To detect how and when readers are experiencing engagement with a literary work, we bring together empirical literary studies and language technology via focusing on the affective state of absorption. The goal of our resource development is to enable the detection of different levels of reading absorption in millions of user-generated reviews hosted on social reading platforms. We present a corpus of social book reviews in English that we annotated with reading absorption categories. Based on these data, we performed supervised, sentence level, binary classification of the explicit presence vs. absence of the mental state of absorption. We compared the performances of classical machine learners where features comprised sentence representations obtained from a pretrained embedding model (Universal Sentence Encoder) vs. neural classifiers in which sentence embedding vector representations are adapted or fine-tuned while training for the absorption recognition task. We discuss the challenges in creating the labeled data as well as the possibilities for releasing a benchmark corpus.

Based on the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Falling back on the distributional hypothesis to statistically model word meaning, we used evolving fields as a metaphor to express time-dependent changes in a vector space model by a combination of random indexing and evolving self-organizing maps (ESOM). To monitor semantic drifts within the observation period, an experiment was carried out on the term space of a collection of 12.8 million Amazon book reviews. For evaluation, the semantic consistency of ESOM term clusters was compared with their respective neighbourhoods in WordNet, and contrasted with distances among term vectors by random indexing. We found that at 0.05 level of significance, the terms in the clusters showed a high level of semantic consistency. Tracking the drift of distributional patterns in the term space across time periods, we found that consistency decreased, but not at a statistically significant level. Our method is highly scalable, with interpretations in philosophy.

Based on real‐world user demands, we demonstrate how animated visualization of evolving text corpora displays the underlying dynamics of semantic content. To interpret the results, one needs a dynamic theory of word meaning. We suggest that conceptual dynamics as the interaction between kinds of intellectual and emotional content and language is key for such a theory. We demonstrate our method by two‐way seriation, which is a popular technique to analyze groups of similar instances and their features as well as the connections between the groups themselves. The two‐way seriated data may be visualized as a two‐dimensional heat map or as a three‐dimensional landscape in which color codes or height correspond to the values in the matrix. In this article, we focus on two‐way seriation of sparse data in the Reuters‐21568 test collection. To achieve a meaningful visualization, we introduce a compactly supported convolution kernel similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high‐dimensional sparse space with values that interpolate nearby elements and provides insight into the clustering structure. We also extend two‐way seriation to deal with online updates of both the row and column spaces and, combined with the convolution kernel, demonstrate a three‐dimensional visualization of dynamics.

With the emergence of high-performance computing instances in the cloud, massive scale computations have become available to technically every organization. Digital libraries typically employ a data-intensive infrastructure, but given the resources, advanced services based on data and text mining could be developed. A fundamental issue is the ease of development and integration of such services. We demonstrate the feasibility by providing a case study on a visual machine learning algorithm with MapReduce running in the cloud in a small cluster.

No abstract available.