19,368 research outputs found
On Identifying Disaster-Related Tweets: Matching-based or Learning-based?
Social media such as tweets are emerging as platforms contributing to
situational awareness during disasters. Information shared on Twitter by both
affected population (e.g., requesting assistance, warning) and those outside
the impact zone (e.g., providing assistance) would help first responders,
decision makers, and the public to understand the situation first-hand.
Effective use of such information requires timely selection and analysis of
tweets that are relevant to a particular disaster. Even though abundant tweets
are promising as a data source, it is challenging to automatically identify
relevant messages since tweet are short and unstructured, resulting to
unsatisfactory classification performance of conventional learning-based
approaches. Thus, we propose a simple yet effective algorithm to identify
relevant messages based on matching keywords and hashtags, and provide a
comparison between matching-based and learning-based approaches. To evaluate
the two approaches, we put them into a framework specifically proposed for
analyzing disaster-related tweets. Analysis results on eleven datasets with
various disaster types show that our technique provides relevant tweets of
higher quality and more interpretable results of sentiment analysis tasks when
compared to learning approach
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Heterogeneous information networks (HINs) are ubiquitous in real-world
applications. In the meantime, network embedding has emerged as a convenient
tool to mine and learn from networked data. As a result, it is of interest to
develop HIN embedding methods. However, the heterogeneity in HINs introduces
not only rich information but also potentially incompatible semantics, which
poses special challenges to embedding learning in HINs. With the intention to
preserve the rich yet potentially incompatible information in HIN embedding, we
propose to study the problem of comprehensive transcription of heterogeneous
information networks. The comprehensive transcription of HINs also provides an
easy-to-use approach to unleash the power of HINs, since it requires no
additional supervision, expertise, or feature engineering. To cope with the
challenges in the comprehensive transcription of HINs, we propose the HEER
algorithm, which embeds HINs via edge representations that are further coupled
with properly-learned heterogeneous metrics. To corroborate the efficacy of
HEER, we conducted experiments on two large-scale real-words datasets with an
edge reconstruction task and multiple case studies. Experiment results
demonstrate the effectiveness of the proposed HEER model and the utility of
edge representations and heterogeneous metrics. The code and data are available
at https://github.com/GentleZhu/HEER.Comment: 10 pages. In Proceedings of the 24th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, London, United Kingdom,
ACM, 201
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