05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Open Access
Strukturierte Modellierung von Affekt in Text
(2020) Klinger, Roman; Padó, Sebastian (Prof. Dr.)
Emotionen, Stimmungen und Meinungen sind Affektzustände, welche nicht direkt von einer Person bei anderen Personen beobachtet werden können und somit als „privat“ angesehen werden können. Um diese individuellen Gefühlsregungen und Ansichten dennoch zu erraten, sind wir in der alltäglichen Kommunikation gewohnt, Gesichtsausdrücke, Körperposen, Prosodie, und Redeinhalte zu interpretieren. Das Forschungsgebiet Affective Computing und die spezielleren Felder Emotionsanalyse und Sentimentanalyse entwickeln komputationelle Modelle, mit denen solche Abschätzungen automatisch möglich werden. Diese Habilitationsschrift fällt in den Bereich des Affective Computings und liefert in diesem Feld Beiträge zur Betrachtung und Modellierung von Sentiment und Emotion in textuellen Beschreibungen. Wir behandeln hier unter anderem Literatur, soziale Medien und Produktbeurteilungen. Um angemessene Modelle für die jeweiligen Phänomene zu finden, gehen wir jeweils so vor, dass wir ein Korpus als Basis nutzen oder erstellen und damit bereits Hypothesen über die Formulierung des Modells treffen. Diese Hypothesen können dann auf verschiedenen Wegen untersucht werden, erstens, durch eine Analyse der Übereinstimmung der Annotatorinnen, zweitens, durch eine Adjudikation der Annotatorinnen gefolgt von einer komputationellen Modellierung, und drittens, durch eine qualitative Analyse der problematischen Fälle. Wir diskutieren hier Sentiment und Emotion zunächst als Klassifikationsproblem. Für einige Fragestellungen ist dies allerdings nicht ausreichend, so dass wir strukturierte Modelle vorschlagen, welche auch Aspekte und Ursachen des jeweiligen Gefühls beziehungsweise der Meinung extrahieren. In Fällen der Emotion extrahieren wir zusätzlich Nennungen des Fühlenden. In einem weiteren Schritt werden die Verfahren so erweitert, dass sie auch auf Sprachen angewendet werden können, welche nicht über ausreichende annotierte Ressourcen verfügen. Die Beiträge der Habilitationsarbeit sind also verschiedene Ressourcen, für deren Erstellung auch zugrundeliegende Konzeptionsarbeit notwendig war. Wir tragen deutsche und englische Korpora für aspektbasierte Sentimentanalyse, Emotionsklassifikation und strukturierte Emotionsanalyse bei. Des Weiteren schlagen wir Modelle für die automatische Erkennung und Repräsentation von Sentiment, Emotion und verwandten Konzepten vor. Diese zeigen entweder bessere Ergebnisse, als bisherige Verfahren oder modellieren Phänomene erstmalig. Letzteres gilt insbesondere bei solchen Methoden, welche auf durch uns erstellte Korpora ermöglicht wurden. In den verschiedenen Ansätzen werden wiederkehrend Konzepte gemeinsam modelliert, sei es auf der Repräsentations- oder der Inferenzebene. Solche Verfahren, welche Entscheidungen im Kontext treffen, zeigen in unserer Arbeit durchgängig bessere Ergebnisse, als solche, welche Phänomene getrennt betrachten. Dies gilt sowohl für den Einsatz künstlicher neuronaler Netze, als auch für die Verwendung probabilistischer graphischer Modelle.
Open Access
Computational modelling of coreference and bridging resolution
(2019) Rösiger, Ina; Kuhn, Jonas (Prof. Dr.)
Open Access
Analysis of political positioning from politician’s tweets
(2023) Maurer, Maximilian Martin
Social 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.
Open Access
Effects of paraphrasing and demographic metadata on NLI classification performance
(2023) Marx Larre, Miguel
Native language identification (NLI) refers to the task of automatically deducing the native language (L1) of a document's author, when the document is written in a second language (L2). Documents stem from different sources, but recently more documents are altered before publication through paraphrasing methods. This alteration changes the content, grammar, and style of the document, which inherently obfuscates the L1 of the author. In addition, the demographic metadata of the author, such as age and gender, may influence the performance with which an author's L1 may be detected. In this thesis, two corpora which provide necessary demographic metadata, the International Corpus of Learner English (ICLE) and the \textsc{Trustpilot} corpus, are used to analyze the impact of paraphrasing and demographic factors in the context of NLI tasks. To analyze the effect of paraphrasing on a document, new versions of both corpora are created, which contain paraphrased versions of the documents contained. The effect is inspected using two state-of-the-art NLI systems to perform the task, while the results were analyzed using a regression analysis in combination with dominance analysis (DA). Paraphrasing was found to have a substantial influence in performance of NLI tasks, regardless of corpus, classifier, or paraphrasing method. The usual influence of demographic factors on NLI tasks could not be confirmed in this thesis. Regression analysis and DA allowed for a more profound analysis of the results, which allowed for findings regarding the influence of specific L1s on performance of NLI tasks.
Open Access
Emotion classification based on the emotion component model
(2020) Heindl, Amelie
The term emotion is, despite its frequent use, still mysterious to researchers. This poses difficulties on the task of automatic emotion detection in text. At the same time, applications for emotion classifiers increase steadily in today's digital society where humans are constantly interacting with machines. Hence, the need for improvement of current state-of-the-art emotion classifiers arises. The Swiss psychologist Klaus Scherer published an emotion model according to which an emotion is composed of changes in the five components cognitive appraisal, physiological symptoms, action tendencies, motor expressions, and subjective feelings. This model, which he calls CPM gained reputation in psychology and philosophy, but has so far not been used for NLP tasks. With this work, we investigate, whether it is possible to automatically detect the CPM components in social media posts and, whether information on those components can aid the detection of emotions. We create a text corpus consisting of 2100 Twitter posts, that has every instance labeled with exactly one emotion and a binary label for each CPM component. With a Maximum Entropy classifier we manage to detect CPM components with an average F1-score of 0.56 and average accuracy of 0.82 on this corpus. Furthermore, we compare baseline versions of one Maximum Entropy and one CNN emotion classifier to extensions of those classifiers with the CPM annotations and predictions as additional features. We find slight performance increases of up to 0.03 for the F1-score for emotion detection upon incorporation of CPM information.
Open Access
Question answering on knowledge bases : A comparative study
(2021) Kanjur, Vishnudatha
Question Answering intends to automatically extract accurate and relevant information as the answer to a particular question. A large amount of data from the Web is stored as Knowledge bases in a structured way. Question answering on Knowledge bases is a research field that involves multiple branches of computer science like natural language processing, information retrieval and artificial intelligence. Knowledge Base Question Answering (KBQA) research involves various challenges to be solved in multiple aspects. This thesis aimed to compare several state-of-the-art methods for single relation KBQA. The widely used standard single relation dataset, SimpleQuestions dataset was used in the study against Freebase Knowledge Base (KB). A comprehensive analysis of the underlying models and their architecture was performed. Furthermore, to identify the drawbacks and possible enhancements, several approaches for evaluating the models were explored. The results show how the models were performed and the suitability of considering them for solving real-world problems in question answering.
Open Access
Modeling paths in knowledge graphs for context-aware prediction and explanation of facts
(2019) Stadelmaier, Josua
Knowledge bases are an important resource for question answering systems and search engines but often suffer from incompleteness. This work considers the problem of knowledge base completion (KBC). In the context of natural language processing, knowledge bases comprise facts that can be formalized as triples of the form (entity 1, relation, entity 2). A common approach for the KBC problem is to learn representations for entities and relations that allow for generalizing existing connections in the knowledge base to predict the correctness of a triple that is not in the knowledge base. In this work, I propose the context path model, which is based on this approach. In contrast to existing KBC models, it also provides explanations for predictions. For this purpose, it uses paths that capture the context of a given triple. The context path model can be applied on top of several existing KBC models. In a manual evaluation, I observe that most of the paths the model uses as explanation are meaningful and provide evidence for assessing the correctness of triples. I also show in an experiment that the performance of the context path model on a standard KBC task is close to a state of the art model.
Open Access
Evaluating methods of improving the distribution of data across users in a corpus of tweets
(2023) Milovanovic, Milan
Corpora 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.
Open Access
CorefAnnotator : a new annotation tool for entity references
(2018) Reiter, Nils
Open Access
Modeling the interface between morphology and syntax in data-driven dependency parsing
(2016) Seeker, Wolfgang; Kuhn, Jonas (Prof. Dr.)
When people formulate sentences in a language, they follow a set of rules specific to that language that defines how words must be put together in order to express the intended meaning. These rules are called the grammar of the language. Languages have essentially two ways of encoding grammatical information: word order or word form. English uses primarily word order to encode different meanings, but many other languages change the form of the words themselves to express their grammatical function in the sentence. These languages are commonly subsumed under the term morphologically rich languages. Parsing is the automatic process for predicting the grammatical structure of a sentence. Since grammatical structure guides the way we understand sentences, parsing is a key component in computer programs that try to automatically understand what people say and write. This dissertation is about parsing and specifically about parsing languages with a rich morphology, which encode grammatical information in the form of words. Today’s parsing models for automatic parsing were developed for English and achieve good results on this language. However, when applied to other languages, a significant drop in performance is usually observed. The standard model for parsing is a pipeline model that separates the parsing process into different steps, in particular it separates the morphological analysis, i.e. the analysis of word forms, from the actual parsing step. This dissertation argues that this separation is one of the reasons for the performance drop of standard parsers when applied to other languages than English. An analysis is presented that exposes the connection between the morphological system of a language and the errors of a standard parsing model. In a second series of experiments, we show that knowledge about the syntactic structure of sentence can support the prediction of morphological information. We then argue for an alternative approach that models morphological analysis and syntactic analysis jointly instead of separating them. We support this argumentation with empirical evidence by implementing two parsers that model the relationship between morphology and syntax in two different but complementary ways.

05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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