10,837 research outputs found
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
Global disease monitoring and forecasting with Wikipedia
Infectious disease is a leading threat to public health, economic stability,
and other key social structures. Efforts to mitigate these impacts depend on
accurate and timely monitoring to measure the risk and progress of disease.
Traditional, biologically-focused monitoring techniques are accurate but costly
and slow; in response, new techniques based on social internet data such as
social media and search queries are emerging. These efforts are promising, but
important challenges in the areas of scientific peer review, breadth of
diseases and countries, and forecasting hamper their operational usefulness.
We examine a freely available, open data source for this use: access logs
from the online encyclopedia Wikipedia. Using linear models, language as a
proxy for location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically, our
proof-of-concept yields models with up to 0.92, forecasting value up to
the 28 days tested, and several pairs of models similar enough to suggest that
transferring models from one location to another without re-training is
feasible.
Based on these preliminary results, we close with a research agenda designed
to overcome these challenges and produce a disease monitoring and forecasting
system that is significantly more effective, robust, and globally comprehensive
than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein
and adjust novelty claims accordingly; revise title; various revisions for
clarit
Analysis and Forecasting of Trending Topics in Online Media Streams
Among the vast information available on the web, social media streams capture
what people currently pay attention to and how they feel about certain topics.
Awareness of such trending topics plays a crucial role in multimedia systems
such as trend aware recommendation and automatic vocabulary selection for video
concept detection systems.
Correctly utilizing trending topics requires a better understanding of their
various characteristics in different social media streams. To this end, we
present the first comprehensive study across three major online and social
media streams, Twitter, Google, and Wikipedia, covering thousands of trending
topics during an observation period of an entire year. Our results indicate
that depending on one's requirements one does not necessarily have to turn to
Twitter for information about current events and that some media streams
strongly emphasize content of specific categories. As our second key
contribution, we further present a novel approach for the challenging task of
forecasting the life cycle of trending topics in the very moment they emerge.
Our fully automated approach is based on a nearest neighbor forecasting
technique exploiting our assumption that semantically similar topics exhibit
similar behavior.
We demonstrate on a large-scale dataset of Wikipedia page view statistics
that forecasts by the proposed approach are about 9-48k views closer to the
actual viewing statistics compared to baseline methods and achieve a mean
average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201
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