Semanlink - Entities AND Conditional random fields

[2002.11402] Detecting Potential Topics In News Using BERT, CRF and Wikipedia

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2020-02-27 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

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 A propos

Effect of Non-linear Deep Architecture in Sequence Labeling

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2018-10-22 A propos

Named Entity Recognition and Classification with Scikit-Learn

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2018-09-16 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

Stanford Named Entity Recognizer

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