27,220 research outputs found
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
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
Joint representation learning of words and entities benefits many NLP tasks,
but has not been well explored in cross-lingual settings. In this paper, we
propose a novel method for joint representation learning of cross-lingual words
and entities. It captures mutually complementary knowledge, and enables
cross-lingual inferences among knowledge bases and texts. Our method does not
require parallel corpora, and automatically generates comparable data via
distant supervision using multi-lingual knowledge bases. We utilize two types
of regularizers to align cross-lingual words and entities, and design knowledge
attention and cross-lingual attention to further reduce noises. We conducted a
series of experiments on three tasks: word translation, entity relatedness, and
cross-lingual entity linking. The results, both qualitatively and
quantitatively, demonstrate the significance of our method.Comment: 11 pages, EMNLP201
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language
processing and information retrieval applications. Prior work often studies
entity relatedness in static settings and an unsupervised manner. However,
entities in real-world are often involved in many different relationships,
consequently entity-relations are very dynamic over time. In this work, we
propose a neural networkbased approach for dynamic entity relatedness,
leveraging the collective attention as supervision. Our model is capable of
learning rich and different entity representations in a joint framework.
Through extensive experiments on large-scale datasets, we demonstrate that our
method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201
- …