37,790 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
Establishing a New State-of-the-Art for French Named Entity Recognition
The French TreeBank developed at the University Paris 7 is the main source of
morphosyntactic and syntactic annotations for French. However, it does not
include explicit information related to named entities, which are among the
most useful information for several natural language processing tasks and
applications. Moreover, no large-scale French corpus with named entity
annotations contain referential information, which complement the type and the
span of each mention with an indication of the entity it refers to. We have
manually annotated the French TreeBank with such information, after an
automatic pre-annotation step. We sketch the underlying annotation guidelines
and we provide a few figures about the resulting annotations
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