Semanlink - Yves Peirsman

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Yves Peirsman

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7 Documents (Long List)

Dealing with Data Scarcity in Natural Language Processing | by Yves Peirsman | NLPTown | Medium 2019)

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2022-07-18 About

Adventures in Zero-Shot Text Classification

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2021-05-25 About

Distilling BERT models with spaCy - Towards Data Science (2019)

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2020-02-15 About

nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

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2019-02-07 About

A collection of notebooks for Natural Language Processing from NLP Town

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2019-02-07 About

Comparing Sentence Similarity Methods

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2018-05-25 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

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sl:creationDate : 2019-02-07
sl:creationTime : 2019-02-07T00:48:52Z
rdf:type : sl:Tag
skos:prefLabel : Yves Peirsman