Universität Stuttgart
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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 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 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 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 Distributional analysis of entities(2022) Gupta, Abhijeet; Padó, Sebastian (Prof. Dr.)Arguably, one of the most important aspects of natural language processing is natural language understanding which relies heavily on lexical knowledge. In computational linguistics, modelling lexical knowledge through distributional semantics has gained considerable popularity. However, the modelling is largely restricted to generic lexical categories (typically common nouns, adjectives, etc.) which are associated with coarse-grained information i.e., the category country has a boundary, rivers and gold deposits. Comparatively, less attention has been paid towards modelling entities which, on the other hand, are associated with fine-grained real-world information, for instance: the entity Germany has precise properties such as, (GDP - 3.6 trillion Euros), (GDP per capita - 44.5 thousand Euros) and (Continent - Europe). The lack of focus on entities and the inherent latency of information in distributional representations warrants greater efforts towards modelling entity related phenomena and, increasing the understanding about the information encoded within distributional representations. This work makes two contributions in that direction: (a) We introduce a semantic relation – Instantiation, a relation between entities and their categories, and distributionally model it to investigate the hypothesis that distributional distinctions do exist in modelling entities versus modelling categories within a semantic space. Our results show that in a semantic space: 1) entities and categories are quite distinct with respect to their distributional behaviour, geometry and linguistic properties; 2) Instantiation relation is recoverable by distributional models; and, 3) for lexical relational modelling purposes, categories are better represented by the centroids of their entities instead of their distributional representations constructed directly from corpora. (b) We also investigate the potential and limitations of distributional semantics for the purpose of Knowledge Base Completion, starting with the hypothesis that fine-grained knowledge is encoded in distributional representations of entities during their meaning construction. We show that: 1) fine-grained information of entities is encoded in distributional representations and can be extracted by simple data-driven supervised models as attribute-value pairs; 2) the models can predict the entire range of fine-grained attributes, as seen in a knowledge base, in one go; and, 3) a crucial factor in determining success in extracting this type of information is contextual support i.e., the extent of contextual information captured by a distributional model during meaning construction. Overall, this thesis takes a step towards increasing the understanding about entity meaning representations in a distributional setup, with respect to their modelling and the extent of knowledge inclusion during their meaning construction.Item Open Access Computational models of word order(2022) Yu, Xiang; Kuhn, Jonas (Prof. Dr.)A sentence in our mind is not a simple sequence of words but a hierarchical structure. We put the sentence in the linear order when we utter it for communication. Linearization is the task of mapping the hierarchical structure of a sentence into its linear order. Our work is based on the dependency grammar, which models the dependency relation between the words, and the resulting syntactic representation is a directed tree structure. The popularity of dependency grammar in Natural Language Processing (NLP) benefits from its separation of structure order and linear order and its emphasis on syntactic functions. These properties facilitate a universal annotation scheme covering a wide range of languages used in our experiments. We focus on developing a robust and efficient computational model that finds the linear order of a dependency tree. We take advantage of deep learning models’ expressive power to encode the syntactic structures of typologically diverse languages robustly. We take a graph-based approach that combines a simple bigram scoring model and a greedy decoding algorithm to search for the optimal word order efficiently. We use the divide-and-conquer strategy to reduce the search space, which restricts the output to be projective. We then resolve the restriction with a transition-based post-processing model. Apart from the computational models, we also study the word order from a quantitative linguistic perspective. We examine the Dependency Length Minimization (DLM) hypothesis, which is believed to be a universal factor that affects the word order of every language. It states that human languages tend to order the words to minimize the overall length of dependency arcs, which reduces the cognitive burden of speaking and understanding. We demonstrate that DLM can explain every aspect of word order in a dependency tree, such as the direction of the head, the arrangement of sibling dependents, and the existence of crossing arcs (non-projectivity). Furthermore, we find that DLM not only shapes the general word order preferences but also motivates the occasional deviation from the preferences. Finally, we apply our model in the task of surface realization, which aims to generate a sentence from a deep syntactic representation. We implement a pipeline with five steps, (1) linearization, (2) function word generation, (3) morphological inflection, (4) contraction, and (5) detokenization, which achieved state-of-the-art performance.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.)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 Computational approaches for German particle verbs: compositionality, sense discrimination and non-literal language(2018) Köper, Maximilian; Schulte im Walde, Sabine (PD Dr.)Anfangen (to start) is a German particle verb. Consisting of two parts, a base verb ("fangen") and particle ("an"), with potentially many or no intervening words in a sentence, particle verbs are highly frequent constructions with special properties. It has been shown that this type of verb represents a serious problem for language technology, due to particle verbs' ambiguity, ability to occur separate and seemingly unpredictable behaviour in terms of meaning. This dissertation addresses the meaning of German particle verbs via large-scale computational approaches. The three central parts of the thesis are concerned with computational models for the following components: i) compositionality, ii) senses and iii) non-literal language. In the first part of this thesis, we shed light on the phenomena by providing information on the properties of particle verbs, as well as the related and prior literature. In addition, we present the first corpus-driven statistical analysis. We use two different approaches for addressing the modelling of compositionality. For both approaches, we rely on large amounts of textual data with an algebraic model for representation to approximate meaning. We put forward the existing methodology and show that the prediction of compositionality can be improved by considering visual information. We model the particle verb senses based only on huge amounts of texts, without access to other resources. Furthermore, we compare and introduce the methods to find and represent different verb senses. Our findings indicate the usefulness of such sense-specific models. We successfully present the first model for detecting the non-literal language of particle verbs in a running text. Our approach reaches high performance by combining the established techniques from metaphor detection with particle verb-specific information. In the last part of the thesis, we approach the regularities and the meaning shift patterns. Here, we introduce a novel data collection approach for accessing the meaning components, as well as a computational model of particle verb analogy. The experiments reveal typical patterns in domain changes. Our data collection indicates that coherent verbs with the same meaning shift represent rather scarce phenomena. In summary, we provide novel computational models to previously unaddressed problems, and we report incremental improvements in the existing approaches. Across the models, we observe that semantically similar or synonymous base verbs behave similarly when combined with a particle. In addition, our models demonstrate the difficulty of particle verbs. Finally, our experiments suggest the usefulness of external normative emotion and affect ratings.