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 Computational modelling of coreference and bridging resolution(2019) Rösiger, Ina; Kuhn, Jonas (Prof. Dr.)Item Open Access Modeling paths in knowledge graphs for context-aware prediction and explanation of facts(2019) Stadelmaier, JosuaKnowledge 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.Item Open Access CorefAnnotator : a new annotation tool for entity references(2018) Reiter, NilsItem 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 The German boundary tones: categorical Perception, perceptual magnets, and the perceptual reference space(2012) Schneider, Katrin; Dogil, Grzegorz (Prof. Dr.)This thesis experimentally analyzes the perception of prosodic categories in German, using the two German boundary tones L% and H% postulated by German phonology. These two boundary tone categories were selected because they constitute the least disputed tonal contrast. In many languages, in German as well, the contrast between the low (L%) and the high (H%) boundary tone corresponds to a contrast in sentence mode. The low boundary tone is interpreted as a statement and the high boundary tone as a question. For all experiments presented in this thesis it is hypothesized that the different perception of L% and H% as statement versus question, respectively, can be attributed to a contrast between two prosodic categories, i.e. to Categorical Perception. The basis for this hypothesis is the observation that the sentence mode of a syntactically ambiguous utterance can only be determined by the height of its boundary tone. Assuming the existence of the two proposed boundary tone categories two experimental designs that can be used to confirm categories, perceptual differences inside a category or perceptual differences between categories are presented. These two designs are the test for the Categorical Perception (CP) and the test for the Perceptual Magnet Effect (PME). Originally, both designs were developed to examine perceptual differences in the segmental domain, especially for the evaluation of phoneme categories. Categorical Perception is confirmed when the boundary between these two categories corresponds to the point at which the discrimination performance between two adjacent stimuli is best. If for two speech events the Categorical Perception test is successful then these two events will be confirmed as being categories of the respective language. A Perceptual Magnet Effect includes a warping of the perceptual space towards a prototype of the respective category. Such a warping does not occur towards a non-prototype of the same category. The result of the warping is a significantly lower discrimination performance around the prototype, i.e. the prototype is not or only hard to discriminate from a adjacent stimulus. Such a warping is not found around a non-prototype, although the acoustic difference between a stimulus and the non-prototype is comparable to the acoustic difference between a stimulus and the prototype. For the analyses and the interpretation of the experimental results the Signal Detection Theory (SDT) and the Exemplar Theory are used. Signal Detection Theory postulates that despite similar auditory abilities subjects may differ in their perceptual results because of their individual response criterion. Exemplar Theory proposes that listeners store their perceived instances of speech events in exemplar clouds located in their perceptual space, and that these instances are stored with much phonetic detail. During speech production, the speaker uses these clouds of similar exemplars to produce an instance of a speech event. Thus, speech perception and production are inseparably connected. The more exemplars are stored the more stable a speech category will get. Only stable categories can develop a category center and a Perceptual Magnet Effect. In various studies reaction times were found to be a reliable indicator for the simplicity of a perceptual decision. Thus, in the experiment presented in this thesis reaction times were measured for each individual decision. The results support the already known correlation, i.e. the more simple a perceptual decision is the lower the reaction time will be. To summarize, the results discussed in this thesis support the existence of prosodic categories in general, and especially those of the high and the low boundary tone in German. These two prosodic categories are used to differentiate between the sentence modes statement versus question, but only in case of syntactically ambiguous phrases. Furthermore, the results support the use on Exemplar Theory for speech data. The category of the low boundary tone seems to contain much more exemplars than the category of the high boundary tone as the latter category is less often produced and thus less often perceived than the first one. This results in a clear Perceptual Magnet Effect for the L% category as there enough exemplar are stored to support the development of a category center, and only in the center of a category the PME can occur. For most listeners the H% category contains only a few exemplars which in turn inhibits the development of a Perceptual Magnet Effect there. The logged reaction times support the perceptual findings and reveal the hypothesis that reaction times correlate with the simplicity of a perceptual decision.Item Open Access Sub-lexical investigations: German particles, prefixes and prepositions(2013) Roßdeutscher, AntjeThe papers investigate constructions with P(repositional) elements in German. It aims at a comprehensive theory of the syntax-semantics interface for the different verbal constructions in German, including verb plus prepostional phrase, (separable) particle verbs, and (inseparable) prefix verbs. The constructions are given syntactic representations following minimalist principles as known from \textit{Distributive Morphology} (DM) according to which a single syntactic engine drives formation of both words and phrases. Among the syntactic principles the Split-P hypothesis plays a central role. A crucial feature of the approach is that the syntactic structures are used as input to the computation of semantic representations according to principles of Discourse Representation Theory (DRT). Several challenges that present themselves for a compositional theory of word- and phrase- formation with P-elements in German are accounted for in the paper: syntactic separability of verb-particle constructions vs non-separability of prefix-verbs; semantic restrictions in the P-elements to build constructions of the former and the latter type; syntactic alternations w.r.t. the realisation of figure and ground arguments and the semantic basis of these alternations. A particular challenge are the differences in the conceptual and aspectual contribution of the same prepositional root in different syntactic contexts.Item Open Access Automatische Kategorisierung von Autoren in Bezug auf Arzneimittel in Twitter(2016) Xu, MInMit der rasch wachsenden Popularität von Twitter werden auch immer mehr unterschiedliche Themen diskutiert. Dies lässt sich auch im Bezug auf die Wirkung von Arzneimitteln beobachten. Es ist daher sehr interessant herauszufinden, welche sozialen Gruppen dazu neigen, bestimmte Arzneimittel in Twitter zu diskutieren und welche Arzneimittel am meisten in Twitter diskutiert werden. Deshalb bietet es sich an, mit Verwendung der Technologie der Textklassifikation, die große Anzahl von Tweets zu kategorisieren. In dieser Arbeit wird das hauptsächlich mit dem Maximum Entropy Klassifikator realisiert, mit den sich die Autoren der Tweets erkennen lassen. Da das Maximum Entropy Modell eine Vielzahl der relevanten oder irrelevanten Kenntnis der Wahrscheinlichkeiten umfassend beobachten kann, erzielt der Maximum Entropy Klassifikator im Vergleich zum naiven Bayes-Klassifikator in dieser Arbeit ein besseres Ergebnis bei der Multi-Klassen-Klassifikation. Die Beeinflussung auf die Leistungen des Maximum Entropy Klassifikator unter der Verwendungen von verschiedenen Methoden, wie Information Gain & Mutual Information und LDA-Topic Model, zur Auswahl der Merkmale und unterschiedlicher Anzahl an Merkmalen wird verglichen und analysiert. Die Ergebnissen zeigen, dass die Methoden Information Gain & Mutual Information und LDA-Topic-Model gute praktische Ansätze sind, mit denen die Merkmale kurzer Texte erkannt werden können. Mit dem Maximum Entropy Klassifikator wird eine durchschnittliche Testgenauigkeit von 79.8% erreicht.Item Open Access Event knowledge and models of logical metonymy interpretation(2014) Zarcone, Alessandra; Padó, Sebastian (Prof. Dr.)During language understanding, people do not only rely on what they read or hear, but they also exploit implicit information. For example, when they process the expression "begin the book", they understand it involves an event which is not explicitly mentioned (e.g. "begin reading the book). This thesis looks at these constructions, known as logical metonymies, which combine an event-selecting verb and entity-denoting object and involve covert events. Logical metonymies are an interesting challenge for theories of lexical semantics: they need to be reconciled with compositionality, they require the integration of context (writers typically write books, students typically read them), and they lie at the interface between lexicon and world knowledge (is the information that books are read stored in our mental lexicon or in our world knowledge?). I critically analyze previous hypotheses on logical metonymy with regard to the answer they provide to two core problems: the source problem (what events are retrieved? what type of event knowledge is assumed?) and the trigger problem (why do some constructions trigger a metonymic interpretation and others do not?). Lexicalist approaches claim that the metonymy arises from a type clash between the event-selecting verb and an entity-denoting object, and posit complex lexical items, encoding event information about artifacts (e.g. book: read), to explain the recovery of covert events. Pragmatic-based approaches argue against the idea that lexical items have an internal structure, suggesting that covert events arise from the underspecification of a logical metonymy and are inferred via non-lexical knowledge. I look with particular attention at the role of event knowledge, which lexicalist approaches place in our mental lexicon, while pragmatic-based approaches place it in our world knowledge. I propose a third hypothesis, based on thematic fit and generalized event knowledge of typical events and their participants, which have been shown to guide efficient incremental processing: I argue that contextual elements cue generalized event knowledge, which plays a key role in determining the covert event for a logical metonymy. I explore this hypothesis from an interdisciplinary perspective, employing both psycholinguistic experiments and computational models, in order to seek converging evidence and confront it with the theoretical investigation. The results from the psycholinguistic experiments and from the computational (distributional) models support the hypothesis that covert event retrieval is guided by generalized event knowledge. I also employ the computational models to analyze previous experimental results and to explore the hypothesis that thematic fit, informed by generalized event knowledge, is ultimately responsible for the trigger of the logical metonymy. I then report on more psycholinguistic evidence showing that a notion of type is indeed necessary to account for differences between metonymic and non-metonymic constructions, and that both type and thematic fit play a role in logical metonymy interpretation. Lastly, I argue for a context-sensitive model of logical metonymy interpretation that exploits an information-rich lexicon, but needs to rethink the notion of type and reconcile it with the notion of thematic fit.Item Open Access The Impact of intensifiers, diminishers and negations on emotion expressions(2017) Strohm, FlorianThere are several areas of application for emotion detection systems, for example social media analysis, for which it is important to reliably recognize expressed emotions. This thesis takes negations, intensifiers and diminishers on emotion expressions in Tweets into account, in order to study whether this can improve an emotion detection system. It uses different emotion classifiers together with various modifier detection approaches to evaluate the impact of modifiers on emotion expressions. The results show that an emotion detection system can be slightly improved if negations are taken into account. The thesis also studies the correlation between modified emotion words and basic emotions to obtain a better understanding about modified emotions. The analysis of the results shows correlations between modified and basic emotions, which enables us to determine the expressed basic emotion of modified emotion words.Item Open Access Natural language processing and information retrieval methods for intellectual property analysis(2014) Jochim, Charles; Schütze, Hinrich (Prof. Dr.)More intellectual property information is generated now than ever before. The accumulation of intellectual property data, further complicated by this continued increase in production, makes it imperative to develop better methods for archiving and more importantly for accessing this information. Information retrieval (IR) is a standard technique used for efficiently accessing information in such large collections. The most prominent example comprising a vast amount of data is the World Wide Web, where current search engines already satisfy user queries by immediately providing an accurate list of relevant documents. However, IR for intellectual property is neither as fast nor as accurate as what we expect from an Internet search engine. In this thesis, we explore how to improve information access in intellectual property collections by combining previously mentioned IR techniques with advanced natural language processing (NLP) techniques. The information in intellectual property is encoded in text (i.e., language), and we expect that by adding better language processing to IR we can better understand and access the data. NLP is a quite varied field encompassing a number of solutions for improving the understanding of language input. We concentrate more specifically on the NLP tasks of statistical machine translation, information extraction, named entity recognition (NER), sentiment analysis, relation extraction, and text classification. Searching for intellectual property, specifically patents, is a difficult retrieval task where standard IR techniques have had only moderate success. The difficulty of this task only increases when presented with multilingual collections as is the case with patents. We present an approach for improving retrieval performance on a multilingual patent collection by using machine translation (an active research area in NLP) to translate patent queries before concatenating these parallel translations into a multilingual query. Even after retrieving an intellectual property document however, we still face the problem of extracting the relevant information needed. We would like to improve our understanding of the complex intellectual property data by uncovering latent information in the text. We do this by identifying citations in a collection of scientific literature and classifying them by their citation function. This classification is successfully carried out by exploiting some characteristics of the citation text, including features extracted via sentiment analysis, NER, and relation extraction. By assigning labels to citations we can better understand the relationships between intellectual property documents, which can be valuable information for IR or other applications.