40,652 research outputs found
Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events
We introduce a novel iterative approach for event coreference resolution that
gradually builds event clusters by exploiting inter-dependencies among event
mentions within the same chain as well as across event chains. Among event
mentions in the same chain, we distinguish within- and cross-document event
coreference links by using two distinct pairwise classifiers, trained
separately to capture differences in feature distributions of within- and
cross-document event clusters. Our event coreference approach alternates
between WD and CD clustering and combines arguments from both event clusters
after every merge, continuing till no more merge can be made. And then it
performs further merging between event chains that are both closely related to
a set of other chains of events. Experiments on the ECB+ corpus show that our
model outperforms state-of-the-art methods in joint task of WD and CD event
coreference resolution.Comment: EMNLP 201
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
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