The present study investigates the prosody of information-seeking (ISQs) and rhetorical questions (RQs) in Standard Chinese, in polar and wh-questions. Like in other languages, ISQs and RQs in Standard Chinese can have the same surface structure, allowing for a direct prosodic comparison between illocution types (ISQ vs RQ). Since Standard Chinese has lexical tone, the use of f0 as a cue to illocution type may be restricted. We investigate the prosodic differences between ISQs and RQs as well as the interplay of prosodic cues to RQs. In terms of f0, results showed that RQs were lower in f0, with the f0 range on the first word being expanded followed by f0 compression. RQs were further longer in duration and more often realized with non-modal voice quality (glottalized voice) as compared to ISQs. These prosodic cues were largely manipulated in tandem (illocutionary pairs with larger durational differences also showed larger differences in mean f0; voice quality, in turn, seemed to be an additional cue). We suggest three possible explanations (assertive force, focus, speaker attitude) that unite the present findings on RQs in Standard Chinese with the findings on RQs in other, non-tonal languages.

Internet-Memes sind ein globales, populäres Medium, oft und zumeist unproblematisiert rezipiert und in ihrer Komik meist nur fragmentarisch, auf bestimmte Aspekte fokussiert analysiert. Die vorliegende Arbeit bemüht sich um eine möglichst umfassende Darstellung der Komik in Memes basierend auf klassischen und modernen Komikkategorien. Auf Grundlage einer umfassend-kritischen Synthese der vorliegenden Fachliteratur und eines präzisen Analysemodells kann so eine begründete Diskussion über memetische Komik, ihre Funktionen und ihre positiven wie problematischen Aspekte geführt werden.

While humans find it easy to process visual information from the real world, machines struggle with this task due to the unstructured and complex nature of the information. Computer vision (CV) is the approach of artificial intelligence that attempts to automatically analyze, interpret, and extract such information. Recent CV approaches mainly use deep learning (DL) due to its very high accuracy. DL extracts useful features from unstructured images in a training dataset to use them for specific real-world tasks. However, DL requires a large number of parameters, computational power, and meaningful training data, which can be noisy, sparse, and incomplete for specific domains. Furthermore, DL tends to learn correlations from the training data that do not occur in reality, making DNNs poorly generalizable and error-prone. Therefore, the field of visual transfer learning is seeking methods that are less dependent on training data and are thus more applicable in the constantly changing world. One idea is to enrich DL with prior knowledge. Knowledge graphs (KG) serve as a powerful tool for this purpose because they can formalize and organize prior knowledge based on an underlying ontological schema. They contain symbolic operations such as logic, rules, and reasoning, and can be created, adapted, and interpreted by domain experts. Due to the abstraction potential of symbols, KGs provide good prerequisites for generalizing their knowledge. To take advantage of the generalization properties of KG and the ability of DL to learn from large-scale unstructured data, attempts have long been made to combine explicit graph and implicit vector representations. However, with the recent development of knowledge graph embedding methods, where a graph is transferred into a vector space, new perspectives for a combination in vector space are opening up. In this work, we attempt to combine prior knowledge from a KG with DL to improve visual transfer learning using the following steps: First, we explore the potential benefits of using prior knowledge encoded in a KG for DL-based visual transfer learning. Second, we investigate approaches that already combine KG and DL and create a categorization based on their general idea of knowledge integration. Third, we propose a novel method for the specific category of using the knowledge graph as a trainer, where a DNN is trained to adapt to a representation given by prior knowledge of a KG. Fourth, we extend the proposed method by extracting relevant context in the form of a subgraph of the KG to investigate the relationship between prior knowledge and performance on a specific CV task. In summary, this work provides deep insights into the combination of KG and DL, with the goal of making DL approaches more generalizable, more efficient, and more interpretable through prior knowledge.

This study critically investigates the U.S.-American Western and its construction of sexuality and gender, revealing that the heteronormative matrix that is upheld and defended in the genre is consistently preceded by the exploration of alternative sexualities and ways to think gender beyond the binary. The endeavor to naturalize heterosexuality seems to be baked in the formula of the U.S.-Western. However, as I show in this study, this endeavor relies on an aporia, because the U.S.-Western can only ever attempt to naturalize gender by constructing it first, hence inevitably and simultaneously construct evidence that supports the opposite: the unnaturalness and contingency of gender and sexuality. My study relies on the works of Raewyn Connell, Pierre Bourdieu, and Judith Butler, and amalgamates in its methodology established approaches from film and literary studies (i.e., close readings) with a Foucaultian understanding of discourse and discourse analysis, which allows me to relate individual texts to cultural, socio-political and economical contexts that invariably informed the production and reception of any filmic text. In an analysis of 14 U.S.-Westerns (excluding three excursions) that appeared between 1903 and 1969 I give ample and minute narrative and film-aesthetical evidence to reveal the complex and contradictory construction of gender and sexuality in the U.S.-Western, aiming to reveal both the normative power of those categories and its structural instability and inconsistency. This study proofs that the Western up until 1969 did not find a stable pattern to represent the gender binary. The U.S.-Western is not necessarily always looking to confirm or stabilize governing constructs of (gendered) power. However, it without fail explores and negotiates its legitimacy. Heterosexuality and male hegemony are never natural, self-evident, incontestable, or preordained. Quite conversely: the U.S.-Western repeatedly – and in a surprisingly diverse and versatile way – reveals the illogical constructedness of the heteronormative matrix. My study therefore offers a fresh perspective on the genre and shows that the critical exploration and negotiation of the legitimacy of heteronormativity as a way to organize society is constitutive for the U.S.-Western. It is the inquiry – not necessarily the affirmation – of the legitimacy of this model that gives the U.S.-Western its ideological currency and significance as an artifact of U.S.-American popular culture.

Stress position in English words is well-known to correlate with both their morphological properties and their phonological organisation in terms of non-segmental, prosodic categories like syllable structure. While two generalisations capturing this correlation, directionality and stratification, are well established, the exact nature of the interaction of phonological and morphological factors in English stress assignment is a much debated issue in the literature. The present study investigates if and how directionality and stratification effects in English can be learned by means of Naive Discriminative Learning, a computational model that is trained using error-driven learning and that does not make any a-priori assumptions about the higher-level phonological organisation and morphological structure of words. Based on a series of simulation studies we show that neither directionality nor stratification need to be stipulated as a-priori properties of words or constraints in the lexicon. Stress can be learned solely on the basis of very flat word representations. Morphological stratification emerges as an effect of the model learning that informativity with regard to stress position is unevenly distributed across all trigrams constituting a word. Morphological affix classes like stress-preserving and stress-shifting affixes are, hence, not predefined classes but sets of trigrams that have similar informativity values with regard to stress position. Directionality, by contrast, emerges as spurious in our simulations; no syllable counting or recourse to abstract prosodic representations seems to be necessary to learn stress position in English.