@phdthesis{BrehmJurish2005,
  author    = {Brehm-Jurish, Eva Ute},
  title     = {Connective ties in discourse : three ERP-studies on causal, temporal and concessive connective ties and their influence on language processing},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-6780},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {Connective ties in discourse: Three ERP studies on causal, temporal and concessive connective ties and their influence on language processing. Questions In four experiments the influence of lexical connectives such as " darum", therefore, " danach", afterwards, and " trotzdem", nevertheless, on the processing of short two-sentence discourses was examined and compared to the processing of deictical sentential adverbs such as " gestern", yesterday, and " lieber", rather. These latter words do not have the property of signaling a certain discourse relation between two sentences, as connective ties do. Three questions were central to the work: * Do the processing contrasts found between connective and non-connective elements extend to connective ties and deictical sentential adverbs (experiments 2 and 3)? * Does the semantic content of the connective ties play the primary role, i.e is the major distinction to be made indeed between connective and non-connective or instead between causal, temporal and concessive? * When precisely is the information provided by connective ties used? There is some evidence that connective ties can have an immediate influence on the integration of subsequent elements, but the end of the second sentences appears to play an important role as well: experiments 2, 3, and 4. Conclusions First of all, the theoretical distinction between connective and non-connective elements does indeed have " cognitive reality" . This has already been shown in previous studies. The present studies do however show, that there is also a difference between one-place discourse elements (deictical sentential adverbs) and two-place discourse elements, namely connective ties, since all experiments examining this contrast found evidence for qualitatively and quantitatively different processing (experiments 1, 2, and 3). Secondly, the semantic type of the connective ties also plays a role. This was not shown for the LAN, found for all connective ties when compared to non-connective elements, and consequently interpreted as a more abstract reflection of the integration of connective ties. There was also no difference between causal and temporal connective ties before the end of the discourses in experiment 3. However, the N400 found for incoherent discourses in experiment 2, larger for connective incoherent than non-connective incoherent discourses, as well as the P3b found for concessive connective ties in the comparison between causal and concessive connective ties gave reason to assume that the semantic content of connective ties is made use of in incremental processing, and that the relation signaled by the connective tie is the one that readers attempt to construct. Concerning when the information provided by connective ties is used, it appears as if connectivity is generally and obligatorily taken at face value. As long as the meaning of a connective tie did not conflict with a preferred canonical discourse relation, there were no differences found for varying connective discourses (experiment 3). However, the fact that concessive connective ties announce the need for a more complex text representation was recognized and made use of immediately (experiment 4). Additionally, a violation of the discourse relation resulted in more difficult semantic integration if a connective tie was present (experiment 2). It is therefore concluded here that connective ties influence processing immediately. This claim has to be modified somewhat, since the sentence-final elements suggested that connective ties trigger different integration processes than non-connective elements. It seems as if the answer to the question of when connective ties are processed is neither exclusively immediately nor exclusively afterwards, but that both viewpoints are correct. It is suggested here that before the end of a discourse economy plays a central role in that a canonical relation is assumed unless there is evidence to the contrary. A connective tie could have the function of reducing the dimensions evaluated in a discourse to the one signaled by the connective tie. At the end of the discourse the representation is evaluated and verified, and an integrated situation model constructed. Here, the complexity of the different discourse relations that connective ties can signal, is expressed.},
  subject      = {Sprachverarbeitung <Psycholinguistik>},
  language  = {en}
}
@phdthesis{Bourgonje2021,
  author    = {Bourgonje, Peter},
  title     = {Shallow discourse parsing for German},
  doi       = {10.25932/publishup-50663},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-506632},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 140},
  year      = {2021},
  abstract  = {While the last few decades have seen impressive improvements in several areas in Natural Language Processing, asking a computer to make sense of the discourse of utterances in a text remains challenging. There are several different theories that aim to describe and analyse the coherent structure that a well-written text inhibits. These theories have varying degrees of applicability and feasibility for practical use. Presumably the most data-driven of these theories is the paradigm that comes with the Penn Discourse TreeBank, a corpus annotated for discourse relations containing over 1 million words. Any language other than English however, can be considered a low-resource language when it comes to discourse processing. This dissertation is about shallow discourse parsing (discourse parsing following the paradigm of the Penn Discourse TreeBank) for German. The limited availability of annotated data for German means the potential of modern, deep-learning based methods relying on such data is also limited. This dissertation explores to what extent machine-learning and more recent deep-learning based methods can be combined with traditional, linguistic feature engineering to improve performance for the discourse parsing task. A pivotal role is played by connective lexicons that exhaustively list the discourse connectives of a particular language along with some of their core properties. To facilitate training and evaluation of the methods proposed in this dissertation, an existing corpus (the Potsdam Commentary Corpus) has been extended and additional data has been annotated from scratch. The approach to end-to-end shallow discourse parsing for German adopts a pipeline architecture and either presents the first results or improves over state-of-the-art for German for the individual sub-tasks of the discourse parsing task, which are, in processing order, connective identification, argument extraction and sense classification. The end-to-end shallow discourse parser for German that has been developed for the purpose of this dissertation is open-source and available online. In the course of writing this dissertation, work has been carried out on several connective lexicons in different languages. Due to their central role and demonstrated usefulness for the methods proposed in this dissertation, strategies are discussed for creating or further developing such lexicons for a particular language, as well as suggestions on how to further increase their usefulness for shallow discourse parsing.},
  language  = {en}
}