Extracting descriptive text from manuscripts to be included in the manuscript metadata is an important task that is generally performed in archives and libraries by experts with a wealth of knowledge on the manuscript...
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ISBN:
(纸本)9783031048814;9783031048807
Extracting descriptive text from manuscripts to be included in the manuscript metadata is an important task that is generally performed in archives and libraries by experts with a wealth of knowledge on the manuscripts contents. Unfortunately, many manuscript collections are so vast that it is not feasible to rely solely on experts to perform this task. To our knowledge, this is the first work aiming at automatic extraction of descriptive text from untranscribed text images. To attempt dealing with such a task, a first step would be to transcribe the handwritten images into text - but achieving sufficiently accurate transcripts is generally unfeasible for large sets of historical manuscripts. We propose new approaches to automatically extract descriptive words which do not rely on any explicit image transcripts. They are based on "probabilistic indexing" , a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty generally exhibited by handwritten text images. We assess the performance of this approach on samples of a large collection of complex manuscripts from the Spanish Archivo General de Indias. Since no standard metrics exist for the novel task considered in this work, we propose two new evaluation measures which aim at measuring the quality of the detected descriptive words in terms close to practical usage of these words. Using these metrics we report promising preliminary results.
Humans have a unique capacity to induce intense emotional states in others by simple acts of verbal communication, and simple messages such as bad can elicit strong emotions in the addressee. However, up to now, resea...
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Humans have a unique capacity to induce intense emotional states in others by simple acts of verbal communication, and simple messages such as bad can elicit strong emotions in the addressee. However, up to now, research has mainly focused on general emotional meaning aspects and paradigms of low personal relevance (e.g., word reading), thereby possibly underestimating the impact of verbal emotion. In the present study, we recorded ERPs while presenting emotional words differing in word-inherent person descriptiveness (in that they may or may not refer to or describe a person;e.g., winner vs. sunflower). We predicted stronger emotional responses to person-descriptive words. Additionally, we enhanced the relevance of the words by embedding them in social-communicative contexts. We observed strong parallels in the characteristics of emotion and descriptiveness effects, suggesting a common underlying motivational basis. Furthermore, word-inherent person descriptiveness affected emotion processing at late elaborate stages reflected in the late positive potential, with emotion effects found only for descriptive words. The present findings underline the importance of factors determining the personal relevance of emotional words.
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