Semanlink - Transformers ; Attention mechanism ; Favoris ; Knowledge Graphs in NLP ; TheWebConf 2021 ; Unsupervised deep pre-training AND Unsupervised deep pre-training

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Arxiv Doc - Attention + Knowledge Graphs - Discuté avec Raphaël - Entity embeddings - Good - Link Prediction - TheWebConf 2021 -

Transformers ; Attention mechanism ; Favoris ; Knowledge Graphs in NLP ; TheWebConf 2021 ; Unsupervised deep pre-training AND Unsupervised deep pre-training

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[2010.03496] Inductive Entity Representations from Text via Link Prediction

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BLP "BERT for Link Prediction". Central idea: **training an entity encoder with a
link prediction objective** (using the textual descriptions of entities when computing entity representations - hence not failing with entities unknown in training)

> a method for **learning representations
of entities**, that uses a **pre-trained Transformer** based
architecture as an entity encoder, and
**link prediction training on a knowledge graph
with textual entity descriptions**.

> using entity descriptions,
an entity encoder is trained for link prediction in
a knowledge graph. The encoder can then be used
without fine-tuning to obtain features for entity classification
and information retrieval

Cites [Xie et al](doc:2020/10/representation_learning_of_know) and [Kepler](doc:2020/11/1911_06136_kepler_a_unified_). They claim that their
objective targeted exclusively for link prediction (and not an objective that combines language modeling
and link prediction as Kepler)
performs better than Kepler's more complex one.

2020-11-03 About