10,400 research outputs found
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
Connotation Frames: A Data-Driven Investigation
Through a particular choice of a predicate (e.g., "x violated y"), a writer
can subtly connote a range of implied sentiments and presupposed facts about
the entities x and y: (1) writer's perspective: projecting x as an
"antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes
x, (3) effect: something bad happened to y, (4) value: y is something valuable,
and (5) mental state: y is distressed by the event. We introduce connotation
frames as a representation formalism to organize these rich dimensions of
connotation using typed relations. First, we investigate the feasibility of
obtaining connotative labels through crowdsourcing experiments. We then present
models for predicting the connotation frames of verb predicates based on their
distributional word representations and the interplay between different types
of connotative relations. Empirical results confirm that connotation frames can
be induced from various data sources that reflect how people use language and
give rise to the connotative meanings. We conclude with analytical results that
show the potential use of connotation frames for analyzing subtle biases in
online news media.Comment: 11 pages, published in Proceedings of ACL 201
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