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 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.Item 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.Item Open Access Computational modelling of coreference and bridging resolution(2019) Rösiger, Ina; Kuhn, Jonas (Prof. Dr.)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 Editorial - perspectives for natural language processing between AI, linguistics and cognitive science(2022) Lenci, Alessandro; Padó, SebastianItem Open Access CUTE - CRETA Un-/Shared Task zu Entitätenreferenzen(2017) Reiter, Nils; Blessing, André; Echelmeyer, Nora; Kremer, Gerhard; Koch, Steffen; Murr, Sandra; Overbeck, Maximilian; Pichler, AxelDies ist die Veröffentlichung eines shared/unshared Task Workshops (entwickelt in CRETA: Center for Reflected Text Analytics), der im Rahmen der DHd 2017 in Bern (CH) stattfand. Im Gegensatz zu shared tasks, bei denen die Performanz verschiedener Systeme/Ansätze/Methoden direkt anhand einer klar definierten und quantitativ evaluierten Aufgabe verglichen wird, sind unshared tasks offen für verschiedenartige Beiträge, die auf einer gemeinsamen Datensammlung basieren. Shared und Unshared Tasks in den Digital Humanities sind ein vielversprechender Weg, Kollaboration und Interaktion zwischen Geistes-, Sozial- und ComputerwissenschaftlerInnen zu fördern und zu pflegen. Konkret riefen wir dazu auf, gemeinsam an einem heterogenen Korpus zu arbeiten, in dem Entitätenreferenzen annotiert wurden. Das Korpus besteht aus Parlamentsdebatten des Deutschen Bundestags, Briefen aus Goethes Die Leiden des jungen Werther, einem Abschnitt aus Adornos Ästhetischer Theorie und den Büchern von Wolframs von Eschenbach Parzival (mittelhochdeutsch). Auch wenn jede Textsorte ihre eigenen Besonderheiten hat, wurden alle nach einheitlichen Annotationsrichtlinien annotiert, die wir auch zur Diskussion stellten. Wir veröffentlichen hier den Aufruf zu Workshop-Beiträgen, die Annotationsrichtlinien, die Korpusdaten samt Beschreibung und die einführenden Vortragsfolien des Workshops.Item Open Access Cross-lingual citations in English papers : a large-scale analysis of prevalence, usage, and impact(2021) Saier, Tarek; Färber, Michael; Tsereteli, TornikeCitation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation-based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available.Item Open Access Using morpho-syntactic and semantic information to improve statistical machine translation(2018) Di Marco, Marion; Schulte im Walde, Sabine (PD Dr.)Statistische Maschinelle Übersetzungssystem werden von Wort-alignierten parallelen Corpora abgeleitet und benutzen üblicherweise keine expliziten linguistischen Informationen. Dies kann zu Generalisierungsproblemen führen, besonders wenn morphologisch komplexe Sprachen übersetzt werden. Diese Arbeit untersucht die Integration von linguistischen Informationen in ein Übersetzungssystem, das in eine morphologisch komplexe Sprache übersetzt: basierend auf einem Übersetzungssystem, das die Morphologie der Zielsprache modelliert, werden syntaktische und semantische Informationen in das System integriert, mit dem Ziel, die Modellierung von Subkategorisierung und Präpositionen zu verbessern.Item Open Access Prosodic event detection for speech understanding using neural networks(2020) Stehwien, Sabrina; Vu, Ngoc Thang (Prof. Dr.)Item Open Access Task-based parser output combination : workflow and infrastructure(2018) Eckart, Kerstin; Kuhn, Jonas (Prof. Dr.)This dissertation introduces the method of task-based parser output combination as a device to enhance the reliability of automatically generated syntactic information for further processing tasks. Parsers, i.e. tools generating syntactic analyses, are usually based on reference data. Typically these are modern news texts. However, the data relevant for applications or tasks beyond parsing often differs from this standard domain, or only specific phenomena from the syntactic analysis are actually relevant for further processing. In these cases, the reliability of the parsing output might deviate essentially from the expected outcome on standard news text. Studies for several levels of analysis in natural language processing have shown that combining systems from the same analysis level outperforms the best involved single system. This is due to different error distributions of the involved systems which can be exploited, e.g. in a majority voting approach. In other words: for an effective combination, the involved systems have to be sufficiently different. In these combination studies, usually the complete analyses are combined and evaluated. However, to be able to combine the analyses completely, a full mapping of their structures and tagsets has to be found. The need for a full mapping either restricts the degree to which the participating systems are allowed to differ or it results in information loss. Moreover, the evaluation of the combined complete analyses does not reflect the reliability achieved in the analysis of the specific aspects needed to resolve a given task. This work presents an abstract workflow which can be instantiated based on the respective task and the available parsers. The approach focusses on the task-relevant aspects and aims at increasing the reliability of their analysis. Moreover, this focus allows a combination of more diverging systems, since no full mapping of the structures and tagsets from the single systems is needed. The usability of this method is also increased by focussing on the output of the parsers: It is not necessary for the users to reengineer the tools. Instead, off-the-shelf parsers and parsers for which no configuration options or sources are available to the users can be included. Based on this, the method is applicable to a broad range of applications. For instance, it can be applied to tasks from the growing field of Digital Humanities, where the focus is often on tasks different from syntactic analysis.