Travelled to:1 × Australia
1 × France
3 × Canada
5 × USA
Collaborated with:J.Heer S.D.Kamvar D.Klein D.Ramage A.Y.Ng S.I.Wang J.Chuang S.Gupta S.Green S.T.Dumais K.Toutanova M.Savva A.X.Chang P.Hanrahan R.Socher C.C.Lin D.L.MacLean A.Lembke
Talks about:model (5) interpret (3) topic (3) constraint (2) languag (2) cluster (2) analysi (2) natur (2) level (2) forum (2)
Person: Christopher D. Manning
DBLP: Manning:Christopher_D=
Contributed to:
2015
20142013
2013201320122011
201120042002Wrote 11 papers:
- CSCW-2015-MacLeanGLMH #analysis #health #named #online
- Forum77: An Analysis of an Online Health Forum Dedicated to Addiction Recovery (DLM, SG, AL, CDM, JH), pp. 1511–1526.
- CHI-2014-SavvaCMH #automation #generative #keyword #named
- TransPhoner: automated mnemonic keyword generation (MS, AXC, CDM, PH), pp. 3725–3734.
- CHI-2013-GreenHM
- The efficacy of human post-editing for language translation (SG, JH, CDM), pp. 439–448.
- ICML-c2-2013-WangM #performance
- Fast dropout training (SIW, CDM), pp. 118–126.
- ICML-c3-2013-ChuangGMH #topic
- Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment (JC, SG, CDM, JH), pp. 612–620.
- CHI-2012-ChuangRMH #analysis #design #modelling #trust #visualisation
- Interpretation and trust: designing model-driven visualizations for text analysis (JC, DR, CDM, JH), pp. 443–452.
- ICML-2011-SocherLNM #natural language #network #parsing #recursion
- Parsing Natural Scenes and Natural Language with Recursive Neural Networks (RS, CCYL, AYN, CDM), pp. 129–136.
- KDD-2011-RamageMD #mining #modelling #topic
- Partially labeled topic models for interpretable text mining (DR, CDM, STD), pp. 457–465.
- ICML-2004-ToutanovaMN #dependence #learning #modelling #random #word
- Learning random walk models for inducing word dependency distributions (KT, CDM, AYN).
- ICML-2002-KamvarKM #algorithm #approach #clustering #modelling #using
- Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach (SDK, DK, CDM), pp. 283–290.
- ICML-2002-KleinKM #clustering #constraints #information management
- From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering (DK, SDK, CDM), pp. 307–314.