Semanlink - Favoris ; Semantic technology AND Entity Retrieval

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

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

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2022-07-18 About

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

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2022-07-12 About

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 About

DataBorg - Knowledge management simplified

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2022-05-14 About

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

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2022-05-11 About

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

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2022-04-19 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

MasakhaNER: Named Entity Recognition for African Languages | MIT Press

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2021-10-14 About

(((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 About

[2103.11811] MasakhaNER: Named Entity Recognition for African Languages

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2021-07-06 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

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

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2021-03-09 About

asahi417/tner: Language model finetuning on NER

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2021-03-04 About

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

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2021-02-24 About

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

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2021-02-24 About

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 About

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

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2021-02-07 About

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

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2021-02-05 About

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

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2021-02-01 About

[2010.00904] Autoregressive Entity Retrieval

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2021-01-14 About

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

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2020-12-01 About

Wikifier

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2020-10-11 About

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

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2020-10-11 About

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

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2020-10-01 About

Disambiguating KBpedia Knowledge Graph Concepts

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2020-05-03 About

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

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2020-05-02 About

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

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2020-05-02 About

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 About

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

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2020-03-13 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

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 About

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

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2020-01-10 About

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

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2020-01-10 About

A Joint Model for Entity Analysis: Coreference, Typing, and Linking (Greg Durrett, Dan Klein 2014)

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2020-01-09 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

[2001.01447] Improving Entity Linking by Modeling Latent Entity Type Information

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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

Custom Named Entity Recognition Using spaCy - Towards Data Science

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2019-12-31 About

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

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2019-12-17 About

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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2019-12-11 About - Local Copy

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

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2019-11-24 About

[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 About

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 About

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

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2019-07-13 About

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

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2019-07-13 About

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

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2019-05-14 About

[1810.09164] Named Entity Disambiguation using Deep Learning on Graphs

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2019-04-26 About

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

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2019-04-25 About

Entity Linking | NLP-progress

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2019-04-25 About

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

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List-only Entity Linking

2019-04-24 About

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

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Entity linking

2019-04-24 About

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

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2019-04-24 About

List-only Entity Linking - ACL Anthology (2017)

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2019-04-24 About

Dandelion API - Semantic Text Analytics as a service

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2019-04-24 About

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

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2019-04-24 About

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

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2019-04-23 About

[1808.07699] End-to-End Neural Entity Linking

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2019-04-23 About

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

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2019-04-16 About

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 About

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

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2019-04-11 About

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

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2019-04-03 About

A SIMPLE ALGORITHM FOR IDENTIFYING ABBREVIATION DEFINITIONS IN BIOMEDICAL TEXT

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2019-04-03 About

nlp - Abbreviation detection - Stack Overflow

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Acronyms (NLP)

2019-04-03 About

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

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Acronyms (NLP)

2019-04-03 About

Acronym Recognition - Recognizing acronyms in Swedish texts

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Acronyms (NLP)

2019-04-03 About

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

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2019-01-27 About

Effect of Non-linear Deep Architecture in Sequence Labeling

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2018-10-22 About

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

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Named Entity Recognition

2018-10-15 About

Named Entity Recognition and Classification with Scikit-Learn

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2018-09-16 About

A Named Entity Recognition Shootout for German (2018)

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Named Entity Recognition

2018-07-12 About

[1806.04470] Design Challenges and Misconceptions in Neural Sequence Labeling

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2018-06-28 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

Named Entity Recognition and the Stanford NER Software (slides)

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2018-05-20 About

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

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2018-05-20 About

Stanford Named Entity Recognizer

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Stanford NER

2018-05-20 About

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

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2018-05-20 About

Exploring neural architectures for NER (CS224N 2018)

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2018-04-05 About

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

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2018-03-06 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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2018-03-05 About

Sequence Tagging with Tensorflow

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2018-03-05 About

[1603.01360] Neural Architectures for Named Entity Recognition

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2018-03-05 About

A survey of named entity recognition and classification (2006)

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Named Entity Recognition

2018-03-05 About

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

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2017-12-30 About

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

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2017-10-01 About

Dexter, an Open Source Framework for Entity Linking

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2014-10-20 About

Extraction de la semantique

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2014-06-18 About

Bing - Knowledge Widget (Beta)

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2014-04-04 About

Context-based Entity Linking | Blog | graus.nu

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Named Entity Recognition

2013-07-18 About

NERD: Named Entity Recognition and Disambiguation

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2013-07-10 About

Wikilinks - Information Extraction and Synthesis Laboratory

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Wikilinks Corpus

2013-03-12 About

Learning from Big Data: 40 Million Entities in Context

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Wikilinks Corpus

2013-03-12 About

Duped by Dupes | Wavii Blog

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2012-08-25 About

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

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2012-04-20 About

LINDEN: Linking Named Entities with Knowledge Base via Semantic Knowledge

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2012-04-19 About

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 About

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

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2010-08-12 About