The semantic structure of texts can be described both at the local microlevel and at a more global macrolevel. A model for text comprehension based on this notion accounts for the formation of a coherent semantic text base in terms of a cyclical process constrained by limitations of working memory. Furthermore, the model includes macro-operators, whose purpose is to reduce the information in a text base to its gist, that is, the theoretical macrostructure. These operations are under the control of a schema, which is a theoretical formulation of the comprehender's goals. The macroprocesses are predictable only when the control schema can be made explicit. On the production side, the model is concerned with the generation of recall and summarization protocols. This process is partly reproductive and partly constructive, involving the inverse operation of the macro-operators. The model is applied to a paragraph from a psychological research report, and methods for the empirical testing of the model are developed. The main goal of this article is to describe the system of mental operations that underlie the processes occurring in text comprehension and in the production of recall and summariza-tion protocols. A processing model will be outlined that specifies three sets of operations. First, the meaning elements of a text become organized into a coherent whole, a process that results in multiple processing of some elements and, hence, in differential retention. A second set of operations condenses the full meaning of the text into its gist. These processes are complemented by a third set of operations that generate new texts from the memorial consequences of the comprehension processes. These goals involve a number of more concrete objectives. We want first to be able to go through a text, sentence by sentence, specifying the processes that these sentences undergo in comprehension as well as the outputs of these processes at various stages of comprehension. Next, we propose to analyze recall protocols and summaries in the same way and to specify for each sentence the operations required to produce such a sentence. The

In this paper, we report results on answering questions for the reading comprehension task, using a machine learning approach. We evaluated our approach on the Remedia data set, a common data set used in several recent papers on the reading comprehension task. Our learning approach achieves accuracy competitive to previous approaches that rely on handcrafted, deterministic rules and algorithms. To the best of our knowledge, this is the first work that reports that the use of a machine learning approach achieves competitive results on answering questions for reading comprehension tests.

Natural language processing (NLP)-a subfield of linguistics and computer science that concerned with the interactions related to lots of real life problem. In this task it was attempted to deal with one of those problems which allow system to answer all the adhoc based questions from a particular passage that proved the ability of a model to understand the actual objective of any text based context. In order to address this problem, two parameter-reduction techniques to lower memory consumption were presented and the training speed of BERT was increased. For the task of question answering, an ALBERT model was fine-tuned, using the SQuAD dataset as well as prediction was resonated on it. Moreover, the compute_predictions_logits were retrofitted to create the prediction for the aim of simplicity and hence dependencies were minimized. It was therefore outperformed BERT even with less parameters.

As a result of the colonial entanglements of many natural history collections, much of the world’s biodiversity heritage is housed in Europe. Increasingly, natural history institutions have started to address this history. However, computational methods for analysing large collections often consist in static visualisations of collection provenance. We argue that interactive visualisations allow users to understand collections better: their content gaps as well as interesting patterns and trends. Using a dataset containing metadata of five million entries from the botanical collection of Naturalis Biodiversity Center, we created an interactive visualisation with Microsoft PowerBI. The visualisation depicts colonial origins and the botany collection’s movement to the Netherlands over time on an interactive map and timeline. This project thus addresses a gap in historical research on the colonial legacy of Dutch botanical collections, and also a gap in computer science research regarding computational techniques for distant reading of natural heritage data. Our interactive visualisation increases the accessibility of the available scientific data. It also contributes to understanding the cultural history of natural history collections and ultimately, highlights the importance of accurate and accessible visual representations of biodiversity collection histories. This project suggests a way forward for natural history museums grappling with their colonial past.