@phdthesis{Peldszus2017,
  author    = {Peldszus, Andreas},
  title     = {Automatic recognition of argumentation structure in short monological texts},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-421441},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 252},
  year      = {2017},
  abstract  = {The aim of this thesis is to develop approaches to automatically recognise the structure of argumentation in short monological texts. This amounts to identifying the central claim of the text, supporting premises, possible objections, and counter-objections to these objections, and connecting them correspondingly to a structure that adequately describes the argumentation presented in the text. The first step towards such an automatic analysis of the structure of argumentation is to know how to represent it. We systematically review the literature on theories of discourse, as well as on theories of the structure of argumentation against a set of requirements and desiderata, and identify the theory of J. B. Freeman (1991, 2011) as a suitable candidate to represent argumentation structure. Based on this, a scheme is derived that is able to represent complex argumentative structures and can cope with various segmentation issues typically occurring in authentic text. In order to empirically test our scheme for reliability of annotation, we conduct several annotation experiments, the most important of which assesses the agreement in reconstructing argumentation structure. The results show that expert annotators produce very reliable annotations, while the results of non-expert annotators highly depend on their training in and commitment to the task. We then introduce the 'microtext' corpus, a collection of short argumentative texts. We report on the creation, translation, and annotation of it and provide a variety of statistics. It is the first parallel corpus (with a German and English version) annotated with argumentation structure, and -- thanks to the work of our colleagues -- also the first annotated according to multiple theories of (global) discourse structure. The corpus is then used to develop and evaluate approaches to automatically predict argumentation structures in a series of six studies: The first two of them focus on learning local models for different aspects of argumentation structure. In the third study, we develop the main approach proposed in this thesis for predicting globally optimal argumentation structures: the 'evidence graph' model. This model is then systematically compared to other approaches in the fourth study, and achieves state-of-the-art results on the microtext corpus. The remaining two studies aim to demonstrate the versatility and elegance of the proposed approach by predicting argumentation structures of different granularity from text, and finally by using it to translate rhetorical structure representations into argumentation structures.},
  language  = {en}
}
@phdthesis{Grishina2019,
  author    = {Grishina, Yulia},
  title     = {Assessing the applicability of annotation projection methods for coreference relations},
  doi       = {10.25932/publishup-42537},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-425378},
  school      = {Universit{\"a}t Potsdam},
  pages     = {viii, 198},
  year      = {2019},
  abstract  = {The main goal of this thesis is to explore the feasibility of using cross-lingual annotation projection as a method of alleviating the task of manual coreference annotation. To reach our goal, we build a first trilingual parallel coreference corpus that encompasses multiple genres. For the annotation of the corpus, we develop common coreference annotation guidelines that are applicable to three languages (English, German, Russian) and include a novel domain-independent typology of bridging relations as well as state-of-the-art near-identity categories. Thereafter, we design and perform several annotation projection experiments. In the first experiment, we implement a direct projection method with only one source language. Our results indicate that, already in a knowledge-lean scenario, our projection approach is superior to the most closely related work of Postolache et al. (2006). Since the quality of the resulting annotations is to a high degree dependent on the word alignment, we demonstrate how using limited syntactic information helps to further improve mention extraction on the target side. As a next step, in our second experiment, we show how exploiting two source languages helps to improve the quality of target annotations for both language pairs by concatenating annotations projected from two source languages. Finally, we assess the projection quality in a fully automatic scenario (using automatically produced source annotations), and propose a pilot experiment on manual projection of bridging pairs. For each of the experiments, we carry out an in-depth error analysis, and we conclude that noisy word alignments, translation divergences and morphological and syntactic differences between languages are responsible for projection errors. We systematically compare and evaluate our projection methods, and we investigate the errors both qualitatively and quantitatively in order to identify problematic cases. Finally, we discuss the applicability of our method to coreference annotations and propose several avenues of future research.},
  language  = {en}
}