Metaphorical patterns in Anthropocene fiction

This article explores metaphorical language in the strand of contemporary fiction that Trexler discusses under the heading of ‘Anthropocene fiction’ – namely, novels that probe the convergence of human experience and geological or climatological processes in times of climate change. Why focus on metaphor? Because, as cognitive linguists working in the wake of Lakoff and Johnson have shown, metaphor plays a key role in closing the gap between everyday, embodied experience and more intangible or abstract realities – including, we suggest, the more-than-human temporal and spatial scales that come to the fore with the Anthropocene. In literary narrative, metaphorical language is typically organized in coherent clusters that amplify the effects of individual metaphors. Based on this assumption, we discuss the results of a systematic coding of metaphorical language in three Anthropocene novels by Margaret Atwood, Jeanette Winterson, and Ian McEwan. We show that the emergent metaphorical patterns enrich and complicate the novels’ staging of the Anthropocene, and that they can destabilize the strict separation between human experience and nonhuman realities

«E-SCIENTROCHAIR»- ONLINE DATABASE FOR MANAGEMENT AND ASSESSMENT OF THE RESEARCH RESOURCES OF THE UNIVERSITY BASIS UNIT – THE CHAIR

The concept named e-ScientRoChair proposes searching for new informing and documentation opportunities, on fundamental structure in academic scientific research, meaning the chair or the research team, anabling the possibility to publish and as well as toOnline Database, Chair, Scientific Exchange, Scientific Research Components

ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing

In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).Comment: In Proceedings of EACL 201