9,505 research outputs found
A matter of words: NLP for quality evaluation of Wikipedia medical articles
Automatic quality evaluation of Web information is a task with many fields of
applications and of great relevance, especially in critical domains like the
medical one. We move from the intuition that the quality of content of medical
Web documents is affected by features related with the specific domain. First,
the usage of a specific vocabulary (Domain Informativeness); then, the adoption
of specific codes (like those used in the infoboxes of Wikipedia articles) and
the type of document (e.g., historical and technical ones). In this paper, we
propose to leverage specific domain features to improve the results of the
evaluation of Wikipedia medical articles. In particular, we evaluate the
articles adopting an "actionable" model, whose features are related to the
content of the articles, so that the model can also directly suggest strategies
for improving a given article quality. We rely on Natural Language Processing
(NLP) and dictionaries-based techniques in order to extract the bio-medical
concepts in a text. We prove the effectiveness of our approach by classifying
the medical articles of the Wikipedia Medicine Portal, which have been
previously manually labeled by the Wiki Project team. The results of our
experiments confirm that, by considering domain-oriented features, it is
possible to obtain sensible improvements with respect to existing solutions,
mainly for those articles that other approaches have less correctly classified.
Other than being interesting by their own, the results call for further
research in the area of domain specific features suitable for Web data quality
assessment
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Alexandria: Extensible Framework for Rapid Exploration of Social Media
The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.Comment: 8 page
Using Content Analysis for Privacy Requirement Extraction and Policy Formalization
Abstract: Privacy in cyberspace is a major concern nowadays and enterprises are required to comply with existing privacy regulations and ensure a certain level of privacy for societal and user acceptance. Privacy is also a multidisciplinary and mercury concept, which makes it challenging to define clear privacy requirements and policies to facilitate compliance check and enforcement at the technical level. This paper investigates the potential of using knowledge engineering approaches to transform legal documents to actionable business process models through the extraction of privacy requirements and formalization of privacy policies. The paper features two contributions: A literature review of existing privacy engineering approaches shows that semi-automatic support for extracting and modeling privacy policies from textual documents is often missing. A case study applying content analysis to five guideline documents on implementing privacy-preserving video surveillance systems yields promising first results towards a methodology on semi-automatic extraction and formalization of privacy policies using knowledge engineering approaches
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