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Probabilistic Graphical Models AND Machine learning

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19 Documents

Christopher Manning sur X : "Now that everyone is writing LLM programs, the idea of doing approximate bayesian inference by sampling along linguistic pipelines (rather than k-best, etc.) is more relevant again"

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2024-03-19 About

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

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2020-02-27 About

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

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2020-01-09 About

[1011.4088] An Introduction to Conditional Random Fields

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2019-10-13 About

[1904.13001] Encoding Categorical Variables with Conjugate Bayesian Models for WeWork Lead Scoring Engine

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2019-07-04 About

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 About

Effect of Non-linear Deep Architecture in Sequence Labeling

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2018-10-22 About

Named Entity Recognition and Classification with Scikit-Learn

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2018-09-16 About

A Framework for Semi supervised Concept Extraction from MOOC content (2017)

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2018-08-12 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

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

[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

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

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2017-10-01 About

On Bayesian inference, maximum entropy and Support Vector Machines methods

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2014-04-25 About

mathematicalmonk's channel - YouTube

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2013-05-24 About

Modeling Uncertainty in Semantic Web Taxonomies

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2006-12-06 About