5,417 research outputs found
FAME: Face Association through Model Evolution
We attack the problem of learning face models for public faces from
weakly-labelled images collected from web through querying a name. The data is
very noisy even after face detection, with several irrelevant faces
corresponding to other people. We propose a novel method, Face Association
through Model Evolution (FAME), that is able to prune the data in an iterative
way, for the face models associated to a name to evolve. The idea is based on
capturing discriminativeness and representativeness of each instance and
eliminating the outliers. The final models are used to classify faces on novel
datasets with possibly different characteristics. On benchmark datasets, our
results are comparable to or better than state-of-the-art studies for the task
of face identification.Comment: Draft version of the stud
Redefining part-of-speech classes with distributional semantic models
This paper studies how word embeddings trained on the British National Corpus
interact with part of speech boundaries. Our work targets the Universal PoS tag
set, which is currently actively being used for annotation of a range of
languages. We experiment with training classifiers for predicting PoS tags for
words based on their embeddings. The results show that the information about
PoS affiliation contained in the distributional vectors allows us to discover
groups of words with distributional patterns that differ from other words of
the same part of speech.
This data often reveals hidden inconsistencies of the annotation process or
guidelines. At the same time, it supports the notion of `soft' or `graded' part
of speech affiliations. Finally, we show that information about PoS is
distributed among dozens of vector components, not limited to only one or two
features
Machine Learning in Automated Text Categorization
The automated categorization (or classification) of texts into predefined
categories has witnessed a booming interest in the last ten years, due to the
increased availability of documents in digital form and the ensuing need to
organize them. In the research community the dominant approach to this problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
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