Semanlink - Data mining AND Information extraction

a language
model with access to information available in a
KB, no assumptions
about the availability of additional components
(such as Named Entity Taggers) or annotations

> While classes of
named entities (e.g., person or location) occur frequently,
each individual name (e.g, Atherton or
Zhouzhuang) may be observed infrequently even
in a very large corpus of text. As a result language
models learn to represent accurately only the most
popular named entities

> knowing that Alice is a name
used to refer to a person should give ample information
about the context in which the word may
occur (e.g., Bob visited Alice).

> ---

> extends a traditional **RNN LM**

> we enhance a traditional LM with a
gating mechanism that controls whether a particular
word is modeled as a general word or as a reference
to an entity
>
> We train the model end-to-end
with only the traditional predictive language modeling
perplexity objective
>
> KALM is trained end-to-end using
a predictive objective on large corpus of text.

> To the best of our knowledge, KALM is the first
unsupervised neural NER approach.

> KALM extends a traditional, RNN-based neural
LM.

2022-08-31 About

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 About

[1909.00426] Global Entity Disambiguation with BERT

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2022-04-18 About

[1910.06294] Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models

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2022-03-31 About

Building Transformer-Based Entity Linking System | by izuna385 | Medium (2021)

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2022-03-23 About

Studio Ousia sur Twitter : "Now using LUKE is easier than ever!" / Twitter

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2022-03-15 About

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 About

Unsupervised_Extractive_Summarization - a Hugging Face Space by Hellisotherpeople

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2021-12-03 About

Link Prediction with Graph Neural Networks and Knowledge Extraction

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2021-09-03 About

[1909.10506] Learning Dense Representations for Entity Retrieval

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> 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 retrieved by approximate nearest
neighbor search. Unlike prior work, **this setup
does not rely on an alias table followed by a
re-ranker, and is thus the first fully learned entity
retrieval model**.

Contributions:

> -  a dual encoder architecture for
learning entity and mention encodings suitable for
retrieval. A key feature of the architecture is that it
employs a modular **hierarchy of sub-encoders that
capture different aspects of mentions and entities**
> - a simple, fully unsupervised **hard negative
mining** strategy that produces massive gains
in retrieval performance, compared to using only
random negatives
> - high
quality candidate entities very efficiently using approximate nearest neighbor search
> - outperforms discrete retrieval
baselines like an alias table or BM25

> strong retrieval
performance across all 5.7 million Wikipedia entities in
around 3ms per mention

> since we are using a two-tower or dual
encoder architecture, **our model cannot use any kind of attention over
both mentions and entities at once**, nor feature-wise
comparisons as done by Francis-Landau et al. (2016).
This is a fairly severe constraint – for example, **we cannot
directly compare the mention span to the entity title**
– but it permits retrieval with nearest neighbor search
for the entire context against a single, all encompassing
representation for each entity

2021-05-01 About

asahi417/tner: Language model finetuning on NER

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2021-03-04 About

kamalkraj/BERT-NER: Pytorch-Named-Entity-Recognition-with-BERT

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2021-02-07 About

[2011.06993] FLERT: Document-Level Features for Named Entity Recognition

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2020-12-01 About

Aran Komatsuzaki sur Twitter : "Big Bird: Transformers for Longer Sequences..."

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2020-07-29 About

[1911.03814] Scalable Zero-shot Entity Linking with Dense Entity Retrieval

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2020-05-02 About

Unsupervised NER using BERT - Hands-on NLP model review - Quora

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2020-03-06 About

[2002.11402] Detecting Potential Topics In News Using BERT, CRF and Wikipedia

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2020-02-27 About

davidsbatista/BREDS: "Bootstrapping Relationship Extractors with Distributional Semantics" (Batista et al., 2015) - code for EMNLP'15 paper

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2020-01-11 About

[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 About

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

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2020-01-09 About

Named Entity Recognition with Bert – Depends on the definition

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2020-01-09 About

How to build deep neural network for custom NER with Keras

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2020-01-07 About

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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2019-12-11 About - Local Copy

[1909.03186] On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

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2019-09-11 About

A Hybrid Graph Model for Distant Supervision Relation Extraction | Springer for Research & Development (ESWC 2019)

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2019-05-30 About

[1601.01343] Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

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2019-01-27 About

Romain Vial (Hyperlex) at Paris NLP meetup, slides

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2019-01-24 About

SIGIR 2018 Tutorial - Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between

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2018-07-09 About

[1404.5367] Lexicon Infused Phrase Embeddings for Named Entity Resolution

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2018-05-22 About

BiLSTM-CNN-CRF Implementation for Sequence Tagging

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2018-05-21 About

Named Entity Recognition and the Road to Deep Learning (2017)

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> the old
and the new-style NLP are not diametrically
opposed: just as it is possible (and useful!) to
incorporate neural-network features into a CRF,
CRFs have influenced some of the best deep
learning models for sequence labelling

This blog post go through the ways of doing NER, starting with CRF:

- When you develop a CRF,
a lot of time goes into finding feature functions (Does a word start with a capital? Is it uppercase? Is it a digit?...) 
- Pb: semantic similarity between words. 
- Gazetteers: lists with names of people, locations and organizations that are known in advance.
- feed Word Embeddings to a CRF: one way is to cluster a set
of word embeddings by distributional similarity, and
provide the CRF with the cluster IDs of a token and its
context words.
- Use word and character based embeddings
- LSTM not good enough -> biLSTM
- **biLSTM predict all labels independently of each other -> add a CRF layer** which outputs a matrix of transition scores between two states: dynamic programming can help find the optimal tag sequence for the sentence

2018-05-20 About

Exploring neural architectures for NER (CS224N 2018)

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2018-04-05 About

Effective Word Representation for Named Entity Recognition (2017)

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2018-03-06 About

Deep learning with word embeddings improves biomedical named entity recognition | Bioinformatics | Oxford Academic (2017)

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2018-03-05 About

[1508.01991] Bidirectional LSTM-CRF Models for Sequence Tagging

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