Semanlink - sl:creationDate : 2020-01-09

Three years after the W3C approved a DRM standard, it's no longer possible to make a functional indie browser / Boing Boing

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2020-01-09 A propos

[1711.00046] Replace or Retrieve Keywords In Documents at Scale

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2020-01-09 A propos

A Joint Model for Entity Analysis: Coreference, Typing, and Linking (Greg Durrett, Dan Klein 2014)

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2020-01-09 A propos

Facebook paid Teen Vogue to run a fake article praising Facebook for "helping ensure the integrity of the 2020 election" / Boing Boing

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2020-01-09 A propos

[2003.05473] Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking (CoNNL 2019)

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Training BERT-base-uncased on English Wikipedia and then fine-tuned and evaluating it
on an entity linking (EL) benchmark (EL implemented as a token classification over the entity vocabulary)

> BERT+Entity is a straightforward extension on top
of BERT, i.e. we initialize BERT with the publicly
available weights from the BERT-base-uncased
model and add an output classification layer on
top of the architecture. Given a contextualized token,
the classifier computes the probability of an
entity link for each entry in the entity vocabulary.

Can BERT’s architecture learn all entity
linking steps jointly? To answer:

> an extreme
simplification of the **entity linking setup that
works surprisingly well**: simply cast it as **a
per token classification over the entire entity
vocabulary** (over 700K classes in our case).

> the model
is the first that performs entity linking without any
pipeline or any heuristics, compared to all prior
approaches. We found that with our approach we
can learn additional entity knowledge in BERT that
helps in entity linking. **However, we also found
that almost none of the downstream tasks really
required entity knowledge**.

### Related work

- > [Durrett and Klein (2014)](/doc/2020/01/a_joint_model_for_entity_analys) were the first to propose
jointly modelling Mention detection, Candidate generation and Entity disambiguation in a graphical
model and could show that each of those steps are
interdependent and benefit from a joint objective

This paper uses neural techniques instead of CRF.

- > [Yamada](/showprop.do?pptyuri=http%3A%2F%2Fwww.semanlink.net%2F2001%2F00%2Fsemanlink-schema%23arxiv_author&pptyval=Ikuya%2BYamada) (2016, 2017) was the first to
investigate neural text representations and entity
linking, but their approach is limited to ED.

cf. [#Wikipedia2Vec](tag:wikipedia2vec). Compare with [newer work by Yamada](doc:2020/09/1909_01259_neural_attentive_b)

2020-01-09 A propos

[2001.01447] Improving Entity Linking by Modeling Latent Entity Type Information

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2020-01-09 A propos

Interpretable Named entity recognition with keras and LIME – Depends on the definition

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2020-01-09 A propos

Named Entity Recognition with Bert – Depends on the definition

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