^This paper describes DeReKo (Deutsches Referenzkorpus), the Archive of General Reference Corpora of Contemporary Written German at the Institut fur Deutsche Sprache (IDS) in Mannheim, and the rationale behind its development. We discuss its design, its legal background, how to access it, available metadata, linguistic annotation layers, underlying standards, ongoing developments, and aspects of using the archive for empirical linguistic research. The focus of the paper is on the advantages of DEREKO’s design as a primordial sample from which virtual corpora can be drawn for the specific purposes of individual studies. Both concepts, primordial sample and virtual corpus are explained and illustrated in detail. Furthermore, we describe in more detail how DEREKO deals with the fact that all its texts are subject to third parties’ intellectual property rights, and how it deals with the issue of replicability, which is particularly challenging given DEREKO’s dynamic growth and the possibility to construct from it an open number of virtual corpora.

/pdf/the-german-reference-corpus-dereko-a-primordial-sample-for-32is4fg3sx.pdf

The german reference corpus DeReKo : a primordial sample for linguistic research

Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m e {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually.

Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data

We shed new light on the authenticity of the writings of Julius Caesar.Hirtius, one of Caesar's generals, must have contributed to Caesar's writings.We benchmark two authorship verification systems on publicly available data sets.We test on both modern data sets, and Latin texts from Antiquity.We show how computational methods inform traditional authentication studies. In this paper, we shed new light on the authenticity of the Corpus Caesarianum, a group of five commentaries describing the campaigns of Julius Caesar (100-44 BC), the founder of the Roman empire. While Caesar himself has authored at least part of these commentaries, the authorship of the rest of the texts remains a puzzle that has persisted for nineteen centuries. In particular, the role of Caesar's general Aulus Hirtius, who has claimed a role in shaping the corpus, has remained in contention. Determining the authorship of documents is an increasingly important authentication problem in information and computer science, with valuable applications, ranging from the domain of art history to counter-terrorism research. We describe two state-of-the-art authorship verification systems and benchmark them on 6 present-day evaluation corpora, as well as a Latin benchmark dataset. Regarding Caesar's writings, our analyses allow us to establish that Hirtius's claims to part of the corpus must be considered legitimate. We thus demonstrate how computational methods constitute a valuable methodological complement to traditional, expert-based approaches to document authentication.

https://pure.knaw.nl/ws/files/2895651/caesar_author.pdf

Authenticating the writings of Julius Caesar

http://kclpure.kcl.ac.uk/portal/files/6253556/BlankeHedges_Humanities_eScience_FGCS_1.pdf

Scholarly primitives

This paper presents the development of an open-source morphology tool for German integrated into a grid-based environment. Departing from the SFST-based SMOR tools (Schmid et al. [1]), we have implemented a minimal lexicon component that works in tandem with the morphological tool. Tests on a list of 30,000 high-frequency German words show that the recognition rate is comparable to other systems with even larger lexicons. Additional tools for the management of lexical data and services built on top of the finite-state transducer are also integrated as web services in the grid, so that all resources can be shared easily among lexicographers, linguists, and finite-state developers.

Morphisto --An Open Source Morphological Analyzer for German

This article builds on a mathematical explanation of one the most prominent stylometric measures, Burrows’s Delta (and its variants), to understand and explain its working. Starting with the conceptual separation between feature selection, feature scaling, and distance measures, we have designed a series of controlled experiments in which we used the kind of feature scaling (various types of standardization and normalization) and the type of distance measures (notably Manhattan, Euclidean, and Cosine) as independent variables and the correct authorship attributions as the dependent variable indicative of the performance of each of the methods proposed. In this way, we are able to describe in some detail how each of these two variables interact with each other and how they influence the results. Thus we can show that feature vector normalization, that is, the transformation of the feature vectors to a uniform length of 1 (implicit in the cosine measure), is the decisive factor for the improvement of Delta proposed recently. We are also able to show that the information particularly relevant to the identification of the author of a text lies in the profile of deviation across the most frequent words rather than in the extent of the deviation or in the deviation of specific words only. .................................................................................................................................................................................

/pdf/understanding-and-explaining-delta-measures-for-authorship-31t4c5qlc6.pdf

Understanding and explaining Delta measures for authorship attribution

TextGrid is a new Grid project in the framework of the German D-Grid initiative, with the aim to deploy Grid technologies for humanities scholars working on historical (German) texts. Its two roots, humanities computing and eScience (Grid computing used by research together with modern communication technologies), are the basis for TextGrid to provide pioneer work in eHumanities. After summarizing Humanities Computing and modern network technologies, community expectations in the fields of philological edition and other application areas are set forth, from which functional requirements such as modularity, distribution, etc. are distilled. The first version of the TextGrid architecture was designed in accordance with these requirements, and focuses on openness by standard conformance and encapsulation. It provides storage Grid services via a pure Web Services interface to dedicated Web Services tools for different aspects of text processing, analysis and retrieval. This platform aims to provide easily usable tools for scholars, but also specifies interfaces for external program developers to add functionality.

/pdf/textgrid-and-ehumanities-4ude31x8ci.pdf

TextGrid and eHumanities

Burrows’s Delta is the most established measure for stylometric difference in literary authorship attribution. Several improvements on the original Delta have been proposed. However, a recent empirical study showed that none of the proposed variants constitute a major improvement in terms of authorship attribution performance. With this paper, we try to improve our understanding of how and why these text distance measures work for authorship attribution. We evaluate the effects of standardization and vector normalization on the statistical distributions of features and the resulting text clustering quality. Furthermore, we explore supervised selection of discriminant words as a procedure for further improving authorship attribution.

/pdf/towards-a-better-understanding-of-burrows-s-delta-in-4isu4h5wcc.pdf

Towards a better understanding of Burrows's Delta in literary authorship attribution

Authorship Attribution is a research area in quantitative text analysis concerned with attributing texts of unknown or disputed authorship to their actual author based on quantitatively measured linguistic evidence (see Juola 2006; Stamatatos 2009; Koppel et al. 2009). Authorship attribution has applications in literary studies, history, forensics and many other fields, e.g. corpus stylistics (Oakes 2009). The fundamental assumption in authorship attribution is that individuals have idiosyncratic habits of language use, leading to a stylistic similarity of texts written by the same person. Many of these stylistic habits can be measured by assessing the relative frequencies of function words or parts of speech, vocabulary richness, and many other linguistic features. Distance metrics between the resulting feature vectors indicate the overall similarity of texts to each other, and can be used for attributing a text of unknown authorship to the most similar of a (usually closed) set of candidate authors. The aim of this paper is to present findings from a larger investigation of authorship attribution methods which centres around the following questions: (a) How and why exactly does authorship attribution based on distance measures work? (b) Why do different distance measures and normalization strategies perform differently? (c) Specifically, why do they perform differently for different languages and language families, and (d) How can such knowledge be used to improve authorship attribution methods? First, we describe current issues in authorship attribution and contextualize our own work. Second, we report some of our earlier research into the question. Then, we present our most recent investigation, which pertains to the effects of normalization methods and distance measures in different languages, describing our aims, data and methods. We conclude with a summary of our results.

/pdf/explaining-delta-or-how-do-distance-measures-for-authorship-5gr29w3wrk.pdf

Thorsten Vitt

Papers

Understanding and explaining Delta measures for authorship attribution

TextGrid and eHumanities

Towards a better understanding of Burrows's Delta in literary authorship attribution

Explaining Delta, or: How do distance measures for authorship attribution work?

Outliers or Key Profiles? Understanding Distance Measures for Authorship Attribution.