Reviewing previous studies and adding new paleomagnetic and micropaleontologic data, this paper focuses on the Middle Miocene chronology and stratigraphy in the Dacian Basin area -a critical moment in a critical location -a choking point between the water masses of the Eastern and Central Paratethys. Firstly, it gives a new time-frame for one major tectonic, biologic and basin evolution event -the intra-Sarmatian tectonic phase -that took place in the above mentioned time interval. Secondly, it proposes a revision of the timing for one of the regional sub-stage boundaries of the Middle Miocene in Paratethys (the Volhynian and the Bessarabian). And thirdly, it offers new, reliable magnetic data from four locations that will be further used in studies regarding tectonic rotations in the Carpathian orogenic system.
List of Illustrations List of Contributors Acknowledgements Introduction, Renee van de Vall 1. Leonardo and female interiority, Robert Zwijnenberg 2. Animals inside: anatomy, interiority and virtue in the early modern Dutch Republic, Rina Knoeff 3. Depicting skin: microscopy and the visual articulation of skin interior 1820-1850, Mieneke te Hennepe 4. The mind at work: the visual representation of cerebral processes, Michael Hagner 5. A penny for your thoughts: brain-scans and the mediation of subjective embodiment, Renee van de Vall 6. Transparent bodies: revealing the myth of interiority, Jenny Slatman 7. Looking for a sponge: how a body learns to be affected by ultrasound, Maud Radstake 8. Imagin(in)g pregnancy in Northwest Tanzania: networks, experiences, and translations, Babette Muller-Rockstroh 9. Mediated memories as amalgamations of mind, matter, and culture, Jose van Dijck 10. Intertwined identities, Gail Weiss 11. Framing interiority: portraits in the age of genomics, Miriam van Rijsingen Bibliography Index
We describe a new version of the Dutch word sense disambiguation system trained and tested on a corrected version of the SENSEVAL-2 data. The system is an ensemble of word experts; each word expert is a memory-based classifier of which the parameters are automatically determined through cross-validation on training material. The original best-performing system, which used only local context features for disambiguation, is further refined by performing additional parallel cross-validation experiments for optimizing algorithmic parameters and the amount of local context available to each of the word experts' memory-based kernels. This procedure produces an accuracy of 84.8% on test material, improving on a baseline score of 77.2% and the previous SENSEVAL-2 score of 84.2%. We show that cross-validation overfits; had the local context been held constant at two left and right neighbouring words, the system would have scored 85.0%.
This paper presents a new probabilistic model of information retrieval. The most important modeling assumption made is that documents and queries are defined by an ordered sequence of single terms. This assumption is not made in well known existing models of information retrieval, but is essential in the field of statistical natural language processing. Advances already made in statistical natural language processing will be used in this paper to formulate a probabilistic justification for using tf x idf term weighting. The paper shows that the new probabilistic interpretation of tf x idf term weighting might lead to better understanding of statistical ranking mechanisms, for example by explaining how they relate to coordination level ranking. A pilot experiment on the Cranfield test collection indicates that the presented model outperforms the vector space model with classical tf x idf and cosine length normalisation.
Taking as an example research on cinemagoing in Broadway picture palaces during Jewish holidays and the interpretation of these findings within the larger context of Jewish-American acculturation, this chapter reflects upon digital cinema historiography and the usage of digitized periodicals. Judith Thissen and Paula Eisenstein Baker argue that ephemeral textual traces of film exhibition and audience practices in newspapers, trade journals and fan magazines allow film historians to visualize the historical dynamics of film culture across time and space. Operationalizing a systematic survey of Variety (1905–1940), supplemented by more traditional archival research, this chapter reveals an ethnic practice of cinemagoing that has been long forgotten and also overlooked by film historians.
Automated Essay Scoring has gained a wider applicability and usage with the integration of advanced Natural Language Processing techniques which enabled in-depth analyses of discourse in order capture the specificities of written texts. In this paper, we introduce a novel Automatic Essay Scoring method for Dutch language, built within the Readerbench framework, which encompasses a wide range of textual complexity indices, as well as an automated segmentation approach. Our method was evaluated on a corpus of 173 technical reports automatically split into sections and subsections, thus forming a hierarchical structure on which textual complexity indices were subsequently applied. The stepwise regression model explained 30.5% of the variance in students’ scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1%) whether they are high or low performance students.
We describe our participation in the INEX 2008 Entity Ranking track. We develop a generative language modeling approach for the entity ranking and list completion tasks. Our framework comprises the following components: (i) entity and (ii) query language models, (iii) entity prior, (iv) the probability of an entity for a given category, and (v) the probability of an entity given another entity. We explore various ways of estimating these components, and report on our results. We find that improving the estimation of these components has very positive effects on performance, yet, there is room for further improvements.
Small and medium enterprises (SMEs) are a driving force for innovation. Stimulation of innovation in these SMEs is often the target of policy interventions, both regionally and nationally. Which technical areas should be in the focus and how to identify and monitor them? In this position paper, we propose hybrid AI methods for innovation monitoring, using natural language processing (NLP) and a dynamic knowledge graph that combines learning, reasoning and knowledge sharing in collaboration with innovation experts.