Semanlink - Favoris ; AI girls and guys AND Knowledge Graphs

[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 About

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

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2022-04-13 About

Domain adaptation of word embeddings through the exploitation of in-domain corpora and knowledge bases (PhD Thesis 2021)

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Thèse de Hicham El Boukkouri, univ Paris-Saclay

[Github](https://github.com/helboukkouri/phd-code)

### Goal

Given a target specialized domain, improve the quality of general-domain
word representations using in-domain corpora and/or knowledge bases

### Contributions

#### a method for specializing general-domain embeddings in a [Low-Resource](tag:nlp_low_resource_scenarios) context.

> - train static representations on the task corpus, 
> - resume the
pre-training of general-domain contextual embeddings on the same task corpus,
> - finally, combine both static and contextual representations into
one final model

#### we tackle the issue of using a general-domain vocabulary in a specialized domain

#### Evaluation of  re-training vs training from scratch on specialized corpora using a specialized vocabulary

training from scratch better, but not that much: re-training from a general model
is still appropriate as it is less expensive and leads to comparable, although
slightly lower, performance

#### Regarding subword-based tokenization systems
> we argue that they are inconvenient in practice -> CharacterBERT, a variant
of BERT that uses ELMo’s character-based system instead of WordPieces. More convenient ti use, superior robustness to misspellings

#### Ways to specialize general-domain representations using knowledge bases

a strong baseline using a simple
method relying on graph embeddings and concatenation, using only is_a relation

> both static and contextual embeddings may effectively be
specialized using this simple approach

#### Knowledge Injection Modules (KIM) that
inject the knowledge representations directly within the BERT-like models' architecture

### Notes

> our experiments focused on a single
setting (i.e. the medical domain and the English language)

> meta-embeddings, an approach that consists in combining
different sets of representations for achieving improved performance

2022-03-23 About

Yosi Shamay sur Twitter : "a new platform for rapid ad-hoc knowledgebase construction using extractive search...."

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2022-02-22 About

[2101.00345] Modeling Fine-Grained Entity Types with Box Embeddings

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2021-06-22 About

[2102.07043] Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

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> a method for constructing **a virtual KB (VKB) trained entirely from text**

Open Predicate Query Language (OPQL): constructing a virtual knowledge base (VKB) that supports KB reasoning & open-domain QA, tackling the incompleteness of knowledge bases by constructing a virtual KB only from text

> OPQL constructs
a VKB by **encoding and indexing a set of
relation mentions** in a way that naturally enables
reasoning and can be trained without any structured
supervision.

> can be used
as an **external memory integrated into a language
model**

cf. this earlier paper [[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base](doc:2020/07/2002_10640_differentiable_rea). But does not require an initial structured KB for distant
supervision.

> The key idea in constructing the OPQL VKB is to use a
dual-encoder pre-training process, similar to 
[[1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning](doc:2021/05/1906_03158_matching_the_blank)

Related work section refers to [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced). Also refers to [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_) (some authors in common)

2021-06-20 About

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

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2020-11-26 About

[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 About

[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 About

[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base

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2020-07-11 About

End-to-End Learning with Text & Knowledge Bases (Bhuwan Dhingra PhD Thesis)

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2020-07-06 About

Peter Bloem sur Twitter : "One of the messages from Ruffinelli et al 2020..."

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2020-05-19 About

[1907.04829] BAM! Born-Again Multi-Task Networks for Natural Language Understanding

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2020-05-12 About

CONCEPTUAL GROUNDING FOR TEXT REPRESENTATION LEARNING

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2019-12-03 About

Extraction de relation via la validation de relation

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2019-12-03 About

The knowledge graph as the default data model for learning on heterogeneous knowledge (2017)

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2019-08-22 About

Learning Structured Embeddings of Knowledge Bases (2011)

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2019-08-03 About

Reasoning With Neural Tensor Networks for Knowledge Base Completion (2013)

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**Predicting the likely truth of additional facts based on existing facts in the knowledge base.**

> we introduce an expressive neural
tensor network suitable for reasoning over relationships between two entities.

Most similar work: [Bordes et al.](http://127.0.0.1:8080/semanlink/doc/2019/08/learning_structured_embeddings_) (2011)

Contributions:

1. new neural tensor
network (**NTN**) suitable for reasoning over relationships between two entities. Generalizes several previous neural network models and provides a more
powerful way to model relational information than a standard neural network layer.
2. a new way to represent entities in knowledge bases, as the
average of  their constituting word vectorss, allowing the sharing of statistical strength between the words describing
each entity (e.g., Bank of China and China).
3. incorporation of word vectors which are trained on large unlabeled text

> We **learn to modify word representations
via grounding in world knowledge**. This essentially allows us to analyze word embeddings and
query them for specific relations. Furthermore, the resulting vectors could be used in other tasks
such as named entity recognition or relation classification in natural language

**Makes use of entity name**: NTN
first learns word vectors from an auxiliary news corpus, and
then initializes the representation of each entity by averaging
the vectors of words contained in its name. For example,
**the embedding of AlfredHitchcock is initialized by the
average word vectors of “alfred” and “hitchcock”**... This kind of methods model textual information
separately from KG facts, and hence fail to leverage
interactions between them.[src](doc:2019/05/knowledge_graph_embedding_a_su)

2019-08-03 About

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

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2019-01-27 About

[1809.00782] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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2018-09-06 About

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

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2018-06-08 About

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

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2018-02-12 About

Translating Embeddings for Modeling Multi-relational Data (2013)

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This work focuses on modeling multi-relational
data from KBs (Wordnet and Freebase in this paper), with the goal of providing an efficient
tool to complete them by automatically adding new facts, without requiring extra knowledge.

**Embedding entities and relationships of multirelational
data**: a method which **models relationships by interpreting them as translations** operating on the
low-dimensional embeddings of the entities. Motivation:
- hierarchical relationships are extremely common in KBs and translations are the natural transformations for representing them.
- cf. word embeddings and the “capital of” relationship between countries and cities, which are (coincidentally rather than willingly) represented by the model as translations in the embedding space. This suggests that there may exist embedding spaces in which 1-to-1 relationships between entities of different types may, as well, be represented by translations. The intention of our model is to enforce such a structure of the embedding space.

[Good blog post by PY Vandenbussche](http://pyvandenbussche.info/2017/translating-embeddings-transe/)

2018-01-05 About

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

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2018-01-03 About

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

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2017-12-30 About