This thesis studies the use of the Integrated Jacobians (IJ) in interpreting multi-class classification models with Siamese models, particularly its application in Natural Language Inference (NLI). The NLI task requires models to understand the logical relationships between two sentences, posing challenges for model interpretability. To address the fact that the original Siamese model was primarily designed for regression tasks, the thesis first expanded Siamese models for classification tasks with bilinear similarity while ensuring that the IJ methods can be utilized. It then adapts two forms of the IJ methods: exact IJ and approximate IJ, to work with newly extended Siamese models. To validate the effectiveness of the extended Siamese models using the IJ meth ods, the thesis conducted experiments on the AllNLI dataset under sentence-BERT framework. The thesis employed four different model configurations and applied both IJ methods to these models. The experimental results demonstrate that the IJ methods effectively provide explanations for us. Finally, the thesis examined the consistency between the explanations provided by the IJ methods and semantic relationships at the lexical and span levels using datasets WordNet and SpanEX. In the analysis, the IJ methods show that the models capture semantic relationships between words and spans, and there is a correlation between these relationships and the model’s predictions. This finding supports the use of the IJ methods to explain the decisions of NLP models.Item Open Access Editorial - perspectives for natural language processing between AI, linguistics and cognitive science(2022) Lenci, Alessandro; Padó, SebastianItem Open Access Analysis of political positioning from politician’s tweets(2023) Maurer, Maximilian MartinSocial media platforms such as Twitter have become important communication channels for politicians to interact with the electorate and communicate their stances on policy issues. In contrast to party manifestos, which lay out curated, compromised positions, the full range of positions within the ideological bounds of a party can be found on social media. This begs the question of how aligned the ideological positions of parties on social media are with their respective manifesto. To assess the alignment of social media and manifesto positions, we correlate the positions automatically retrieved from the tweets with manifesto-based positions for the German federal elections of 2017 and 2021. Additionally, we assess whether the change in positions over time is aligned between social media and manifestos. We retrieve ideological positions by aggregating distances between parties from sentence representations of their members' tweets from a corpus containing >2M individual tweets of 421 German politicians. We leverage domain-specific information by training a sentence embedding model such that representations of tweets with co-occurring hashtags are closer to each other than ones without co-occurring hashtags, following the assumption that hashtags approximate policy-related topics. Our experiments compare this political social media domain-specific model with other political domain and general domain sentence embedding models. We find high, significant correlations between the Twitter-retrieved positions and manifesto positions, especially for our domain-specific fine-tuned model. Moreover, for this model, we find overlaps in terms of how the positions change over time. These results indicate that the ideological positions of parties on Twitter correspond to the ideological positions as laid out in the manifestos to a large extent.Item Open Access A computational stylistics of poetry : distant reading and modeling of German and English verse(2023) Haider, Thomas; Kuhn, Jonas (Prof. Dr.)This doctoral thesis is about the computational modeling of stylistic variation in poetry. As ‘a computational stylistics’ it examines the forms, social embedding, and the aesthetic potential of literary texts by means of computational and statistical methods, ranging from simple counting over information theoretic measures to neural network models, including experiments with representation learning, transfer learning, and multi-task learning. We built small corpora to manually annotate a number of phenomena that are relevant for poetry, such as meter, rhythm, rhyme, and also emotions and aesthetic judgements that are elicited in the reader. A strict annotation workflow allows us to better understand these phenomena, from how to conceptualize them and which problems arise when trying to annotate them on a larger scale. Furthermore, we built large corpora to discover patterns in a wide historical, aesthetic and linguistic range, with a focus on German and English writing, encompassing public domain texts from the late 16th century up into the early 20th century. These corpora are published with metadata and reliable automatic annotation of part-of-speech tags, syllable boundaries, meter and verse measures. This thesis contains chapters on diachronic variation, aesthetic emotions, and modeling prosody, including experiments that also investigate the interaction between them. We look at how the diction of poets in different languages changed over time, which topics and metaphors were and became popular, both as a reaction to aesthetic considerations and also the political climate of the time. We investigate which emotions are elicited in readers when they read poetry, how that relates to aesthetic judgements, how we can annotate such emotions, and then train models to learn them. Also, we present experiments on how to annotate prosodic devices on a large scale, how well we can train computational models to predict the prosody from text, and how informative those devices are for each other.Item Open Access Bridging behavioral gaps : automatic extrapolation of concreteness norms in Arabic using English-tuned KNN approaches(2025) Aziz, MarinaThis thesis addresses the automatic extrapolation of concreteness norms for nouns in both Modern Standard Arabic and English. The main goal is to enable reliable estimation of how concrete or abstract words are (e.g. “apple” vs. “justice”) using computational methods, supporting applications in psycholinguistics and natural language processing. To this end, a novel dataset of 202 Arabic nouns rated for concreteness is introduced and aligned with established English norms. To predict concreteness, the study compares a K-Nearest Neighbors (KNN) regression model based on FastText and transformer-based embeddings with predictions from Chat GPT. The KNN models achieve high accuracy, Spearman ρ = 0.92 (RMSE = 0.43) for English and ρ = 0.83 (RMSE = 0.69) for Arabic on held-out test sets. By contrast, ChatGPT predictions, while consistent across runs, yield lower correlations (ρ = 0.80 for both English and Arabic) and higher RMSE values, confirming that KNN remains more accurate for concreteness estimation. Keywords: concreteness norms; abstractness; Arabic; English; K-Nearest Neighbors; word embeddings; FastText; transformer models; ChatGPT; lexical semantics; psycholinguistics; norm extrapolationItem Open Access Melodies of the majority language : investigating prosodic phenomena in majority English(2025) Quraishi, TanishqThis thesis is a comparative study of majority and monolingual English speaker groups on three key prosodic phenomena, namely pitch accents, boundary tones and intonational phrases. To that end, the data employed is extracted from a multilingual corpus consisting of comparable speaker data with the necessary annotations. The nature of the dataset is on of semi-spontaneous narratives elicited in a semi-naturalistic manner. Three binomial generalized linear mixed effects models and one linear mixed effect model encode fixed and random effects in order to analyse the influence and interaction of various factors such as bilingualism, gender and formality on linguistic intonation. The statistical analysis performed on semi-spontaneous speech, reveals a significant influence of formality on higher monotonal pitch accent usage. The influence of gender is significant on the likelihood of a speaker using a high boundary tone in their utterance. The role of bilingualism and formality influence pitch accent placements on content words, and lastly formality impacts the length of an intonational phrase, where formal contexts consist of shorter phrases.