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Linguistic relativists have traditionally asked 'how language influences thought', but conversation analysts and anthropological linguists have moved the focus from thought to social action. We argue that 'social action' should in this context not become simply a new dependent variable, because the formulation 'does language influence action' suggests that social action would already be meaningfully constituted prior to its local (verbal and multi-modal) accomplishment. We draw on work by the gestalt psychologist Karl Duncker to show that close attention to action-in-a-situation helps us ground empirical work on cross-cultural diversity in an appreciation of the invariances that make culture-specific elements of practice meaningful.
Novel formats of construction-based description hold great potential for phenomena that fall through the cracks in traditional kinds of linguistic reference works. On the example of German verb argument structure constructions with a prepositional object, we demonstrate that a construction-based description of such phenomena is superior to existing lexicographic and grammaticographic treatments, but that it also poses a number of new problems. The most fundamental of these relates to the fact that construction-based analyses can be proposed on different levels of abstraction. We illustrate pertinent problems relating to the precise identification of constructional form and meaning and suggest a multi-layered descriptive format for web-based electronic reference constructica that can accommodate these challenges. Semantically, the proposed solution integrates both lumping and splitting perspectives on constructional grain size and permits users to flexibly zoom in and out on individual elements in the resource. Formally, it can capture variation in the number and marking of realised arguments as found in e.g. passives and transitivity alternations. Aspects of the theoretical controversy between Construction Grammar and Valency Theory are addressed where relevant, but our focus is on questions of description and the practical implementation of construction-based analyses in a suitable type of linguistic reference work.
Smooth turn-taking in conversation depends in part on speakers being able to communicate their intention to hold or cede the floor. Both prosodic and gestural cues have been shown to be used in this context. We investigate the interplay of pitch movements and hand gestures at locations at which speaker change becomes relevant, comparing their use in German and Swedish. We find that there are some shared functions of prosody and gesture with regard to turn-taking in the two languages, but that these shared functions appear to be mediated by the different phonological demands on pitch in the two languages.
Looking at gestures as a means for communication, they can serve conversational participants at several levels. As co-speech gestures, they can add information to the verbally expressed content and they can serve to manage turn-taking. In order to look closer at the interplay between these resources in face-to face conversation, we annotated hand gestures, syntactic completion points and the related turn-organisation, and measured the timing of gesture strokes and their lexical/phrasal referent. In a case study on German, we observe the trend that speakers vary less in gesturelexis on- and offsets when keeping the turn after syntactic completions than at speaker changes, backchannel or other locations of a conversation. This indicates that timing properties of non-verbal cues interact with verbal cues to manage turn-taking.
We examine the new task of detecting derogatory compounds (e.g. curry muncher). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. idiot) since they are more sparsely represented in lexical resources previously found effective for this task (e.g. Wiktionary). We propose an unsupervised classification approach that incorporates linguistic properties of compounds. It mostly depends on a simple distributional representation. We compare our approach against previously established methods proposed for extracting derogatory unigrams.
We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.
We present a descriptive analysis on the two datasets from the shared task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS), the only existing German dataset for opinion role extraction of its size. Our analysis discusses the individual properties of the three components, subjective expressions, sources and targets and their relations towards each other. Our observations should help practitioners and researchers when building a system to extract opinion roles from German data.
A Supervised learning approach for the extraction of opinion sources and targets from German text
(2019)
We present the first systematic supervised learning approach for the extraction of opinion sources and targets on German language data. A wide choice of different features is presented, particularly syntactic features and generalization features. We point out specific differences between opinion sources and targets. Moreover, we explain why implicit sources can be extracted even with fairly generic features. In order to ensure comparability our classifier is trained and tested on the dataset of the STEPS shared task.