Table 11 also presents the comparison of three different models (i.e., Word2Vec or Para2Vec, LDA and TF-IDF) in generating the vector representations of documents.
To leverage the advantages of feature-based representations of documents, we propose TournaRank, an unsupervised approach inspired by real-life game and sport competition principles.
As the TF-IDF vector representations of documents do not have the ability in capturing the semantic relation between texts, the comparison results between TF-IDF and the other two models can be expected.
In addition to the above baseline, the topic model LDA [5], and TF-IDF [18] are compared to Word2Vec or Para2Vec in generating the vector representations of documents in our experiments.
These global distributions are coarse geographical representations of documented occurrence patterns – bounded by maximum extents – for each species.
Section 3 outlines the proposed repository model, and how it supports the representation of document features.
In this work, we proposed click-words as an alternative representation of document keywords.
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.
Hence, the meaning of an EPR, as a representation of documents, should be transformed into a collaborative environment that supports workflow, enables new care models and allows secure access to distributed health data.
Moreover, the annotations are helpful to model documents/sections of the SAD template that are partially completed (or even empty), or to refine the representation of documents that are not accurately described by their textual contents (e.g., documents that contain many images and little text).
