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Despite the great importance of the Latin language in the past, there are relatively few resources available today to develop modern NLP tools for this language. Therefore, the EvaLatin Shared Task for Lemmatization and Part-of-Speech (POS) tagging was published in the LT4HALA workshop. In our work, we dealt with the second EvaLatin task, that is, POS tagging. Since most of the available Latin word embeddings were trained on either few or inaccurate data, we trained several embeddings on better data in the first step. Based on these embeddings, we trained several state-of-the-art taggers and used them as input for an ensemble classifier called LSTMVoter. We were able to achieve the best results for both the cross-genre and the cross-time task (90.64% and 87.00%) without using additional annotated data (closed modality). In the meantime, we further improved the system and achieved even better results (96.91% on classical, 90.87% on cross-genre and 87.35% on cross-time).
In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysis
In an ideal world, extraction of machine-readable data and knowledge from natural-language biodiversity literature would be done automatically, but not so currently. The BIOfid project has developed some tools that can help with important parts of this highly demanding task, while certain parts of the workflow cannot be automated yet. BIOfid focuses on the 20th century legacy literature, a large part of which is only available in printed form. In this workshop, we will present the current state of the art in mobilisation of data from our corpus, as well as some challenges ahead of us. Together with the participants, we will exercise or explain the following tasks (some of which can be performed by the participants themselves, while other tasks currently require execution by our specialists with special equipment): Preparation of text files as an input; pre-processing with TextImager/TextAnnotator; semiautomated annotation and linking of named entities; generation of output in various formats; evaluation of the output. The workshop will also provide an outlook for further developments regarding extraction of statements from natural-language literature, with the long-term aim to produce machine-readable data from literature that can extend biodiversity databases and knowledge graphs.
Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract relevant information. To improve the access to semantic biodiversity information, we have launched the BIOfid project (www.biofid.de) and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. However, to make such a portal work, a couple of methods had to be developed or adapted first. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. Such methods draw on machine learning techniques, which in turn are trained by learning data. To this end, among others, we gathered the BIOfid text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. A special feature of BIOfid is its multiple annotation approach, which takes into account both general and biology-specific classifications, and by this means goes beyond previous, typically taxon- or ontology-driven proper name detection. We describe the design decisions and the genuine Annotation Hub Framework underlying the BIOfid annotations and present agreement results. The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. Finally, some general lessons, in particular with multiple annotation projects, are drawn.
The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23% F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.
In diesem Beitrag untersuchen wir Entwicklungstendenzen von Infrastrukturen in den Digitalen Geisteswissenschaften. Wir argumentieren, dass infolge (1) der Verfügbarkeit von immer mehr Daten über sozial-semiotische Netzwerke, (2) der Methodeninflation in geisteswissenschaftlichen Disziplinen, (3) der zunehmend hybriden Arbeitsteilung zwischen Mensch und Maschine und (4) der explosionsartigen Vermehrung künstlicher Texte ein erheblicher Anpassungsdruck auf die Weiterentwicklung solcher Infrastrukturen entstanden ist. In diesem Zusammenhang beschreiben wir drei Informationssysteme, die sich unter anderem durch die Interaktionsmöglichkeiten unterscheiden, die sie ihren Nutzern bieten, um solchen Herausforderungen zu begegnen. Dabei skizzieren wir mit VienNA eine neuartige Architektur solcher Systeme, welche aufgrund ihrer Flexibilität die Möglichkeit bieten könnte, letztere Herausforderungen zu bewältigen.
Dieses Dokument beschreibt eine Applikation namens Stolperwege, die als prototypische Kommunikationstechnologie für eine mobile Public History of the Holocaust dienen soll, und zwar ausgehend vom Beispiel des Kunstprojekts namens Stolpersteine von Gunter Demnig. Auf diese Weise soll eine zentrale Herausforderung bezogen auf die Vermittlung der Geschichte des Holocaust aufgegriffen werden, welche in der Anknüpfung an die neuesten Entwicklungen von Kommunikationsmedien besteht. Die Stolperwege-App richtet sich an Schülerinnen und Schüler, Bewohnerinnen und Bewohner, Historikerinnen und Historiker und allgemein an Besucherinnen und Besucher einer Stadt, die vor Ort den Spuren des Holocaust nachspüren wollen, um sich an der Schreibung einer Public History of the Holocaust aktiv zu beteiligen.
