Semanlink - Embeddings in NLP AND Knowledge Graphs

[2205.15952] Knowledge Graph -- Deep Learning: A Case Study in Question Answering in Aviation Safety Domain

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2022-06-11 About

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

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2022-04-13 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

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

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

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2019-10-09 About

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

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2019-01-27 About

Wikipedia2Vec

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2018-12-23 About

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

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2018-06-08 About

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

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2018-04-14 About

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 About

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

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2018-02-12 About

Knowledge Graph and Text Jointly Embedding (2014)

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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

Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017)

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2017-10-21 About

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 About

ConceptNet

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2017-09-18 About