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 Automatic term extraction for conventional and extended term definitions across domains(2020) Hätty, Anna; Schulte im Walde, Sabine (apl. Prof. Dr.)A terminology is the entirety of concepts which constitute the vocabulary of a domain or subject field. Automatically identifying various linguistic forms of terms in domain-specific corpora is an important basis for further natural language processing tasks, such as ontology creation or, in general, domain knowledge acquisition. As a short overview for terms and domains, expressions like 'hammer', 'jigsaw', 'cordless screwdriver' or 'to drill' can be considered as terms in the domain of DIY (’do-it-yourself’); 'beaten egg whites' or 'electric blender' as terms in the domain of cooking. These examples cover different linguistic forms: simple terms like 'hammer' and complex terms like 'beaten egg whites', which consist of several simple words. However, although these words might seem to be obvious examples of terms, in many cases the decision to distinguish a term from a ‘non-term’ is not straightforward. There is no common, established way to define terms, but there are multiple terminology theories and diverse approaches to conduct human annotation studies. In addition, terms can be perceived to be more or less terminological, and the hard distinction between term and ‘non-term’ can be unsatisfying. Beyond term definition, when it comes to the automatic extraction of terms, there are further challenges, considering that complex terms as well as simple terms need to be automatically identified by an extraction system. The extraction of complex terms can profit from exploiting information about their constituents because complex terms might be infrequent as a whole. Simple terms might be more frequent, but they are especially prone to ambiguity. If a system considers an assumed term occurrence in text, which actually carries a different meaning, this can lead to wrong term extraction results. Thus, term complexity and ambiguity are major challenges for automatic term extraction. The present work describes novel theoretical and computational models for the considered aspects. It can be grouped into three broad categories: term definition studies, conventional automatic term extraction models, and extended automatic term extraction models that are based on fine-grained term frameworks. Term complexity and ambiguity are special foci here. In this thesis, we report on insights and improvements on these theoretical and computational models for terminology: We find that terms are concepts that can intuitively be derstood by lay people. We test more fine-grained term characterization frameworks that go beyond the conventional term/‘non-term’-distinction. We are the first to describe and model term ambiguity as gradual meaning variation between general and domain-specific language, and use the resulting representations to prevent errors typically made by term extraction systems resulting from ambiguity. We develop computational models that exploit the influence of term constituents on the prediction of complex terms. We especially tackle German closed compound terms, which are a frequent complex term type in German. Finally, we find that we can use similar strategies for modeling term complexity and ambiguity computationally for conventional and extended term extraction.Item Open Access Cycle-consistent adversarial networks for automatic speech recognition(2024) Li, Chia-Yu; Vu, Ngoc Thang (Prof. Dr.)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 Cross-lingual frame comparability : computational and linguistic perspectives(2023) Sikos, Jennifer; Padó, Sebastian (Prof. Dr.)Frames are descriptions of commonplace scenarios or events. Because they describe everyday scenes, such as buying or eating, it seems reasonable to assume that many frames in one language would carry over directly to other languages. However, the specifics of how that scene is realized can be highly specific to a culture; it is still an open research question as to how well (and how many) frames actually apply across languages. This thesis concerns cross-lingual frame comparability - the degree to which a frame can be transferred from one language to another. It addresses several aspects of frame comparability: what is frame comparability; how a computational system can measure cross-lingual frame comparability; and how frame comparability affects cross-lingual models of frames.Item Open Access Methods for mining political opinions from texts and large language models(2025) Ceron, Tanise; Padó, Sebastian (Prof. Dr.)In democratic societies, the diversity of opinions enables individuals to express their values and engage with differing perspectives. This thesis investigates political opinions through two lenses: texts and models, examining both ideological positions and policy issue preferences. While ideological analysis is well-established, policy issue preferences represent a more nuanced, underexplored research area. Investigating political opinions from political parties is essential for understanding voter choices, policy decision-making, and the shifts in party agendas over time. In the first part of this thesis, I focus on methods for mining political opinions from party manifestos. Automating the identification of political opinions helps process large datasets, minimize annotation time, and offer timely updates on newly released information from parties. I investigate how accurately party positions can be identified from texts with minimal annotations and the level of detail achievable in this process. We also explore the extent to which party positions can be identified on a large scale across different languages and countries.Results demonstrate that the identification of party positions can be distinguished between the tasks of political scaling and positioning which have substantial differences in terms of evaluation and application. Additionally, findings indicate that improving text representations through in-domain fine-tuning significantly benefits the performance when methods depend on text similarity. And finally, party scaling across languages achieves high performance with multilingual models. Models have become my object of study with the advent of LLMs. They introduce new concerns regarding the type of biases embedded and reproduced by them. Given the importance of shedding light on political biases in LLMs, the second part of this thesis addresses the evaluation and identification of political biases in LLMs. Our research questions center on robustly evaluating LLMs for biases and identifying the political biases regarding ideology and policy issue preferences. This thesis provides definitions of political bias and political worldview, which aid in designing methods for their evaluation. Moreover, it contributes with a framework for a robust evaluation of biases in LLMs and a dataset for evaluating political opinions in LLMs. Finally, findings indicate that small parameter size models are not reliable in their answers, and that LLMs do hold consistent political worldviews in relation to some policy issues. Overall, they highlight the necessity for continued research to understand the complexities and societal implications of developing models integrating diverse political opinions into AI systems.Item Open Access Understanding and adapting to user mental models in task-oriented dialog(2026) Vanderlyn, Lindsey; Vu, Ngoc Thang (Prof. Dr.)Item Open Access What makes discussions constructive? : modeling argument and deliberative quality(2025) Falk, Neele; Lapesa, Gabriella (Jun.-Prof. Dr.)Public discourse is an important building block of democracy: it is where opinions are exchanged and certain narratives are formed or reinforced. In recent years, the importance of everyday discussions, for example on social media, has grown. These platforms allow citizens from different backgrounds and with different political views to participate. The aim of the following thesis is to empirically model constructive discussion behavior. Only constructive discourse can strengthen democracy through participation and the exchange of different perspectives. If this fails, the result is fake news and declining trust in democracy—and thus a threat to any democratic system. This work takes an interdisciplinary perspective. The research is based on actual discussions between lay citizens (e.g., online discussions). The theoretical basis is provided by normative frameworks that define what constitutes a constructive discussion or a good argument (e.g., deliberative theory or the definition of good argumentation in rhetoric). These are modeled and evaluated using methods from natural language processing (NLP). NLP allows large amounts of data to be analyzed efficiently—for example, as shown in a publication of this work, all discussion posts on Reddit about the COVID-19 pandemic. The resulting models can in turn be used to develop semi-automatic interventions for more constructive discussion behavior: the moderation of (online) discussions. The thesis is divided into three parts: the first part examines how central aspects of constructive discussion behavior can be defined and how they can be automatically measured or evaluated in natural language. In the social sciences, these aspects are summarized and characterized under the core concept of "deliberative quality", while in rhetoric and computational linguistics they are treated under the concept of "argument quality". The second part of this thesis takes a closer look at a particular aspect of deliberative quality: the narration of personal experiences and stories (known as "storytelling"), a phenomenon that is often used to clarify a point of view. In the context of constructive discourse, storytelling can, for example, promote perspective-taking, is less conflict-laden than value-based argumentation, and can contribute to creating a collective identity or a shared narrative. The third part of this thesis deals with semi-automatic support for moderation. This is based on data containing human moderation behavior (e.g., from experts in the field of moderation or from active discussion participants). In this part, the findings from the first two building blocks are brought together to examine the relationship between moderation and aspects of (non-)constructive discussion behavior and, based on this, how these insights can be used to develop models for supporting human moderators.Item Open Access Challenges of computational social science analysis with NLP methods(2022) Dayanik, Erenay; Padó, Sebastian (Prof. Dr.)Computational Social Science (CSS) is an emerging research area at the intersection of social science and computer science, where problems of societal relevance can be addressed by novel computational methods. With the recent advances in machine learning and natural language processing as well as the availability of textual data, CSS has opened up to new possibilities, but also methodological challenges. In this thesis, we present a line of work on developing methods and addressing challenges in terms of data annotation and modeling for computational political science and social media analysis, two highly popular and active research areas within CSS. In the first part of the thesis, we focus on a use case from computational political science, namely Discourse Network Analysis (DNA), a framework that aims at analyzing the structures behind complex societal discussions. We investigate how this style of analysis, which is traditionally performed manually, can be automated. We start by providing a requirement analysis outlining a roadmap to decompose the complex DNA task into several conceptually simpler sub-tasks. Then, we introduce NLP models with various configurations to automate two of the sub-tasks given by the requirement analysis, namely claim detection and classification, based on different neural network architectures ranging from unidirectional LSTMs to Transformer based architectures. In the second part of the thesis, we shift our focus to fairness, a central concern in CSS. Our goal in this part of the thesis is to analyze and improve the performances of NLP models used in CSS in terms of fairness and robustness while maintaining their overall performance. With that in mind, we first analyze the above-mentioned claim detection and classification models and propose techniques to improve model fairness and overall performance. After that, we broaden our focus to social media analysis, another highly active subdomain of CSS. Here, we study text classification of the correlated attributes, which pose an important but often overlooked challenge to model fairness. Our last contribution is to discuss the limitations of the current statistical methods applied for bias identification; to propose a multivariate regression based approach; and to show that, through experiments conducted on social media data, it can be used as a complementary method for bias identification and analysis tasks. Overall, our work takes a step towards increasing the understanding of challenges of computational social science. We hope that both political scientists and NLP scholars can make use of the insights from this thesis in their research.Item Open Access Linguistically-informed modeling of potentials for misunderstanding(2024) Anthonio, Talita; Roth, Michael (Dr.)Misunderstandings are prevalent in communication. While there is a large amount of work on misunderstandings in conversations, only little attention has been given to misunderstandings that arise from text. This is because readers and writers typically do not interact with one another. However, texts that potentially evoke different interpretations can be identified by certain linguistic phenomena, especially those related to implicitness or underspecificity. In Computational Linguistics, there is a considerable amount of work conducted on such linguistic phenomena and the computational modeling thereof. However, most of these studies do not examine when these phenomena cause misunderstandings. This is a crucial aspect, because ambiguous language does not always cause misunderstanding. In this thesis, we provide the first steps to develop a computational model that can automatically identify whether an instructional text is likely to cause misunderstandings ("potentials for misunderstanding"). To achieve this goal, we build large corpora with potentials for misunderstanding in instructional texts. We follow previous work and define misunderstandings as the existence of multiple, plausible interpretations. As these interpretations may be similar in meaning to one another, we specifically define misunderstandings as the existence of multiple plausible, but conflicting interpretations. Therefore, we find texts that potentially cause misunderstanding ("potentials for misunderstanding") by looking for passages that have several plausible interpretations that are conflicting to one another. We automatically identify such passages from revision histories of instructional texts, based on the finding that we can find potentials for misunderstanding by looking into older versions of a text, and their clarifications thereof in newer versions. We specifically look for unclarified sentences that contain implicit and underspecified language, and study their clarifications. Through several analyses and crowdsourcing studies, we demonstrate that our corpora provide valuable resources on potentials for misunderstanding, as we find that revised sentences are better than their previous ones. Furthermore, we show that the provided corpora can be used for several computational modeling purposes. The three resulting models can each be combined to identify whether a text potentially causes misunderstanding or not. More specifically, we first develop a model that can detect improvements in a text, even when they are subtle and closely dependent on the context. In an analysis, we verify that the judgements from the model on what makes a better or equally good sentence overlap with the judgements by humans. Secondly, we build a transformer-based language model that automatically resolves potentials for misunderstanding caused by implicit references. We find that modeling discourse context improves the performance of this model. In an analysis, we find that the best model is not only capable of generating the golden resolution, but also capable of generating several plausible resolutions for implicit references in instructional text. We use this finding to build a large dataset with plausible and implausible resolutions of implicit and underspecified elements. We use the resulting dataset for a third computational task, in which we train a model to automatically distinguish between plausible and implausible resolutions for implicit and underspecified elements. We show that this model and the provided dataset can be used to find passages with several, plausible clarifications. Since our definition of misunderstanding focuses on conflicting clarifications, we conduct a final study to conclude the thesis. In particular, we provide and validate a crowdsourcing set-up that allows to find the cases with conflicting, plausible, resolutions. The set-up and findings could be used in future research to directly train a model to identify passages with implicit elements that have conflicting resolutions.Item Open Access Prosodic event detection for speech understanding using neural networks(2020) Stehwien, Sabrina; Vu, Ngoc Thang (Prof. Dr.)
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