semantic history embedding in online generative topic models pu wang (presenter) authors: loulwah...

Semantic History Embedding in

OnlineGenerative Topic

ModelsPu Wang (presenter)

Authors:Loulwah AlSumait (lalsumai@gmu.edu)Daniel Barbará (dbarbara@gmu.edu)Carlotta Domeniconi (carlotta@cs.gmu.edu)

Department of Computer ScienceGeorge Mason University

SDM 2009

Outline Introduction and related work Online LDA (OLDA) Parameter Generation

Sliding history window Contribution weights

Experiments Conclusion and future work

Introduction When a topic is observed at a certain

time, it is more likely to appear in the future

previously discovered topics hold important information about the underlying structure of data

Incorporating such information in future knowledge discovery can enhance the inferred topics

Related Work Q. Sun, R. Li et al. ACL 2008.

LDA-based Fisher kernel to measure the text semantic similarity between blocks of LDA documents

X. Wang et al. ICDM 2007 Topical N-Gram model that automatically identified

feasible N-grams based on the context that surround it

X. Phan et al. IW3C2 2008. a classifier on both a small set of labeled documents

in addition to an LDA topic model estimated from Wikipedia.

Tracking Topics

Tracking Topics

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Topic Evolution Tracking

PriorsConstruction

Emerging Topic

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Inference Process

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Parameter Generation

Simple inference problem Gibbs Sampling Current

streamHistoric

observations

Topic Evolution Tracking Topic alignment over time Handles changes in lexicon, topic drift

Topic 1 (0.65)

Bank (0.44), money (0.35), loan (0.21)

Topic 2 (0.35)

Factory (0.53), production (0.34), labor (0.13)

Topic 1 (0.43) Bank (0.5), credit (0.32), money (0.18)

Topic 2 (0.57) Factory (0.48), cost (0.32), manufacturing (0.2)

t Time t+1

P(topic) P(word|topic)

Aligned topicsover time

Sliding History Window Consider all topic-word distributions within

a “sliding history window” (δ) Alternatives for keeping track of history at

time t full memory, δ= t short memory, δ=1 Intermediate memory, δ= c

Matrix Evolution MatrixDictionary

Topic distribution over time

Contribution Control Evolution Tuning Parameters ω

Individual weights of models Decaying history: ω1 < ω2<…< ωδ

Equal contributions: ω1 = ω2=…= ωδ

Total weight of history (vs. weight of new observations)

Balanced weights (sum=1) Biased toward the past (sum>1) Biased toward the future (sum<1)

Parameter Generation Priors of Topic distribution over words at

time t+1

Generate topic distribution

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Experimental Design “Matlab Topic Modeling Toolbox”, by Mark Steyvers

and Tom Griffiths Datasets:

NIPS Proceedings from 1988-2000 1,740 papers, 13,649 unique words, 2,301,375 word tokens 13 streams, size from 90 to 250 doc’s per stream

Reuters-21578 News from 26-FEB-1987 to 19-OCT-1987 10,337 documents; 12,112 unique words; 793,936 word tokens 30 streams (29/340 doc’s, 1/517 doc’s)

Baselines: OLDAfixed: no memory OLDA (ω(1) ): short memory

Performance Evaluation measure: Perplexity Test set: documents of next year or stream

ReutersOLDA with fixed β vs. OLDA with semantic β

No memory

ReutersOLDA with different window size and weights• Increasing window size enhanced prediction

• Incremental history information (δ>1,sum>1) did not improve topic estimation at all Increase window size

short memory

Equal contribution

Incremental History Information

NIPSOLDA with Different Window

No memory

Short memory

• Increasing window size enhanced prediction w.r.t. short memory

• Window size greater than 3 enhanced prediction

• Effect of total weight

•

NIPSOLDA with Different Total Weight

No memory

Sum of weight = 1

Decrease sum of weights

Models with lower total weight resulted in better prediction

NIPS & ReutersOLDA with Different Total Weight

• Variable sum(ω)

• δ = 2Decrease to

tal

sum of weights

Increase total

sum of weights

NIPSOLDA with Equal vs Decaying History Contribution

Conclusions the effect of embedding semantic information in

LDA topic modeling of text streams Parameter generation based on topical

structures inferred in the past Semantic embedding enhances OLDA prediction Effect of

Total influence of history, History window size, and Equal or decaying contributions

Future work use of prior-knowledge effect of embedded historic semantics on detecting

emerging and/or periodic topics