MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

@article{Ma2021MuVERIF,
  title={MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations},
  author={Xinyin Ma and Yong Jiang and Nguyen Bach and Tao Wang and Zhongqiang Huang and Fei Huang and Weiming Lu},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.05716}
}
Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are… 

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References

SHOWING 1-10 OF 29 REFERENCES

Improving Zero-Shot Entity Retrieval through Effective Dense Representations

This work proposes a simple approach for improving candidate generation by efficiently embedding mention-entity pairs in dense space through a BERT-based bi-encoder and introduces a new pooling function and incorporate entity type side-information.

Autoregressive Entity Retrieval

Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per article). The ability to retrieve such

Scalable Zero-shot Entity Linking with Dense Entity Retrieval

This paper introduces a simple and effective two-stage approach for zero-shot linking, based on fine-tuned BERT architectures, and shows that it performs well in the non-zero-shot setting, obtaining the state-of-the-art result on TACKBP-2010.

Learning Dense Representations for Entity Retrieval

We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are

Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

A novel embedding method specifically designed for NED that jointly maps words and entities into the same continuous vector space and extends the skip-gram model by using two models.

Zero-Shot Entity Linking by Reading Entity Descriptions

It is shown that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities and proposed domain-adaptive pre-training (DAP) is proposed to address the domain shift problem associated with linking unseen entities in a new domain.

Robust Disambiguation of Named Entities in Text

A robust method for collective disambiguation is presented, by harnessing context from knowledge bases and using a new form of coherence graph that significantly outperforms prior methods in terms of accuracy, with robust behavior across a variety of inputs.

Learning relatedness measures for entity linking

This paper formalizes the problem of learning entity relatedness as a learning-to-rank problem, and proposes a methodology to create reference datasets on the basis of manually annotated data.

Robust named entity disambiguation with random walks

This article presents two novel approaches guided by a natural notion of semantic similarity for the collective disambiguation of all entities mentioned in a document at the same time based on learning-to-rank.

Learning Cross-Context Entity Representations from Text

Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of