22 research outputs found
Neural Cross-Lingual Entity Linking
A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the English Wikipedia: to compare textual clues across
languages we need to compute similarity between textual fragments across
languages. In this paper, we propose a neural EL model that trains fine-grained
similarities and dissimilarities between the query and candidate document from
multiple perspectives, combined with convolution and tensor networks. Further,
we show that this English-trained system can be applied, in zero-shot learning,
to other languages by making surprisingly effective use of multi-lingual
embeddings. The proposed system has strong empirical evidence yielding
state-of-the-art results in English as well as cross-lingual: Spanish and
Chinese TAC 2015 datasets.Comment: Association for the Advancement of Artificial Intelligence (AAAI),
201
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
Many fundamental problems in natural language processing rely on determining
what entities appear in a given text. Commonly referenced as entity linking,
this step is a fundamental component of many NLP tasks such as text
understanding, automatic summarization, semantic search or machine translation.
Name ambiguity, word polysemy, context dependencies and a heavy-tailed
distribution of entities contribute to the complexity of this problem.
We here propose a probabilistic approach that makes use of an effective
graphical model to perform collective entity disambiguation. Input mentions
(i.e.,~linkable token spans) are disambiguated jointly across an entire
document by combining a document-level prior of entity co-occurrences with
local information captured from mentions and their surrounding context. The
model is based on simple sufficient statistics extracted from data, thus
relying on few parameters to be learned.
Our method does not require extensive feature engineering, nor an expensive
training procedure. We use loopy belief propagation to perform approximate
inference. The low complexity of our model makes this step sufficiently fast
for real-time usage. We demonstrate the accuracy of our approach on a wide
range of benchmark datasets, showing that it matches, and in many cases
outperforms, existing state-of-the-art methods
Named entity disambiguation at scale
Named Entity Disambiguation (NED) is a crucial task in many Natural Language Processing applications such as entity linking, record linkage, knowledge base construction, or relation extraction, to name a few. The task in NED is to map textual variations of a named entity to its formal name. It has been shown that parameterless models for NED do not generalize to other domains very well. On the other hand, parametric learning models do not scale well when the number of formal names expands above the order of thousands or more. To tackle this problem, we propose a deep architecture with superior performance on NED and introduce a strategy to scale it to hundreds of thousands of formal names. Our experiments on several datasets for alias detection demonstrate that our system is capable of obtaining superior results with a large margin compared to other state-of-the-art systems