2,523 research outputs found
End-to-end Neural Coreference Resolution
We introduce the first end-to-end coreference resolution model and show that
it significantly outperforms all previous work without using a syntactic parser
or hand-engineered mention detector. The key idea is to directly consider all
spans in a document as potential mentions and learn distributions over possible
antecedents for each. The model computes span embeddings that combine
context-dependent boundary representations with a head-finding attention
mechanism. It is trained to maximize the marginal likelihood of gold antecedent
spans from coreference clusters and is factored to enable aggressive pruning of
potential mentions. Experiments demonstrate state-of-the-art performance, with
a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model
ensemble, despite the fact that this is the first approach to be successfully
trained with no external resources.Comment: Accepted to EMNLP 201
Use Generalized Representations, But Do Not Forget Surface Features
Only a year ago, all state-of-the-art coreference resolvers were using an
extensive amount of surface features. Recently, there was a paradigm shift
towards using word embeddings and deep neural networks, where the use of
surface features is very limited. In this paper, we show that a simple SVM
model with surface features outperforms more complex neural models for
detecting anaphoric mentions. Our analysis suggests that using generalized
representations and surface features have different strength that should be
both taken into account for improving coreference resolution.Comment: CORBON workshop@EACL 201
Distantly Labeling Data for Large Scale Cross-Document Coreference
Cross-document coreference, the problem of resolving entity mentions across
multi-document collections, is crucial to automated knowledge base construction
and data mining tasks. However, the scarcity of large labeled data sets has
hindered supervised machine learning research for this task. In this paper we
develop and demonstrate an approach based on ``distantly-labeling'' a data set
from which we can train a discriminative cross-document coreference model. In
particular we build a dataset of more than a million people mentions extracted
from 3.5 years of New York Times articles, leverage Wikipedia for distant
labeling with a generative model (and measure the reliability of such
labeling); then we train and evaluate a conditional random field coreference
model that has factors on cross-document entities as well as mention-pairs.
This coreference model obtains high accuracy in resolving mentions and entities
that are not present in the training data, indicating applicability to
non-Wikipedia data. Given the large amount of data, our work is also an
exercise demonstrating the scalability of our approach.Comment: 16 pages, submitted to ECML 201
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
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