4,240 research outputs found
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All
Collective entity disambiguation aims to jointly resolve multiple mentions by
linking them to their associated entities in a knowledge base. Previous works
are primarily based on the underlying assumption that entities within the same
document are highly related. However, the extend to which these mentioned
entities are actually connected in reality is rarely studied and therefore
raises interesting research questions. For the first time, we show that the
semantic relationships between the mentioned entities are in fact less dense
than expected. This could be attributed to several reasons such as noise, data
sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE,
a new tree-based objective for the entity disambiguation problem. The key
intuition behind MINTREE is the concept of coherence relaxation which utilizes
the weight of a minimum spanning tree to measure the coherence between
entities. Based on this new objective, we design a novel entity disambiguation
algorithms which we call Pair-Linking. Instead of considering all the given
mentions, Pair-Linking iteratively selects a pair with the highest confidence
at each step for decision making. Via extensive experiments, we show that our
approach is not only more accurate but also surprisingly faster than many
state-of-the-art collective linking algorithms
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc
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
Neural Collective Entity Linking
Entity Linking aims to link entity mentions in texts to knowledge bases, and
neural models have achieved recent success in this task. However, most existing
methods rely on local contexts to resolve entities independently, which may
usually fail due to the data sparsity of local information. To address this
issue, we propose a novel neural model for collective entity linking, named as
NCEL. NCEL applies Graph Convolutional Network to integrate both local
contextual features and global coherence information for entity linking. To
improve the computation efficiency, we approximately perform graph convolution
on a subgraph of adjacent entity mentions instead of those in the entire text.
We further introduce an attention scheme to improve the robustness of NCEL to
data noise and train the model on Wikipedia hyperlinks to avoid overfitting and
domain bias. In experiments, we evaluate NCEL on five publicly available
datasets to verify the linking performance as well as generalization ability.
We also conduct an extensive analysis of time complexity, the impact of key
modules, and qualitative results, which demonstrate the effectiveness and
efficiency of our proposed method.Comment: 12 pages, 3 figures, COLING201
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