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Opinion holder extraction is one of the most important tasks in sentiment analysis. We will briefly outline the importance of predicates for this task and categorize them according to part of speech and according to which semantic role they select for the opinion holder. For many languages there do not exist semantic resources from which such predicates can be easily extracted. Therefore, we present alternative corpus-based methods to gain such predicates automatically, including the usage of prototypical opinion holders, i.e. common nouns, denoting for example experts or analysts, which describe particular groups of people whose profession or occupation is to form and express opinions towards specific items.
We explore the feasibility of contextual healthiness classification of food items. We present a detailed analysis of the linguistic phenomena that need to be taken into consideration for this task based on a specially annotated corpus extracted from web forum entries. For automatic classification, we compare a supervised classifier and rule-based classification. Beyond linguistically motivated features that include sentiment information we also consider the prior healthiness of food items.
We investigate the task of detecting reliable statements about food-health relationships from natural language texts. For that purpose, we created a specially annotated web corpus from forum entries discussing the healthiness of certain food items. We examine a set of task-specific features (mostly) based on linguistic insights that are instrumental in finding utterances that are commonly perceived as reliable. These features are incorporated in a supervised classifier and compared against standard features that are widely used for various tasks in natural language processing, such as bag of words, part-of speech and syntactic parse information.
Interested in formally modelling similarity between narratives, we investigate judgements of similarity between narratives in a small corpus of film reviews and book–film comparisons. A main finding is that judgements tend to concern multiple levels of story representation at once. As these texts are pragmatically related to reception contexts, we find many references to reception quality and optimality. We conclude that current formal models of narrative can not capture the task of naturalistic narrative comparisons given in the analysed reviews, but that the development of models containing a more reception-oriented point of view will be necessary.
The understanding of story variation, whether motivated by cultural currents or other factors, is important for applications of formal models of narrative such as story generation or story retrieval. We present the first stage of an experiment to elicit natural narrative variation data suitable for evaluation with respect to story similarity, to qualitative and quantitative analysis of story variation, and also for data processing. We also present few preliminary results from the first stage of the experiment, using Red Riding Hood and Romeo and Juliet as base texts.
With the advent of mobile devices, mediatized political discourse became more dynamic. I assume that the microblog Twitter can be considered as a medium for spatial coordination during protests. Therefore, the case of neo-Nazi demonstrations and counter-protests in the city of Dresden that occurred in February 2012 is analysed. Data consists of microposts that occurred during the event. Quantitative analysis of hashtag and retweet frequencies was performed as well as qualitative speech act pattern analysis and a tempo-spatial discourse analysis on selected subsets of microposts. Results show that a common linguistic practice is verbal georeferencing and by that constructing space. Empirical analysis indicates a strong relation between communicational online space and physical offline place: Protest participants permanently reconfigure spatial context discursively and thus the contested protest area becomes a temporarily meaningful place.
We examine predicative adjectives as an unsupervised criterion to extract subjective adjectives. We do not only compare this criterion with a weakly supervised extraction method but also with gradable adjectives, i.e. another highly subjective subset of adjectives that can be extracted in an unsupervised fashion. In order to prove the robustness of this extraction method, we will evaluate the extraction with the help of two different state-of-the-art sentiment lexicons (as a gold standard).
The perception of prosodic prominence is influenced by different sources like different acoustic cues, linguistic expectations and context. We use a generalized additive model and a random forest to model the perceived prominence on a corpus of spoken German. Both models are able to explain over 80% of the variance. While the random forests give us some insights on the relative importance of the cues, the general additive model gives us insights on the interaction between different cues to prominence.
A frequently replicated finding is that higher frequency words tend to be shorter and contain more strongly reduced vowels. However, little is known about potential differences in the articulatory gestures for high vs. low frequency words. The present study made use of electromagnetic articulography to investigate the production of two German vowels, [i] and [a], embedded in high and low frequency words. We found that word frequency differently affected the production of [i] and [a] at the temporal as well as the gestural level. Higher frequency of use predicted greater acoustic durations for long vowels; reduced durations for short vowels; articulatory trajectories with greater tongue height for [i] and more pronounced downward articulatory trajectories for [a]. These results show that the phonological contrast between short and long vowels is learned better with experience, and challenge both the Smooth Signal Redundancy Hypothesis and current theories of German phonology.