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Corpora with high-quality linguistic annotations are an essential component in many NLP applications and a valuable resource for linguistic research. For obtaining these annotations, a large amount of manual effort is needed, making the creation of these resources time-consuming and costly. One attempt to speed up the annotation process is to use supervised machine-learning systems to automatically assign (possibly erroneous) labels to the data and ask human annotators to correct them where necessary. However, it is not clear to what extent these automatic pre-annotations are successful in reducing human annotation effort, and what impact they have on the quality of the resulting resource. In this article, we present the results of an experiment in which we assess the usefulness of partial semi-automatic annotation for frame labeling. We investigate the impact of automatic pre-annotation of differing quality on annotation time, consistency and accuracy. While we found no conclusive evidence that it can speed up human annotation, we found that automatic pre-annotation does increase its overall quality.
Zur Erforschung der generationsbedingten Variation im pfälzischen Sprachinseldialekt am Niederrhein
(2012)
This article discusses questions concerning the creation, annotation and sharing of spoken language corpora. We use the Hamburg Map Task Corpus (HAMATAC), a small corpus in which advanced learners of German were recorded solving a map task, as an example to illustrate our main points. We first give an overview of the corpus creation and annotation process including recording, metadata documentation, transcription and semi-automatic annotation of the data. We then discuss the manual annotation of disfluencies as an example case in which many of the typical and challenging problems for data reuse – in particular the reliability of interpretative annotations – are revealed.
Einstellungen und Meinungen prägen das menschliche Handeln; auch die Sprache, die einen zentralen Anker der menschlichen Identität bildet, ist davon betroffen. Der vorliegende Band präsentiert die Ergebnisse eines interdisziplinären Forschungsprojekts zu aktuellen Spracheinstellungen in Deutschland aus sprachwissenschaftlicher und aus sozialpsychologischer Sicht. Mentale Konzepte von Dialekten werden dabei ebenso besprochen wie Bewertungen von Deutsch und anderen Sprachen, Stereotype und Eigen- und Fremdbewertungen.
Des Weiteren wird in einer Sprachstandserhebung die Stellung der deutschen Sprache in Deutschland in der Zusammenschau mehrerer einschlägiger Daten und Statistiken, etwa zur Stellung des Deutschen an Schulen und Hochschulen oder zu deutschsprachigen Medien, dokumentiert.
Der Band bietet damit die bislang erste umfassende Darstellung von Einstellungen zum Deutschen, zu Varietäten des Deutschen, zu anderen Sprachen und zu Sprechern dieser Sprachen und Varietäten.
Knowledge Acquisition with Natural Language Processing in the Food Domain: Potential and Challenges
(2012)
In this paper, we present an outlook on the effectiveness of natural language processing (NLP) in extracting knowledge for the food domain. We identify potential scenarios that we think are particularly suitable for NLP techniques. As a source for extracting knowledge we will highlight the benefits of textual content from social media. Typical methods that we think would be suitable will be discussed. We will also address potential problems and limits that the application of NLP methods may yield.
In this paper, we examine methods to extract different domain-specific relations from the food domain. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. As we need to process a large amount of unlabeled data our methods only require a low level of linguistic processing. This has also the advantage that these methods can provide responses in real time.
In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and the overall polarity of the respective sentences. Layer 2 is concerned with polarity on the word- and phrase-level, annotating both subjective and factual language. The annotations on Layer 3 focus on the expression-level, denoting frames of private states such as objective and direct speech events. These three layers and their respective annotations are intended to be fully independent of each other. At the same time, exploring for and discovering interactions that may exist between different layers should also be possible. The reliability of the respective annotations was assessed using the average pairwise agreement and Fleiss’ multi-rater measures. We believe that MLSA is a beneficial resource for sentiment analysis research, algorithms and applications that focus on the German language.