Topic Model Tutorial Part 1 – The IntuitionTopic 2 Topic 2 → Topic 2 document 0: 23/05/16 54...

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23/05/16 1 Topic Model Tutorial Part 1 – The Intuition Christoph Carl Kling [email protected]

Transcript of Topic Model Tutorial Part 1 – The IntuitionTopic 2 Topic 2 → Topic 2 document 0: 23/05/16 54...

Page 1: Topic Model Tutorial Part 1 – The IntuitionTopic 2 Topic 2 → Topic 2 document 0: 23/05/16 54 Latent Dirichlet Allocation (LDA) - A word in a document is likely to belong to the

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Topic Model TutorialPart 1 – The Intuition

Christoph Carl [email protected]

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Conference Dinner

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Conference dinner

- I sit at a table with a probability proportional to the number

of people already sitting there

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Conference dinner

- I sit at a table with a probability proportional to the number

of people already sitting there

- If everybody does the same and there are more and more

people entering, the probabilities for choosing the tables

converge

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Conference dinner

- I sit at a table with a probability proportional to the number

of people already sitting there

- If everybody does the same and there are more and more

people entering, the probabilities for choosing the tables

converge

- The scheme yields a sample of a Dirichlet distribution

Parameters: initial number of participants at each table

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Dirichlet Distribution

- The scheme yields a sample of a Dirichlet distribution

Parameters: initial number of participants at each table

- “rich get richer”, preferential attachment

- Initial settings of < 1 participant at each table produce

sparse distributions

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In reality, I choose tables based on the number of people AND the topic they talk about!

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In reality, I choose tables based on the number of people AND the topic they talk about!

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Topics

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Articles are labelled with tags(e.g. politics, economy, sports, ...)

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Politics: election, party, vote, candidate, ...Economy: dollar, crisis, financial, market, …Sports: soccer, basketball, match, score, ...

Articles are labelled with tags(e.g. politics, economy, sports, ...)

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Politics: election, party, vote, candidate, ...Economy: dollar, crisis, financial, market, …Sports: soccer, basketball, match, score, ...

Articles are labelled with tags(e.g. politics, economy, sports, ...)

Topics

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Topic Modelling

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Topic Modelling

Automatically extract topics from text documents!

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Latent Semantic Analysis

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Term-document matrix

high occurrencelow occurrence

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Term-document matrix

term frequencies document 4

high occurrencelow occurrence

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Term-document matrix

how often does document 4contain the word “blood”?

high occurrencelow occurrence

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Latent Semantic Analysis (LSA)

- Topic model based on “matrix decomposition”

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Latent Semantic Analysis (LSA)

- Topic model based on “matrix decomposition”

- Topics are described by “loadings” over the terms

Topic 1

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The Test Dataset

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probabilistic topic model probabilistic topic model probabilistic topic model probabilistic topic model probabilistic topic model probabilistic topic model

probabilistic topic model famous fashion model

famous fashion modelfamous fashion model

famous fashion model famous fashion model famous fashion model famous fashion model famous fashion model famous fashion model famous fashion model famous fashion model famous fashion model famous fashion model

document 0: document 1: document 2: document 3: document 4: document 5: document 6: document 7: document 8: document 9: document 10: document 11: document 12: document 13: document 14: document 15: document 16: document 17: document 18: document 19:

Test dataset

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Topic 1: famous, fashion, modelTopic 2: model, probabilistic, topic

Expected topics

probabilistic topic model probabilistic topic model …

famous fashion model famous fashion model

...

Test dataset

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Test dataset

Term-document matrix

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Test dataset

Term-document matrix

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Test dataset

Term-document matrix

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Topic 1 Topic 2

LSA

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Topic 1 Topic 2

LSA

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Topic 1 Topic 2

LSA

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Topic 1 Topic 2

LSA

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Topic 1 Topic 2

LSA

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LSA – Weaknesses

- Topic loadings can be negative → hard to interpret!

- LSA has problems to cope with word ambiguities

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Probabilistic LSA

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Probabilistic LSA (PLSA)

- Based on categorical distributions

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Probabilistic LSA (PLSA)

- Based on categorical distributions

- Probabilistic model that explains

the creation of documents

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Probabilistic LSA (PLSA)

The PLSA model for the creation of words in documents:

1) Documents have each a categorical distribution

over the topics

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Probabilistic LSA (PLSA)

The PLSA model for the creation of words in documents:

1) Documents have each a categorical distribution

over the topics

2) Topics have each a categorical distribution over

all words

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Probabilistic LSA (PLSA)

The PLSA model for the creation of words in documents:

1) Documents have each a categorical distribution

over the topics

2) Topics have each a categorical distribution over

all words

3) Creation of a word in document i:

1)Draw a topic from

2)Draw a word from

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Topic 1 Topic 2

Probabilistic LSA (PLSA)

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Topic 1 Topic 2

Probabilistic LSA (PLSA)

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Topic 1 Topic 2

Probabilistic LSA (PLSA)

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Document 0 (probabilistic topic model)

Probabilistic LSA (PLSA)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Topic 1 Topic 2

Loadin

g

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Document 0 (probabilistic topic model) Document 7 (famous fashion model)

Probabilistic LSA (PLSA)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Topic 1 Topic 2

Loadin

g

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PLSA – Strengths & Weaknesses

- Topics are probability distributions and easy to interpret!

- PLSA still has problems to cope with ambiguous words

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Latent Dirichlet Allocation

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

famous fashion modeldocument 7:

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

famous fashion model

Topic 1 Topic 1 ?

document 7:

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

famous fashion model

Topic 1 Topic 1 → Topic 1

document 7:

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

probabilistic topic modeldocument 0:

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

probabilistic topic model

Topic 2 Topic 2 ?

document 0:

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

probabilistic topic model

Topic 2 Topic 2 → Topic 2

document 0:

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

- We would need some preference for already assigned

topics in a document

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Latent Dirichlet Allocation (LDA)

- A word in a document is likely to belong to the same topic

as the other words of that document

- We would need some preference for already assigned

topics in a document

→ Dirichlet distribution!

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Dirichlet distribution

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Dirichlet distribution

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Topic 1 Topic 2

Latent Dirichlet Allocation (LDA)

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Document 0 (probabilistic topic model) Document 7 (famous fashion model)

Probabilistic topic model (with sparse Dirichlet)

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LDA – Strengths

- LDA can cope with ambiguous words!

- Most popular topic model

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(Human) Evaluation

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PLSA LDATopic 1 family,registered,like,hard,members,… first,network,time,won,week,third,...

Topic 2 high,left,planned,organization,story,… two,house,found,police,car,home,..

Topic 3 normal,predicted,first,chief,health,… cents,futures,cent,lower,higher,...

… … ...

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Topic Model Game

- Tests the semantic coherence of topics

- Given the top-5 words of a topic and an intruder word

from a different topic – find the intruder word!

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Topic Model Game

Given the top-5 words of a topic and an intruder word from a different topic – find the intruder word!

air pollution power blood environmental nuclear

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Topic Model Game

Given the top-5 words of a topic and an intruder word from a different topic – find the intruder word!

air pollution power blood environmental nuclear

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Topic Model Game

https://tinyurl.com/tmt16

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Summary

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Summary

- Dirichlet distribution (Polya urn scheme)

- Latent Semantic Analysis (LSA)

- Probabilistic Latent Semantic Analysis (PLSA)

- Latent Dirichlet Allocation (LDA)

- Human evaluation of topic models


Topic Modeling of Multimodal Data: An Autoregressive Approach · Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with mul-timodal

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Measuring Topic Quality in Latent Dirichlet Allocationsergey/slides/N14_PhMLtalk.pdf · Topic modeling Measuring topic quality On Bayesian inference Latent Dirichlet Allocation Outline

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Dirichlet Mixtures, the Dirichlet Process, and the Structure of Protein

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A study of Rhaly operators on Dirichlet series with real ...€¦ · The ordinary Dirichlet series plays a prominent role in analytic number theory and has been a prime topic of study

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A Latent Dirichlet Allocation Systematic Literature Review ... · (TM) technique and Latent Dirichlet Allocation (LDA) topic model were used to cluster articles in different topics.

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Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive ...proceedings.mlr.press/v70/cong17a/cong17a.pdf · eters across all layers and topics, with topic and layer specific learning

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Dirichlet Processes: A Gentle Tutorialkbe/dp_tutorial.pdf · Dirichlet Processes A gentle tutorial ... and Dirichlet Process introduction ... parameters for different mixture

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Ouyang Ruofei Topic Model Latent Dirichlet Allocation Ouyang Ruofei May. 10 2013 LDA.

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Large Scale High-Precision Topic Modeling on Twittertranslectures.videolectures.net/site/normal_dl/tag=899955/kdd2014_yang_twitter_01.pdfLDA (latent Dirichlet allocation) & variants

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Hierarchical Optimal Transport for Document Representationpapers.nips.cc/paper/8438-hierarchical-optimal... · Topic models like latent Dirichlet allocation (LDA) (Blei et al., 2003)

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LatentDirichletAllocation: ExtractingTopicsfrom ...webdocs.cs.ualberta.ca/~hindle1/2016/06Hindle.pdf · Keywords: latent dirichlet allocation, topic modelling, software engineering,

LatentDirichletAllocation: ExtractingTopicsfrom ...webdocs.cs.ualberta.ca/~hindle1/2016/06Hindle.pdf · Keywords: latent dirichlet allocation, topic modelling, software engineering,

Employing Latent Dirichlet Allocation Model for Topic Extraction … · 2017. 10. 20. · 2. Literature Review 2.1. Research of Topic Extraction for English Text Topic extraction

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Probabilistic Topic Models - Latent Dirichlet Allocation€¦ · Latent Dirichlet allocation. Journal of Machine Learning Research 3:993-1022, 2003. MLReadingGroup ProbabilisticTopicModels

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Dirichlet-Hawkes Processes with Applications to …lsong/papers/DuFarAhmSmoSon15.pdf · Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams

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Decoupling Sparsity and Smoothness in the ... - Data Mining · topic models using neural networks. Topic models based on latent Dirichlet allocation (LDA) successfully use the Dirichlet

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arXiv:1412.4986v1 [cs.DC] 16 Dec 2014 · Topic models provide a way to aggregate vocabulary from a document corpus to form latent \top-ics." In particular, Latent Dirichlet Allocation

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A Decision Support System for Inbound Marketers: An Empirical Use of Latent Dirichlet Allocation Topic Model to Guide Infographic Designers

A Decision Support System for Inbound Marketers: An Empirical Use of Latent Dirichlet Allocation Topic Model to Guide Infographic Designers

Variation Dirichlet Blur Kernel Estimation doubledecsai.ugr.es/vip/files/journals/Variation Dirichlet Blur Kernel... · Variational Dirichlet Blur Kernel Estimation ... learned image

Variation Dirichlet Blur Kernel Estimation doubledecsai.ugr.es/vip/files/journals/Variation Dirichlet Blur Kernel... · Variational Dirichlet Blur Kernel Estimation ... learned image

Techniques for Dimensionality Reduction Topic Models/LDA Latent Dirichlet Allocation.

Techniques for Dimensionality Reduction Topic Models/LDA Latent Dirichlet Allocation.

[Kim+ ICML2012] Dirichlet Process with Mixed Random Measures : A Nonparametric Topic Model for Labeled Data

[Kim+ ICML2012] Dirichlet Process with Mixed Random Measures : A Nonparametric Topic Model for Labeled Data