PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤

More documents

Recommendations

Info

Biterm Topic Model(BTM) • Biterm: co-occurred word pairs in short text – "visit apple store" -> "visit apple", "visit store", "apple store“! • Model the generation of biterms with latent topic structure – a topic ~ a probability distribution over words – a corpus ~ a mixture of topics – a biterm ~ two i.i.d sample drawn from one topic
Comparison between different models LDA Mixture of Unigram BTM l Document level topic distribution – Suffer sparsity of the doc! l Model the generation of each word – Ignore context l Corpus level topic distribution – Alleviate doc sparsity l Single topic assumption in each document – Too strong assumption l Corpus level topic distribution – Alleviate doc sparsity l Model the generation of word pairs – Leverage context
Page 1 and 2: Big Data: Science or Gimmick! 程
Page 3 and 4: Is Big Data Big Value Predicti
Page 5 and 6: Big Challenges Exist in Big Da
Page 7 and 8: Topics in this talk Data Comple
Page 9 and 10: Topic Modeling meets Big Data
Page 11 and 12: Sparse and Meaningful Topics • L
Page 13 and 14: Group Sparse Topical Coding Group
Page 15 and 16: Results • Datasets - 20-newsgrou
Page 17 and 18: Short texts are prevalent Uncov
Page 19: Key idea of our approach • T
Page 23 and 24: Evaluation on Baidu Zhidao • Dat
Page 25 and 26: Ranking meets Big Data High com
Page 27 and 28: Top-‐k Learning to Rank Ran
Page 29 and 30: Top-‐k Ranking Framework Top
Page 31 and 32: Problems • Predict the percolati
Page 33 and 34: Challenges in the era of Big Data
Page 35 and 36: Can be useful on Social Networ
Page 37 and 38: Case1: Query the Real People o
Page 39 and 40: Summary • Three ScienAfic Chal

PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?

PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤