Semanlink - Favoris ; Entities AND NN 4 NLP

[Ikuya Yamada sur Twitter : "Is entity representation effective to improve multilingual language models?..."](doc:2022/04/ikuya_yamada_sur_twitter_is_)

> Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, **existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks**. In this study, we explore the **effectiveness of leveraging entity representations for downstream cross-lingual tasks**.
>
> the key insight is that incorporating entity representations into the input allows us to extract more language-agnostic features.

[Github](https://github.com/studio-ousia/luke)

> Entity representations are known to enhance
language models in mono-lingual settings
(Zhang et al., 2019: [ERNIE](tag:ernie.html); Peters et al., 2019:  [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced); Wang et al.,
2021 [[1911.06136] KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](doc:2020/11/1911_06136_kepler_a_unified_); Xiong et al., 2020; Yamada et al., 2020: [[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](doc:2020/11/2010_01057_luke_deep_context))
presumably by introducing real-world knowledge.
We show that using entity representations facilitates
cross-lingual transfer by providing languageindependent
features.
>
> Multilingual extension of LUKE. The model is trained with the multilingual
masked language modeling (MLM) task as well
as the masked entity prediction (MEP) task with
Wikipedia entity embeddings

> We investigate two ways of using the entity representations
in cross-lingual transfer tasks:
> 1. perform
entity linking for the input text, and append
the detected entity tokens to the input sequence.
The entity tokens are expected to provide language independent
features to the model
> 2. use the entity
[MASK] token from the MEP task as a languageindependent
feature extractor.

2022-04-17 A propos

Ikuya Yamada sur Twitter : "Is entity representation effective to improve multilingual language models?..."

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2022-04-13 A propos

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

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2022-03-31 A propos

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

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2022-03-15 A propos

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

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2021-02-07 A propos

[1911.03681] E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT

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> way of **injecting factual knowledge about entities into the pretrained BERT model**.

(Feeding entity vectors
into BERT as if they
were wordpiece vectors without additional encoder
pretraining)

>
> **We align [Wikipedia2Vec](tag:wikipedia2vec) entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors**. The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to [ERNIE](tag:ernie) (Zhang et al., 2019) and [KnowBert](tag:knowbert) (Peters et al., 2019), but it **requires no expensive further pretraining of the BERT encoder**.
>
> Our vector space alignment strategy is inspired by
cross-lingual word vector alignment

Related work on Entity-enhanced BERT:

> ([ERNIE](doc:2019/08/_1905_07129_ernie_enhanced_la) and [Knowbert](doc:2020/05/1909_04164_knowledge_enhanced)) are based on the design principle
that BERT be adapted to entity vectors. They introduce
new encoder layers to feed pretrained entity
vectors into the Transformer, and they require additional
pretraining to integrate the new parameters.
In contrast, E-BERT’s design principle is that entity
vectors be adapted to BERT.
>
> Two other knowledge-enhanced MLMs are [KEPLER](doc:2020/11/1911_06136_kepler_a_unified_)
(Wang et al., 2019c) and K-Adapter (Wang
et al., 2020)... Their factual knowledge
does not stem from entity vectors – instead, they
are trained in a multi-task setting on relation classification
and knowledge base completion.

Not to be cofounded with [[2009.02835] E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce](doc:2020/12/2009_02835_e_bert_a_phrase_a)

2021-01-12 A propos

Supporting content decision makers with machine learning | Dec, 2020 | Netflix TechBlog

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2020-12-11 A propos

[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

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2020-11-26 A propos

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 A propos

[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 A propos

[1909.01259] Neural Attentive Bag-of-Entities Model for Text Classification

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A model that performs **text classification using entities in a knowledge base**.

> Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine **simple high-recall entity detection based on a dictionary** (word->list of entities), to detect entities in a document, with a novel neural **attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities**.

2 steps:

1. Entity detection
2. Classification using the detected entities (+text) as inputs

Regarding entity linking, a local model which uses cosine
similarity between the embedding of the target
entity and the word-based representation of
the document to capture the relevance of an entity
given a document.

Embeddings from the KB: computed using [#Wikipedia2Vec](tag:wikipedia2vec) (similar words and entities
close to one another in a unified vector space)

Model using attention, with 2 features :

- cosine similarity between the
embedding of the entity and the word based
representation of the document
- the probability that the entity
name refers to the entity in KB.

Somewhat [related](doc:2020/01/investigating_entity_knowledge_)

### Conclusion:

>a neural
network model that performs text classification using
entities in Wikipedia. We combined simple
dictionary-based entity detection with a neural attention
mechanism to enable the model to focus
on a small number of unambiguous and relevant
entities in a document.

2020-09-02 A propos

[1812.06280] Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia

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2020-09-02 A propos

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 A propos

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

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2020-05-02 A propos

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

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2020-03-06 A propos

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

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2020-02-27 A propos

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

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2020-01-11 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

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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2020-01-09 A propos

20% Accuracy Bump in Text Classification with ME-ULMFiT

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2019-06-23 A propos

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

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2019-01-27 A propos

Wikipedia2Vec

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2018-12-23 A propos

A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 A propos

[1404.5367] Lexicon Infused Phrase Embeddings for Named Entity Resolution

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2018-05-22 A propos

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 A propos

[1804.01486] Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

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2018-04-14 A propos

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

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2018-03-05 A propos

Modèle Neuronal de Recherche d'Information Augmenté par une Ressource Sémantique (2017)

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2018-02-12 A propos

Knowledge Graph and Text Jointly Embedding (2014)

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2018-01-05 A propos

Learning Concept-Driven Document Embeddings for Medical Information Search (2017)

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2018-01-03 A propos

Combining word and entity embeddings for entity linking (ESWC 2017)

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2017-12-30 A propos

Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems - Microsoft Research (2015)

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> new simple, yet effective approaches to
learn domain specific word embeddings.

## Intro

> Adapting word embeddings, such as jointly capturing
syntactic and semantic information, can further enrich semantic
word representations for several tasks, e.g., sentiment
analysis (Tang et al. 2014), named entity recognition
(Lebret, Legrand, and Collobert 2013), entity-relation extraction
(Weston et al. 2013), etc. (Yu and Dredze 2014)
has introduced a lightly supervised word embedding learning
extending word2vec. They incorporate prior information to the objective
function as a regularization term considering synonymy relations
between words from Wordnet (Fellbaum 1999).

> In this work, we go one step further and investigate if
enriching the word2vec word embeddings trained on unstructured/
unlabeled text with domain specific semantic relations
obtained from knowledge sources (e.g., knowledge
graphs, search query logs, etc.) can help to discover relation
aware word embeddings. Unlike earlier work, **we encode the
information about the relations between phrases, thereby,
entities and relation mentions are all embedded into a low dimensional
vector space**.

## Related work (Learning Word Embeddings with Priors)

- word2vec
- Relational Constrained Model (RTM) (Yu and Dredze 2014)
While CBOW learns lexical word embeddings from provided text, the RTM learns embeddings of words based on their similarity to other words provided by a knowledge resource (eg. wordnet)
- Joint model  (Yu and Dredze 2014)
combines CBOW and RTM through linear combination

2017-10-02 A propos

Named Entity Recognition using Word Embedding as a Feature (2016)

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2017-10-01 A propos