Catálogo de publicaciones - libros

Knowing the location of a user is important for several use cases, such as location specific recommendations, demographic analysis, or monitoring of disaster outbreaks. We present a bottom up study on the impact of text- and metadata-derived contextual features for Twitter geolocation prediction. The final model incorporates individual types of tweet information and achieves state-of-the-art performance on a publicly available test set. The source code of our implementation, together with pretrained models, is freely available at .

The abundance of temporal information in documents has lead to an increased interest in processing such information in the NLP community by considering temporal expressions. Besides domain-adaptation, acquiring knowledge on variation of temporal expressions according to time is relevant for improvement in automatic processing. So far, frequency-based accounts dominate in the investigation of specific temporal expressions. We present an approach to investigate diachronic changes of temporal expressions based on relative entropy – with the advantage of using conditioned probabilities rather than mere frequency. While we focus on scientific writing, our approach is generalizable to other domains and interesting not only in the field of NLP, but also in humanities.

The (CA) concerns the estimation of a user that an implicature, derived from an utterance generated in a dialogue or recommender system, reflects the epistemic state of the system about the validity of alternative expressions. The CA can be assigned globally or locally. In this paper, we present an experimental study on the effects of locally and globally assigned competence in a sales scenario. The results of this study suggest that dialogue systems should include means for modelling global competence and that assigning local competence does not improve the pragmatic competence of a dialogue system.

In this paper we introduce a software design to greatly simplify the elicitation and management of process metadata for researchers. Detailed documentation of a research process not only aids in achieving reproducibility, but also increases usefulness of the documented work for others as a cornerstone of good scientific practice. However, in reality, time pressure together with the lack of simple documentation methods makes documenting workflows an arduous and often neglected task. Our method for a clean process documentation combines benefits of version control with integration into existing institutional infrastructure and a novel schema for describing process metadata.

This paper improves visual representations for multi-modal semantic models, by (i) applying standard dimensionality reduction and denoising techniques, and by (ii) proposing a novel technique that takes corpus-based textual information into account when enhancing visual embeddings. We explore our contribution in a visual and a multi-modal setup and evaluate on benchmark word similarity and relatedness tasks. Our findings show that NMF, denoising as well as perform significantly better than the original vectors or SVD-modified vectors.

In this work we analyse a set of persuasive essays, which were marked and graded with respect to their overall quality. Additionally, we performed a small-scale machine learning experiment incorporating features from the argumentative analysis in order to automatically classify good and bad essays on a four-point scale. Our results indicate that bad essays suffer from more than just incomplete argument structures, which is already visible using simple surface features. We show that good essays distinguish themselves in terms of the amount of argumentative elements (such as major claims, premises, etc.) they use. The results, which have been obtained using a small corpus of essays in German, indicate that information about the argumentative structure of a text is helpful in distinguishing good and bad essays.