The 20 most recently published documents
Traditionally, research on language change has been a post-mortem activity, focused on isolated changes that are complete and often only documented in written texts. In the 1960s the field was advanced considerably by Labovian sociolinguistics and the investigation of “change in progress” adduced through patterns of community-internal linguistic variation correlated with external facts about speakers such as age and class (see Labov 1994 for an overview). However, despite the many benefits of such work on “dynamic synchrony,” we still know relatively little about how language change unfolds over the lifetimes of individual speakers, that is, in real time (cf. Bailey et al. 1991). The logistical challenges of such research are, of course, considerable. Whereas it is straightforward for psycholinguists to observe language development in children over the course of a few years, documenting changes in the verbal behavior of individuals over several decades is by contrast much less feasible. Nevertheless, present theoretical models of language change could be considerably improved by the results of real-time studies.
Berichtet wird aus einem Forschungsprojekt des Instituts für deutsche Sprache, Mannheim, das sich zum Ziel gesetzt hat, Sprachwandel in statu nascendi zu beobachten, den Sprecher und die individuellen Veränderungen seines Sprechens und seiner Einstellung zur Sprache nach Ablauf von etwa vier Jahrzehnten in den Blick zu nehmen. Erneut interviewt werden Sprecher deutscher Dialekte oder Umgangssprachen, die in verschiedenen Forschungsprojekten in den 50er und 60er Jahren aufgenommen wurden und von denen eine Tonbandaufnahme im Deutschen Spracharchiv archiviert ist. Im Rahmen einer dem Forschungsprojekt vorgeschalteten inzwischen abgeschlossenen Pilotstudie wurde ein umfängliches methodisches Instrumentarium erprobt, um aussagekräftiges Vergleichsmaterial und Sprachbiographien einiger ausgewählter Sprecher elizitieren zu können. Auf der Basis dieser Studie werden das Projektdesign und die Analysekategorien für die Hauptuntersuchung festgelegt.
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.
We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict the authors of scientific publications in the same discipline at levels well above chance.
Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy.
In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.
Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
(2010)
In this paper, we explore the effectiveness of bootstrapping supervised machine-learning polarity classifiers using the output of domain-independent rule-based classifiers. The benefit of this method is that no labeled training data are required. Still, this method allows to capture in-domain knowledge by training the supervised classifier on in-domain features, such as bag of words.
We investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. The former addresses the issue in how far relevant constructions for polarity classification, such as word sense disambiguation, negation modeling, or intensification, are important for this self-training approach. We not only compare how this method relates to conventional semi-supervised learning but also examine how it performs under more difficult settings in which classes are not balanced and mixed reviews are included in the dataset.
Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary.
This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.
We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in which a virtual agent assists a customer in a supermarket in finding those products that satisfy their needs best. Moreover, we focus on those relation types that can be extracted from natural language text corpora, ideally content from the internet, such as web forums, that are easy to retrieve. A typical relation type that meets these requirements are pairs of food items that are usually consumed together. Such a relation type could be used by a virtual agent to suggest additional products available in a shop that would potentially complement the items a customer has already in their shopping cart. Our gold standard comprises structural data, i.e. relation tables, which encode relation instances. These tables are vital in order to evaluate natural language processing systems that extract those relations.
Der Beitrag beschäftigt sich mit der Interaktion zwischen blinden und sehenden Personen bei der kooperativen Anfertigung einer Audiodeskription. Eine Audio-deskription ist die verbale Beschreibung visueller Inhalte für Sehbeeinträchtigte und stellt eine Sonderform der Translation dar. Auf der Basis von Videodaten wird die Kooperation eines Dreierteams mit den Verfahren der multimodalen Interaktionsanalyse untersucht. Ein Charakteristikum dieser Kooperation besteht darin, dass eines der Teammitglieder blind ist und die beiden anderen sehen können. Das Erkenntnisinteresse richtet sich besonders auf die professionelle Beteiligung des blinden Teammitglieds an der Interaktion. Die Analyse zeigt, wie Blindheit als Ressource für die kooperative Herstellung der Audiodeskription genutzt wird und wie die Beteiligten in einer visuell asymmetrischen Situation interagieren. Der Beitrag ist eine der seltenen Untersuchungen, die sich mit professioneller Interaktion zwischen Blinden und Sehenden beschäftigen. Er diskutiert Aspekte von genereller Relevanz für die weitere Entwicklung der empirischen Interaktionsforschung, vor allem in Bezug auf eine Erweiterung von Beteiligungsperspektiven in Richtung Inklusion.
Bibliographie
(1980)
Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
(2014)
We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.