33. データのロード
• PLDA形式のデータを読み込む
a 2 is 1 character 1
a 2 is 1 b 1 character 1 after 1
class MyPipe extends Pipe{ static InstanceList load(String fileName) {
@Override ArrayList<Pipe> pipeList = new ArrayList<Pipe>();
public Instance pipe(Instance inst) { pipeList.add(new MyPipe());
String data = (String)inst.getData(); pipeList.add(new TokenSequence2FeatureSequence());
String array[] = data.split("¥¥s+"); InstanceList list =
TokenSequence ret = new TokenSequence(); new InstanceList(new SerialPipes(pipeList));
for(int i = 0 ; i < array.length ; i += 2){ CsvIterator it = new CsvIterator(fileName, "(.*)",1, 0,0);
String word = array[i]; list.addThruPipe(it);
int freq = Integer.parseInt(array[i + 1]); return list;
for(int f = 0 ; f < freq; ++f){ }
ret.add(new Token(word));
}
}
inst.setData(ret);
return inst;
}
}
36. トピックの代表的単語の抽出
• printTopWordsを使う
0 0.1847 algorithm learning function gradient convergence parameter error iteration vector
1 0.03452 map dominance ocular development pattern mapping organizing kohonen eye
2 0.01327 hint return data cost market stock prediction load subscriber
3 0.71807 case term result form consider general defined order paper
4 0.02225 face images recognition image faces representation hand video facial
5 0.42392 values line order point number high step result factor
6 0.01545 disparity gamma game play player partition games board operator
7 0.09096 local point region surface contour segment data field path
8 0.04591 prediction series error network predict training road predictor committee
9 0.12844 vector matrix linear space component dimensional point data transformation
...
38. 参考文献
• [Asuncion+ 2009] On smoothing and inference for topic models, UAI
• [Blei+ 2003] Latent Dirichlet allocation, JMLR
• [Griffiths and Steyvers 2004] Finding scientific topics, PNAS
• [Newman+ 2007] Distributed inference for latent Dirichlet allocation, NIPS
• [Smola and Narayanamurthy 2010] An architecture for parallel topic models, VLDB
• [Steyvers and Griffiths 2007] Probabilistic topic models, In Handbook of Latent
Semantic Analysis
• [Teh+ 2007] A collapsed variational Bayesian inference algorithm for latent
Dirichlet allocation, NIPS
• [Wallach+ 2009] Rethinking LDA: Why Priors Matter, NIPS
• [Wang+ 2009] PLDA: Parallel Latent Dirichlet Allocation for Large-scale Applications,
AAIM
• [Wilson and Chew 2010] Term Weighting Schemes for Latent Dirichlet Allocation,
ACL
• [Yan+ 2009] Parallel Inference for Latent Dirichlet Allocation on Graphics
Processing Units, NIPS
• [Yao+ 2009] Efficient methods for topic model inference on streaming document
collections, SIGKDD
39. 参考文献2
• [Bao and Chang 2010] AdHeat: an influence-based
diffusion model for propagating hints to match ads
• [Chen+ 2009] Collaborative filtering for Orkut
communities : discovery of user latent behavior
• [Lau+ 2010] Best topic word selection for topic
labelling, Colling
• [Phan+ 2008] Learning to classify short and sparse text
& web with hidden topics from large-scale data
collections
• [Wei and Croft 2006] LDA-based document models for
ad-hoc retrieval, SIGIR
