Semanlink - Favoris AND Entities

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 A propos

[1807.00745] Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

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2022-07-18 A propos

[2006.01969] REL: An Entity Linker Standing on the Shoulders of Giants

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2022-07-12 A propos

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 A propos

Tara Safavi sur Twitter : "CascadER, a new knowledge graph (KG) link prediction method leveraging structured relations + unstructured text..."

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2022-07-07 A propos

DataBorg - Knowledge management simplified

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2022-05-14 A propos

[2204.08173] TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

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2022-05-11 A propos

[2205.04260] EASE: Entity-Aware Contrastive Learning of Sentence Embedding

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2022-05-11 A propos

SapienzaNLP/extend: Entity Disambiguation as text extraction (ACL 2022)

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2022-04-19 A propos

[1909.00426] Global Entity Disambiguation with BERT

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2022-04-18 A propos

[2110.08151] mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

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[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

EASE: Entity-Aware Contrastive Learning of Sentence Embedding | Papers With Code

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2022-04-08 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

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

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2022-03-23 A propos

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

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2022-03-15 A propos

MasakhaNER: Named Entity Recognition for African Languages | MIT Press

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2021-10-14 A propos

[2110.06176] Mention Memory: incorporating textual knowledge into Transformers through entity mention attention

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2021-10-13 A propos

(((ل()(ل() 'yoav))))👾 sur Twitter : "my two cents on why NLP as a field is focusing on the ML-ish / algorithmic / leaderboard-ish aspects (incl., now, LLMs) and not on the underlying language phenomena: it is just so much easier, on so many levels."

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2021-08-30 A propos

[2103.11811] MasakhaNER: Named Entity Recognition for African Languages

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2021-07-06 A propos

[2001.03765] Learning Cross-Context Entity Representations from Text

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2021-06-22 A propos

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

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2021-06-22 A propos

[1807.04905] Ultra-Fine Entity Typing

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2021-06-22 A propos

[2106.04098] Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model

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2021-06-16 A propos

[2009.12030] AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding

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2021-05-17 A propos

[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 A propos

Christopher Dengsø sur Twitter : "The moderation API now detects addresses in addition to other personal details."

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2021-03-09 A propos

asahi417/tner: Language model finetuning on NER

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2021-03-04 A propos

Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020)

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2021-02-24 A propos

Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020) (slides)

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2021-02-24 A propos

Why we switched from Spacy to Flair to anonymize French case law | by Michaël Benesty | Towards Data Science

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2021-02-10 A propos

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

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2021-02-07 A propos

Linking Entities with Knowledge Graphs | by Sigurd Berglann | Strise | Medium

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2021-02-05 A propos

Custom NLP Approaches to Data Anonymization | by Omri Mendels | Towards Data Science

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2021-02-01 A propos

[2010.00904] Autoregressive Entity Retrieval

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2021-01-14 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

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

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2020-12-01 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

Text-Enhanced Representation Learning for Knowledge Graph (IJCAI 2016)

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2020-11-03 A propos

[1911.06136] KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation

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2020-11-03 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

Wikifier

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2020-10-11 A propos

TAGME: on-the-fly annotation of short text fragments!

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2020-10-11 A propos

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

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2020-10-03 A propos

SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions (AAAI 2017)

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2020-10-02 A propos

Representation learning of knowledge graphs with entity descriptions (AAAI 2016)

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"Description-Embodied Knowledge Representation Learning" (DKRL)

> In most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods... Experimental results on real-world datasets show that, our method outperforms other baselines on the knowledge graph completion and entity classification tasks, especially under the zero-shot setting, which indicates that **our method is capable of building representations for novel entities according to their descriptions**. [Source code on github](https://github.com/xrb92/DKRL).

For fact triples: TransE. Meanwhile, given an entity we will also learn to maximize
the likelihood of predicting its description (using either CBOW or CNN encoder) (head + relation = tail, also in "text space")

Two types of representations for entities: structure-based
representations and description-based representations. They are learned simultaneously into the same
vector space but not forced to be unified **so that novel
entities with only descriptions can be represented**.

2020-10-02 A propos

[2001.08053] Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization

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2020-10-01 A propos

[1806.06478] Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

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2020-09-06 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

Ranked Entities in Search Results at Google

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2020-07-02 A propos

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 A propos

[1910.12507] A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?

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2020-05-04 A propos

Disambiguating KBpedia Knowledge Graph Concepts

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2020-05-03 A propos

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

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2020-05-02 A propos

Sebastian Riedel sur Twitter : Happy to introduce BLINK, the @facebookai open-source entity linker!...

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2020-05-02 A propos

Iterative Entity Alignment with Improved Neural Attribute Embedding

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2020-04-29 A propos

A Comprehensive Survey of Knowledge Graph Embeddings with Literals: Techniques and Applications

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2020-04-29 A propos

[2001.09522] TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

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how to add a set of new concepts to an existing taxonomy.

[Tweet](https://twitter.com/mickeyjs6/status/1253772146142216194?s=20) [GitHub](https://github.com/mickeystroller/TaxoExpan)

> we study the taxonomy expansion task: given an
existing taxonomy and a set of new emerging concepts, we aim
to automatically expand the taxonomy to incorporate these new
concepts (without changing the existing relations in the given taxonomy).

> To the best of our knowledge, this is the first study on **how to
expand an existing directed acyclic graph (as we model a taxonomy
as a DAG) using self-supervised learning**.

Self-supervised framework, the existing taxonomy being used as training data: it learns a model to predict whether a query concept is the direct hyponym of an anchor concept.

> 2 techniques:
>
> 1. a **position-enhanced graph neural network that encodes the local structure of an anchor concept** in the existing taxonomy,
> 2. a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.

Regarding 1: uses [GNN](/tag/graph_neural_networks.html) to model the "ego network" of concepts (potential “siblings”
and “grand parents” of the query concept).

> Regular
GNNs fail to distinguish nodes with different relative positions to
the query (i.e., some nodes are grand parents of the query while
the others are siblings of the query). To address this limitation, we
present a simple but effective enhancement to inject such position
information into GNNs using position embedding. We show that
such embedding can be easily integrated with existing GNN architectures
(e.g., [GCN](/tag/graph_convolutional_networks) and GAT) and significantly boosts the
prediction performance

Regarding point 2: uses InfoNCE loss, cf. [Contrastive Predictive Coding](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1807.03748)

> Instead of predicting
whether each individual ⟨query concept, anchor concept⟩ pair
is positive or not, we first group all pairs sharing the same query
concept into a single training instance and learn a model to select
the positive pair among other negative ones from the group.

(Hum, ça me rappelle quelque chose)

> assume each concept (in existing taxonomy + set of new concepts) has an initial embedding
vector learned from some text associated with this concept.

To keep things tractable, only attempts to find a single parent node of each new concept.

2020-04-25 A propos

[2004.06842] Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

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2020-04-17 A propos

Martynas Jusevicius sur Twitter : "Is there a solution for entity recognition that would use a local #KnowledgeGraph to look for matches? Ideally any SPARQL datasource..."

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2020-03-13 A propos

AmbiverseNLU: A Natural Language Understanding suite by Max Planck Institute for Informatics

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2020-03-13 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

pfliu-nlp/Named-Entity-Recognition-NER-Papers: An elaborate and exhaustive paper list for Named Entity Recognition (NER)

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2020-01-12 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

Trust, but verify! Better entity linking through automatic verification (2017)

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2020-01-10 A propos

HITS at TAC KBP 2015:Entity Discovery and Linking, and Event Nugget Detection

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2020-01-10 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

[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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2020-01-09 A propos

How to build deep neural network for custom NER with Keras

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2020-01-07 A propos

Custom Named Entity Recognition Using spaCy - Towards Data Science

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2019-12-31 A propos

NER algo benchmark: spaCy, Flair, m-BERT and camemBERT on anonymizing French commercial legal cases

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2019-12-17 A propos

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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2019-12-11 A propos - Copie locale

Extraction de relation via la validation de relation

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2019-12-03 A propos

Elasticsearch RSS feed indexer with Spacy entity extraction

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2019-11-24 A propos

Meetup Paris #40 : Beyond plain text: elasticsearch’s annotated text field type (en anglais) - YouTube

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2019-11-24 A propos

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

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2019-10-09 A propos

[1908.08983] A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

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2019-08-28 A propos

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 A propos

Nandana Mihindukulasooriya sur Twitter : "I wonder if there is a service for disambiguation and entity linking for @wikidata where I can pass a text and get Wikidata entities mentioned in it!

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2019-07-13 A propos

Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019) - Slides

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2019-07-13 A propos

Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019) - YouTube

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2019-07-13 A propos

20% Accuracy Bump in Text Classification with ME-ULMFiT

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2019-06-23 A propos

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

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2019-05-30 A propos

Knowledge-Based Short Text Categorization Using Entity and Category Embedding | Springer for Research & Development (2019)

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2019-05-30 A propos

Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions. W Shen (2015)

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2019-05-14 A propos

[1810.09164] Named Entity Disambiguation using Deep Learning on Graphs

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2019-04-26 A propos

[1802.01021] DeepType: Multilingual Entity Linking by Neural Type System Evolution

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2019-04-25 A propos

Entity Linking | NLP-progress

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2019-04-25 A propos

Collective List-Only Entity Linking: A Graph-Based Approach - IEEE Journals & Magazine (2018)

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List-only Entity Linking

2019-04-24 A propos

Entity Linking via Symmetrical Attention-Based Neural Network and Entity Structural Features (2019)

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Entity linking

2019-04-24 A propos

Name List Only? Target Entity Disambiguation in Short Texts - ACL Anthology (2015)

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2019-04-24 A propos

List-only Entity Linking - ACL Anthology (2017)

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2019-04-24 A propos

Dandelion API - Semantic Text Analytics as a service

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2019-04-24 A propos

[1812.09449] A Survey on Deep Learning for Named Entity Recognition

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2019-04-24 A propos

[1807.06036] Pangloss: Fast Entity Linking in Noisy Text Environments

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2019-04-23 A propos

[1808.07699] End-to-End Neural Entity Linking

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2019-04-23 A propos

Phoneme Recognition (caveat emptor) – CMUSphinx Open Source Speech Recognition

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2019-04-16 A propos

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 A propos

Evaluating Solutions for Named Entity Recognition | Novetta.com (2018)

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2019-04-11 A propos

A machine learning model to understand fancy abbreviations, trained on Tolkien

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2019-04-03 A propos

A SIMPLE ALGORITHM FOR IDENTIFYING ABBREVIATION DEFINITIONS IN BIOMEDICAL TEXT

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2019-04-03 A propos

nlp - Abbreviation detection - Stack Overflow

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Acronyms (NLP)

2019-04-03 A propos

How Search Engines Might Expand Abbreviations in Queries - SEO by the Sea ⚓

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Acronyms (NLP)

2019-04-03 A propos

Acronym Recognition - Recognizing acronyms in Swedish texts

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Acronyms (NLP)

2019-04-03 A propos

[1902.05196] Categorical Metadata Representation for Customized Text Classification

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2019-02-18 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

Effect of Non-linear Deep Architecture in Sequence Labeling

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2018-10-22 A propos

Deep Learning for Named Entity Recognition #1: Public Datasets and Annotation Methods

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Named Entity Recognition

2018-10-15 A propos

Neural Network Embeddings Explained – Towards Data Science

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2018-10-09 A propos

Named Entity Recognition and Classification with Scikit-Learn

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2018-09-16 A propos

[Learning Note] StarSpace For Multi-label Text Classification

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2018-09-11 A propos

HyperE: Hyperbolic Embeddings for Entities

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2018-07-27 A propos

A Named Entity Recognition Shootout for German (2018)

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Named Entity Recognition

2018-07-12 A propos

[1806.04470] Design Challenges and Misconceptions in Neural Sequence Labeling

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2018-06-28 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

BiLSTM-CNN-CRF Implementation for Sequence Tagging

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2018-05-21 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

Named Entity Recognition and the Stanford NER Software (slides)

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2018-05-20 A propos

Named Entity Recognition: Milestone Papers, Models and Technologies (2017)

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2018-05-20 A propos

Stanford Named Entity Recognizer

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Stanford NER

2018-05-20 A propos

How to Train your Own Model with NLTK and Stanford NER Tagger? (for English, French, German…)

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2018-05-20 A propos

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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2018-05-20 A propos

Implementing Poincaré Embeddings | RARE Technologies

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2018-05-20 A propos

[1709.03856] StarSpace: Embed All The Things!

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2018-05-13 A propos

[1607.07956] Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification (COLING 2016)

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2018-05-12 A propos

Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning

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2018-05-10 A propos

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

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2018-04-14 A propos

Exploring neural architectures for NER (CS224N 2018)

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2018-04-05 A propos

SPACY'S ENTITY RECOGNITION MODEL: incremental parsing with Bloom embeddings & residual CNNs - YouTube

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2018-03-06 A propos

Effective Word Representation for Named Entity Recognition (2017)

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2018-03-06 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

[1508.01991] Bidirectional LSTM-CRF Models for Sequence Tagging

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2018-03-05 A propos

Sequence Tagging with Tensorflow

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2018-03-05 A propos

[1603.01360] Neural Architectures for Named Entity Recognition

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2018-03-05 A propos

A survey of named entity recognition and classification (2006)

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Named Entity Recognition

2018-03-05 A propos

[1604.06737] Entity Embeddings of Categorical Variables

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2018-03-03 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

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 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

[1705.08039] Poincaré Embeddings for Learning Hierarchical Representations

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2017-12-16 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

Dexter, an Open Source Framework for Entity Linking

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2014-10-20 A propos

Extraction de la semantique

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2014-06-18 A propos

Bing - Knowledge Widget (Beta)

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2014-04-04 A propos

Context-based Entity Linking | Blog | graus.nu

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Named Entity Recognition

2013-07-18 A propos

NERD: Named Entity Recognition and Disambiguation

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2013-07-10 A propos

Wikilinks - Information Extraction and Synthesis Laboratory

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Wikilinks Corpus

2013-03-12 A propos

Learning from Big Data: 40 Million Entities in Context

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Wikilinks Corpus

2013-03-12 A propos

Duped by Dupes | Wavii Blog

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2012-08-25 A propos

From Words to Concepts and Back: Dictionaries for Linking Text, Entities and Ideas

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2012-05-22 A propos

From Linked Data to Linked Entities: a Migration Path - Giovanni Bartolomeo, Stefano Salsano

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2012-04-20 A propos

LINDEN: Linking Named Entities with Knowledge Base via Semantic Knowledge

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2012-04-19 A propos

A Spectrometry of Linked Data

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Entity mining is still a troublesome open problem. In past years many approaches allowed to automate the generation of equivalence links between references using schema matching or various heuristics based on the recognition of similar property values. In contrast, few of them considered the analysis of the network of equivalence links (“equivalence network”) as an indication of the likelihood and strength of the equivalence.

Could a URI reference (URIRef) be thought as exactly “attached” to its referent? Could it make sense to talk about entity “identifiers” or would it be better to talk about more ambiguous “references”, i.e., placeholders for any model that satisfies the formal semantics of the Semantic Web (Hayes)? Booth observes that the aforementioned question, which in the past has been often regarded as fundamental in the debate about identity on the Web, is relatively unimportant. As long as an entity, identified by whatsoever URIRef, is associated to at least one description containing machine understandable information, this information can be automatically processed and used by applications.

2012-04-16 A propos

Linked Data Entity Extraction with Zemanta and OpenCalais - benjamin nowack's blog

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2010-08-12 A propos