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With such user interfaces, a structured visual representation of the document contents is presented to the user, who can then manipulate it to control perception and analysis of these contents.
Instead, the retrieval system has the searcher's query and some representation of the document, say an abstract and descriptors, or possibly the full text of the document.
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To this end, we have configured a sub-pipeline of NLP tasks that produces a term-based representation of the documents.
A representation of the documents (and the words) in a 2- or 3-dimensional space allows the results to be visualized, and similarities and differences exposed.
As explained in Section 3.5.2 for the second set of experiments, we use an automatically obtained MeSH representation of the documents.
Abstractive summarization allows an internal representation of the source document so as to produce a faithful summary of the source.
This environment uses a structure called H-SMIL-Net [28] (a temporal extension of the Petri Net structure [29]) for internal representation of the SMIL document, which offers a structured data representation model.
This stage runs a NLP semantic analysis of the documents (hosted in the Wiki), and afterwards merges the semantic annotations with the partial representation of those documents.
This paper presents a platform for the integration of functionalities for the representation of documents, the management of the collections and the multifaceted access to their contents.
This language allows processing values that comply with a data model, which provides a tree representation of the XML documents (Berglund et al. 2010).
Figure 7 Network representation of the topics and documents in the analysed corpus.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com