2,758 research outputs found
Highlighter: automatic highlighting of electronic learning documents
Electronic textual documents are among the most popular teaching content accessible through e-learning platforms. Teachers or learners with different levels of knowledge can access the platform and highlight portions of textual content which are deemed as particularly relevant. The highlighted documents can be shared with the learning community in support of oral lessons or individual learning. However, highlights are often incomplete or unsuitable for learners with different levels of knowledge. This paper addresses the problem of predicting new highlights of partly highlighted electronic learning documents. With the goal of enriching teaching content with additional features, text classification techniques are exploited to automatically analyze portions of documents enriched with manual highlights made by users with different levels of knowledge and to generate ad hoc prediction models. Then, the generated models are applied to the remaining content to suggest highlights. To improve the quality of the learning experience, learners may explore highlights generated by models tailored to different levels of knowledge. We tested the prediction system on real and benchmark documents highlighted by domain experts and we compared the performance of various classifiers in generating highlights. The achieved results demonstrated the high accuracy of the predictions and the applicability of the proposed approach to real teaching documents
A Survey on Event-based News Narrative Extraction
Narratives are fundamental to our understanding of the world, providing us
with a natural structure for knowledge representation over time. Computational
narrative extraction is a subfield of artificial intelligence that makes heavy
use of information retrieval and natural language processing techniques.
Despite the importance of computational narrative extraction, relatively little
scholarly work exists on synthesizing previous research and strategizing future
research in the area. In particular, this article focuses on extracting news
narratives from an event-centric perspective. Extracting narratives from news
data has multiple applications in understanding the evolving information
landscape. This survey presents an extensive study of research in the area of
event-based news narrative extraction. In particular, we screened over 900
articles that yielded 54 relevant articles. These articles are synthesized and
organized by representation model, extraction criteria, and evaluation
approaches. Based on the reviewed studies, we identify recent trends, open
challenges, and potential research lines.Comment: 37 pages, 3 figures, to be published in the journal ACM CSU
Approximate Inference in Continuous Determinantal Point Processes
Determinantal point processes (DPPs) are random point processes well-suited
for modeling repulsion. In machine learning, the focus of DPP-based models has
been on diverse subset selection from a discrete and finite base set. This
discrete setting admits an efficient sampling algorithm based on the
eigendecomposition of the defining kernel matrix. Recently, there has been
growing interest in using DPPs defined on continuous spaces. While the
discrete-DPP sampler extends formally to the continuous case, computationally,
the steps required are not tractable in general. In this paper, we present two
efficient DPP sampling schemes that apply to a wide range of kernel functions:
one based on low rank approximations via Nystrom and random Fourier feature
techniques and another based on Gibbs sampling. We demonstrate the utility of
continuous DPPs in repulsive mixture modeling and synthesizing human poses
spanning activity spaces
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