12,650 research outputs found
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes
In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
People can recognize scenes across many different modalities beyond natural
images. In this paper, we investigate how to learn cross-modal scene
representations that transfer across modalities. To study this problem, we
introduce a new cross-modal scene dataset. While convolutional neural networks
can categorize cross-modal scenes well, they also learn an intermediate
representation not aligned across modalities, which is undesirable for
cross-modal transfer applications. We present methods to regularize cross-modal
convolutional neural networks so that they have a shared representation that is
agnostic of the modality. Our experiments suggest that our scene representation
can help transfer representations across modalities for retrieval. Moreover,
our visualizations suggest that units emerge in the shared representation that
tend to activate on consistent concepts independently of the modality.Comment: Conference paper at CVPR 201
The horse before the cart: improving the accuracy of taxonomic directions when building tag hierarchies
Content on the Web is huge and constantly growing, and building taxonomies for such content can help with navigation and organisation, but building taxonomies manually is costly and time-consuming. An alternative is to allow users to construct folksonomies: collective social classifications. Yet, folksonomies are inconsistent and their use for searching and browsing is limited. Approaches have been suggested for acquiring implicit hierarchical structures from folksonomies, however, but these approaches suffer from the ‘popularity-generality’ problem, in that popularity is assumed to be a proxy for generality, i.e. high-level taxonomic terms will occur more often than low-level ones. To tackle this problem, we propose in this paper an improved approach. It is based on the Heymann–Benz algorithm, and works by checking the taxonomic directions against a corpus of text. Our results show that popularity works as a proxy for generality in at most 90.91% of cases, but this can be improved to 95.45% using our approach, which should translate to higher-quality tag hierarchy structure
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