Linking pattern to process in cultural evolution: explaining material culture diversity among the Northern Khanty of Northwest Siberia

Book description: This volume offers an integrative approach to the application of evolutionary theory in studies of cultural transmission and social evolution and reveals the enormous range of ways in which Darwinian ideas can lead to productive empirical research, the touchstone of any worthwhile theoretical perspective. While many recent works on cultural evolution adopt a specific theoretical framework, such as dual inheritance theory or human behavioral ecology, Pattern and Process in Cultural Evolution emphasizes empirical analysis and includes authors who employ a range of backgrounds and methods to address aspects of culture from an evolutionary perspective. Editor Stephen Shennan has assembled archaeologists, evolutionary theorists, and ethnographers, whose essays cover a broad range of time periods, localities, cultural groups, and artifacts

Towards a corpus-based, statistical approach of translation quality : measuring and visualizing linguistic deviance in student translations

In this article we present a corpus-based statistical approach to measuring translation quality, more particularly translation acceptability, by comparing the features of translated and original texts. We discuss initial findings that aim to support and objectify formative quality assessment. To that end, we extract a multitude of linguistic and textual features from both student and professional translation corpora that consist of many different translations by several translators in two different genres (fiction, news) and in two translation directions (English to French and French to Dutch). The numerical information gathered from these corpora is exploratively analysed with Principal Component Analysis, which enables us to identify stable, language-independent linguistic and textual indicators of student translations compared to translations produced by professionals. The differences between these types of translation are subsequently tested by means of ANOVA. The results clearly indicate that the proposed methodology is indeed capable of distinguishing between student and professional translations. It is claimed that this deviant behaviour indicates an overall lower translation quality in student translations: student translations tend to score lower at the acceptability level, that is, they deviate significantly from target-language norms and conventions. In addition, the proposed methodology is capable of assessing the acceptability of an individual student’s translation – a smaller linguistic distance between a given student translation and the norm set by the professional translations correlates with higher quality. The methodology is also able to provide objective and concrete feedback about the divergent linguistic dimensions in their text

The Shape of Art History in the Eyes of the Machine

How does the machine classify styles in art? And how does it relate to art historians' methods for analyzing style? Several studies have shown the ability of the machine to learn and predict style categories, such as Renaissance, Baroque, Impressionism, etc., from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through its visual analysis. However, such a representation is not necessarily interpretable. We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 77K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history. Surprisingly, the networks could place the works of art in a smooth temporal arrangement mainly based on learning style labels, without any a priori knowledge of time of creation, the historical time and context of styles, or relations between styles. The learned representations showed that there are few underlying factors that explain the visual variations of style in art. Some of these factors were found to correlate with style patterns suggested by Heinrich W\"olfflin (1846-1945). The learned representations also consistently highlighted certain artists as the extreme distinctive representative of their styles, which quantitatively confirms art historian observations

Stylistic variation over 200 years of court proceedings according to gender and social class

We present an approach to detect stylistic variation across social variables (here: gender and social class), considering also diachronic change in language use. For detection of stylistic variation, we use relative entropy, measuring the difference between probability distributions at different linguistic levels (here: lexis and grammar). In addition, by relative entropy, we can determine which linguistic units are related to stylistic variation.This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) under grants SFB1102: Information Density and Linguistic Encoding (www.sfb1102.uni-saarland.de) and the start-up grant for research projects from Saarland University

Using relative entropy for detection and analysis of periods of diachronic linguistic change

We present a data-driven approach to detect periods of linguistic change and the lexical and grammatical features contributing to change. We focus on the development of scientific English in the late modern period. Our approach is based on relative entropy (Kullback-Leibler Divergence) comparing temporally adjacent periods and sliding over the time line from past to present. Using a diachronic corpus of scientific publications of the Royal Society of London, we show how periods of change reflect the interplay between lexis and grammar, where periods of lexical expansion are typically followed by periods of grammatical consolidation resulting in a balance between expressivity and communicative efficiency. Our method is generic and can be applied to other data sets, languages and time ranges.This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) under grants SFB1102: Information Density and Linguistic Encoding (www.sfb1102.uni-saarland.de) and EXC 284: Multimodal Computing and Interaction (www.mmci.uni-saarland.de)

Modeling intra-textual variation with entropy and surprisal: topical vs. stylistic patterns

We present a data-driven approach to investigate intra-textual variation by combining entropy and surprisal. With this approach we detect linguistic variation based on phrasal lexico-grammatical patterns across sections of research articles. Entropy is used to detect patterns typical of specific sections. Surprisal is used to differentiate between more and less informationally-loaded patterns as well as type of information (topical vs. stylistic). While we here focus on research articles in biology/genetics, the methodology is especially interesting for digital humanities scholars, as it can be applied to any text type or domain and combined with additional variables (e.g. time, author or social group).This work is funded by Deutsche Forschungsgemeinschaft (DFG) under grants SFB 1102: Information Density and Linguistic Encoding (www.sfb1102.uni-saarland.de) and EXC 284: Multimodal Computing and Interaction (www.mmci.uni-saarland.de)