Semanlink - Conditional random fields

> 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

[1508.01991] Bidirectional LSTM-CRF Models for Sequence Tagging

Tags:

2018-03-05 A propos

Sequence Tagging with Tensorflow

Tags:

2018-03-05 A propos

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

Tags:

2017-10-01 A propos

Propriétés

sl:creationDate : 2014-04-24
sl:creationTime : 2014-04-24T01:19:12Z
sl:describedBy : https://en.wikipedia.org/wiki/Conditional_random_field
rdf:type : sl:Tag
skos:altLabel : CRF@en
skos:prefLabel : Conditional random fields@en