Research: Difference between revisions

Revision as of 01:51, 8 September 2017

In the CLIP lab, we approach research on computational linguistics and information processing from a variety of angles. Some of our ongoing projects focus on the following challenges:

Computational psycholinguistics
Computational social science
Cross-language information retrieval
Data science for finance / social good
Deep learning
E-discovery
Pattern discover in graphs / ranking and recommendation
Human-in-the-loop machine learning
Machine translation
Mental health
Privacy-aware information retrieval
Speech retrieval
Urban computing / smart environments

CLIP research has been supported by the following organizations: NSF, DARPA, ARL, IARPA, OFR (Treasury), NIST, IMLS, Google, Yahoo and the World Bank.

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-==Machine Translation==
+In the CLIP lab, we approach research on computational linguistics and information processing from a variety of angles. Some of our ongoing projects focus on the following challenges:
-==Summarization ==
+* Computational psycholinguistics
+* Computational social science
+* Cross-language information retrieval
+* Data science for finance / social good
+* Deep learning
+* E-discovery
+* Pattern discover in graphs / ranking and recommendation
+* Human-in-the-loop machine learning
+* Machine translation
+* Mental health
+* Privacy-aware information retrieval
+* Speech retrieval
+* Urban computing / smart environments
-==Parsing and Tagging==
+CLIP research has been supported by the following organizations: NSF, DARPA, ARL, IARPA, OFR (Treasury), NIST, IMLS, Google, Yahoo and the World Bank.
-==Sentiment Analysis==
-==Bayesian Modeling==
-{| border="0" cellpadding="5" cellspacing="0" align="center"
-|-
-! colspan="3" style="background: #ffefef;" | Cross‐language Bayesian models for Web‐scale text analysis using MapReduce
-|-
-| PI
-| Jimmy Lin
-|-
-| Other Faculty
-| Jordan Boyd-Graber, Philip Resnik
-|-
-| Students
-| Lisa Simpson
-|-
-| style="border-bottom: 3px solid grey;" | Funding
-| style="border-bottom: 3px solid grey;" | NSF 1018625
-|-
-| colspan="3" align="center" |
-While I agree with Reviewer 3 that the experiments involve an admirable diversity of datasets (as compared to a typical NIPS paper), I personally don't feel that the contribution has been compellingly described or validated, and suspect on the basis of the presentation that the actual improvements are pretty marginal. I attached a confidence of 4 to my review partly because of the difficulty I had in assessing the precise contribution --- I read and write lots of structured generative models and it took several tries for me to get a sense for how exactly this paper differed from previous efforts --- and partly because I'm not in the trenches of jointly modeling text and relational data, so maybe somebody closer to those datasets would be more informed and therefore more impressed by the results as reported.
-|}

Research: Difference between revisions

Computational Linguistics and Information Processing

Revision as of 01:51, 8 September 2017