Semanlink - Favoris ; Technical girls and guys AND Entity Retrieval

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Favoris ; Technical girls and guys AND Entity Retrieval

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23 Documents

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2024-04-12 About

Entity Embedding Completion for Wide-Coverage Entity Disambiguation

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2022-12-11 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

(((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

[2010.00904] Autoregressive Entity Retrieval

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2021-01-14 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

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

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

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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2019-12-11 About - Local Copy

Entity Linking | NLP-progress

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2019-04-25 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

[1404.5367] Lexicon Infused Phrase Embeddings for Named Entity Resolution

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2018-05-22 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

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

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2018-03-06 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

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

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2017-12-30 About

NERD: Named Entity Recognition and Disambiguation

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2013-07-10 About

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

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2010-08-12 About