brendan_oconnor_ai brendan_oconnor_ai-2008 brendan_oconnor_ai-2008-114 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: With my friend Doug , I just finished making a game — PalinSpeak.com — where you can chat with a Sarah Palin simulator. Check it out, it’s the best thing to hit the Internet since sliced bread. I’ll post more the technical details (n-gram generation and query-answer matching, hurrah!) later…
sentIndex sentText sentNum sentScore
1 With my friend Doug , I just finished making a game — PalinSpeak. [sent-1, score-0.917]
2 Check it out, it’s the best thing to hit the Internet since sliced bread. [sent-3, score-0.698]
3 I’ll post more the technical details (n-gram generation and query-answer matching, hurrah! [sent-4, score-0.918]
wordName wordTfidf (topN-words)
[('doug', 0.348), ('matching', 0.318), ('finished', 0.296), ('hit', 0.279), ('generation', 0.279), ('technical', 0.279), ('internet', 0.244), ('later', 0.244), ('friend', 0.227), ('check', 0.22), ('details', 0.213), ('making', 0.202), ('game', 0.192), ('ll', 0.179), ('best', 0.158), ('thing', 0.155), ('post', 0.147), ('since', 0.106)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 114 brendan oconnor ai-2008-09-30-PalinSpeak.com
Introduction: With my friend Doug , I just finished making a game — PalinSpeak.com — where you can chat with a Sarah Palin simulator. Check it out, it’s the best thing to hit the Internet since sliced bread. I’ll post more the technical details (n-gram generation and query-answer matching, hurrah!) later…
2 0.1197309 137 brendan oconnor ai-2009-04-15-Pirates killed by President
Introduction: A lesson in x-axis scaling, and choosing which data to compare. Two current graphs making their rounds on the internet: ( about this .)
3 0.10166489 140 brendan oconnor ai-2009-05-18-Announcing TweetMotif for summarizing twitter topics
Introduction: Update (3/14/2010): There is now a TweetMotif paper . Last week, I, with my awesome friends David Ahn and Mike Krieger , finished hacking together an experimental prototype, TweetMotif , for exploratory search on Twitter. If you want to know what people are thinking about something, the normal search interface search.twitter.com gives really cool information, but it’s hard to wade through hundreds or thousands of results. We take tweets matching a query and group together similar messages, showing significant terms and phrases that co-occur with the user query. Try it out at tweetmotif.com . Here’s an example for a current hot topic, #WolframAlpha : It’s currently showing tweets that match both #WolframAlpha as well as two interesting bigrams: “queries failed” and “google killer”. TweetMotif doesn’t attempt to derive the meaning or sentiment toward the phrases — NLP is hard, and doing this much is hard enough! — but it’s easy for you to look at the tweet
4 0.092670195 58 brendan oconnor ai-2007-04-08-More fun with Gapminder - Trendalyzer
Introduction: Watching internet usage vs. income on the Gapminder.org visualizer is very interesting. Several things are quite apparent. (1) Internet usage exploded in all countries in the world. (2) Richer countries have more internet usage (linear relationship on the scatterplot), but it’s been increasing in all countries regardless. The animation has a few issues, of course — I think some of the funny, rapid movements at the very start are due to issues with when they started collecting reliable data on internet usage in the early 90′s, and the data probably started being more reliable at different times in different countries, etc. These are countervailing phenomena that require attention to variation within and across groups of data. I can’t imagine pie charts or tables of numbers that could ever convey this level of nuance. One last thing. Going to the linear scale (so you can see differences in large amounts of internet use), watch South Korea vs. United States. Korea, a c
5 0.072991192 25 brendan oconnor ai-2005-09-02-Submit your poker data!
Introduction: Upload your poker hand histories to www.pokernomics.com , economist Stephen Levitt’s fringe-of-economics project to study what are effective strategies in poker. This absolultely makes sense to me as an economics research project, only because I’m used to thinking of economics from the view of multi-agent systems and game theory… This is definitely all about game theory, maybe not economics. Game theory reaches beyond the bounds of the study of goods and services… blog entry on it
6 0.065876216 67 brendan oconnor ai-2007-07-07-Happiness incarnate on the Colbert Report
7 0.057025257 43 brendan oconnor ai-2006-07-30-4-move rock, paper, scissors!
8 0.056141604 132 brendan oconnor ai-2009-01-07-Love it and hate it, R has come of age
9 0.053645723 155 brendan oconnor ai-2009-09-20-Quiz: “art” and “pharmaceuticals”
10 0.053054973 71 brendan oconnor ai-2007-07-27-China: fines for bad maps
11 0.052176129 173 brendan oconnor ai-2011-08-27-CMU Twitter Part-of-Speech tagger 0.2
12 0.048267938 61 brendan oconnor ai-2007-05-24-Rock Paper Scissors psychology
13 0.04804438 187 brendan oconnor ai-2012-09-21-CMU ARK Twitter Part-of-Speech Tagger – v0.3 released
14 0.045589514 53 brendan oconnor ai-2007-03-15-Feminists, anarchists, computational complexity, bounded rationality, nethack, and other things to do
15 0.045381095 20 brendan oconnor ai-2005-07-11-guns, germs, & steel pbs show?!
16 0.044936471 131 brendan oconnor ai-2008-12-27-Facebook sentiment mining predicts presidential polls
17 0.042900287 59 brendan oconnor ai-2007-04-08-Random search engine searcher
18 0.037977628 138 brendan oconnor ai-2009-04-17-1 billion web page dataset from CMU
19 0.037385639 4 brendan oconnor ai-2005-05-16-Online Deliberation 2005 conference blog & more is up!
20 0.036915064 16 brendan oconnor ai-2005-07-05-finding some decision science blogs
topicId topicWeight
[(0, -0.094), (1, -0.038), (2, -0.055), (3, 0.058), (4, -0.082), (5, -0.081), (6, 0.053), (7, -0.043), (8, -0.014), (9, -0.002), (10, -0.15), (11, 0.145), (12, -0.066), (13, -0.091), (14, -0.013), (15, 0.007), (16, -0.153), (17, -0.002), (18, -0.045), (19, 0.073), (20, -0.131), (21, 0.106), (22, 0.085), (23, -0.025), (24, 0.163), (25, 0.041), (26, -0.008), (27, -0.057), (28, 0.166), (29, -0.132), (30, 0.104), (31, 0.023), (32, -0.077), (33, 0.03), (34, -0.029), (35, -0.105), (36, -0.052), (37, 0.011), (38, -0.12), (39, -0.071), (40, 0.011), (41, -0.008), (42, -0.127), (43, 0.067), (44, 0.05), (45, 0.115), (46, -0.107), (47, 0.061), (48, -0.15), (49, -0.117)]
simIndex simValue blogId blogTitle
same-blog 1 0.99735814 114 brendan oconnor ai-2008-09-30-PalinSpeak.com
Introduction: With my friend Doug , I just finished making a game — PalinSpeak.com — where you can chat with a Sarah Palin simulator. Check it out, it’s the best thing to hit the Internet since sliced bread. I’ll post more the technical details (n-gram generation and query-answer matching, hurrah!) later…
2 0.6926626 137 brendan oconnor ai-2009-04-15-Pirates killed by President
Introduction: A lesson in x-axis scaling, and choosing which data to compare. Two current graphs making their rounds on the internet: ( about this .)
3 0.55032283 58 brendan oconnor ai-2007-04-08-More fun with Gapminder - Trendalyzer
Introduction: Watching internet usage vs. income on the Gapminder.org visualizer is very interesting. Several things are quite apparent. (1) Internet usage exploded in all countries in the world. (2) Richer countries have more internet usage (linear relationship on the scatterplot), but it’s been increasing in all countries regardless. The animation has a few issues, of course — I think some of the funny, rapid movements at the very start are due to issues with when they started collecting reliable data on internet usage in the early 90′s, and the data probably started being more reliable at different times in different countries, etc. These are countervailing phenomena that require attention to variation within and across groups of data. I can’t imagine pie charts or tables of numbers that could ever convey this level of nuance. One last thing. Going to the linear scale (so you can see differences in large amounts of internet use), watch South Korea vs. United States. Korea, a c
4 0.35099682 140 brendan oconnor ai-2009-05-18-Announcing TweetMotif for summarizing twitter topics
Introduction: Update (3/14/2010): There is now a TweetMotif paper . Last week, I, with my awesome friends David Ahn and Mike Krieger , finished hacking together an experimental prototype, TweetMotif , for exploratory search on Twitter. If you want to know what people are thinking about something, the normal search interface search.twitter.com gives really cool information, but it’s hard to wade through hundreds or thousands of results. We take tweets matching a query and group together similar messages, showing significant terms and phrases that co-occur with the user query. Try it out at tweetmotif.com . Here’s an example for a current hot topic, #WolframAlpha : It’s currently showing tweets that match both #WolframAlpha as well as two interesting bigrams: “queries failed” and “google killer”. TweetMotif doesn’t attempt to derive the meaning or sentiment toward the phrases — NLP is hard, and doing this much is hard enough! — but it’s easy for you to look at the tweet
5 0.31543204 59 brendan oconnor ai-2007-04-08-Random search engine searcher
Introduction: It’s sweeping the internet — I wrote a little plugin for the firefox/internet explorer search box, so when you search it randomly picks one of several search engines. You get to see what’s out there (you mean there’s something besides Google?) in your daily searching. Search a Random Search Engine
6 0.31194466 43 brendan oconnor ai-2006-07-30-4-move rock, paper, scissors!
7 0.30564007 61 brendan oconnor ai-2007-05-24-Rock Paper Scissors psychology
8 0.27521354 173 brendan oconnor ai-2011-08-27-CMU Twitter Part-of-Speech tagger 0.2
9 0.27046159 66 brendan oconnor ai-2007-06-29-Evangelicals vs. Aquarians
10 0.25017965 67 brendan oconnor ai-2007-07-07-Happiness incarnate on the Colbert Report
11 0.22722478 25 brendan oconnor ai-2005-09-02-Submit your poker data!
12 0.22606486 78 brendan oconnor ai-2007-09-26-EEG for the Wii and in your basement
13 0.21724713 194 brendan oconnor ai-2013-04-16-Rise and fall of Dirichlet process clusters
14 0.21447992 158 brendan oconnor ai-2010-03-31-How Facebook privacy failed me
15 0.21090293 68 brendan oconnor ai-2007-07-08-Game outcome graphs — prisoner’s dilemma with FUN ARROWS!!!
16 0.20765351 146 brendan oconnor ai-2009-07-15-Beta conjugate explorer
17 0.20357248 69 brendan oconnor ai-2007-07-08-Washington in 1774
18 0.19628048 139 brendan oconnor ai-2009-04-22-Performance comparison: key-value stores for language model counts
19 0.19455618 141 brendan oconnor ai-2009-05-24-Zipf’s law and world city populations
20 0.19092496 128 brendan oconnor ai-2008-11-28-Calculating running variance in Python and C++
topicId topicWeight
[(16, 0.805)]
simIndex simValue blogId blogTitle
same-blog 1 0.99044645 114 brendan oconnor ai-2008-09-30-PalinSpeak.com
Introduction: With my friend Doug , I just finished making a game — PalinSpeak.com — where you can chat with a Sarah Palin simulator. Check it out, it’s the best thing to hit the Internet since sliced bread. I’ll post more the technical details (n-gram generation and query-answer matching, hurrah!) later…
2 0.94557142 161 brendan oconnor ai-2010-08-09-An ML-AI approach to P != NP
Introduction: Like everyone, I’ve been just starting to look at the new, tentative, proof that P != NP from Vinay Deolalikar. After reading the intro, what’s most striking is that probabilistic graphical models and mathematical logic are at the core of the proof. This feels like a machine learning and artificial intelligence-centric approach to me — very different from what you usually see in mainstream CS theory. (Maybe I should feel good that in my undergrad I basically stopped studying normal math and spent all my time with this weird stuff instead!) He devotes several chapters to an introduction to graphical models — Ising models, conditional independence, MRF’s, Hammersley-Clifford, and all that other stuff you see in Koller and Friedman or something — and then logic and model theory! I’m impressed.
3 0.89630729 62 brendan oconnor ai-2007-05-29-"Stanford Impostor"
Introduction: I’ve gotten a zillion emails about it by now, but it was recently found that a young woman had been living for a year in Stanford dorms claiming to be a freshman, when in fact she was not a student of any sort at all. This seems to have engendered much discussion in the Stanford community, e.g. here . (The LA Times has a decent piece). I mostly think the story is just sad, but a few thoughts: The security scare angle is a bit ridiculous, as is the blame game being played. I suspect the Stanford community is naive and high-strung about these things. My friends who went to state schools say things like this happen all the time. Besides, staffs in co-ops are sometimes happy to let vagrants hang out. The motivations for and mechanics of her deception are clearly the important part of this story (e.g. how did she live with herself? ) Her first two quarters were in Kimball, then then her last quarter was in Okada. I’m surprised the Kimball staff weren’t enough in-touch wit
4 0.76713467 97 brendan oconnor ai-2008-03-24-Quick-R, the only decent R documentation on the internet
Introduction: For R users or wannabes… I really love R, but it has horrid documentation and a steep learning curve. Recently I was introduced to Quick-R , a really excellent documentation site. I think it’s made the system dramatically more useful for me.
5 0.29652405 160 brendan oconnor ai-2010-04-22-Updates: CMU, Facebook
Introduction: It’s been a good year. Last fall I started a master’s program in the Language Technologies department at CMU SCS , taking some great classes, hanging out with a cool lab , and writing two new papers (for ICWSM , involving Twitter: polls and tweetmotif ; also did some coref work, financial text regression stuff, and looked at social lexicography .) I also applied to CS and stats PhD programs at several universities. Next year I’ll be starting the PhD program in the Machine Learning Department here at CMU. I’m excited! Just the other day I was looking at videos on my old hard drive and found a presentation by Tom Mitchell on “the Discipline of Machine Learning” that I downloaded back in 2007 or so. (Can’t find it online right now, but this is similar .) That might be where I heard of the department first. Maybe some day I will be smarter than the guy who wrote this rant (though I am much more pro-stats and anti-ML these days…). Also, I was recently named a fina
6 0.22126096 129 brendan oconnor ai-2008-12-03-Statistics vs. Machine Learning, fight!
7 0.19661543 140 brendan oconnor ai-2009-05-18-Announcing TweetMotif for summarizing twitter topics
9 0.1521904 12 brendan oconnor ai-2005-07-02-$ echo {political,social,economic}{cognition,behavior,systems}
10 0.15121794 147 brendan oconnor ai-2009-07-22-FFT: Friedman + Fortran + Tricks
11 0.14895226 166 brendan oconnor ai-2011-03-02-Poor man’s linear algebra textbook
12 0.14693725 132 brendan oconnor ai-2009-01-07-Love it and hate it, R has come of age
13 0.14386462 135 brendan oconnor ai-2009-02-23-Comparison of data analysis packages: R, Matlab, SciPy, Excel, SAS, SPSS, Stata
14 0.14225693 122 brendan oconnor ai-2008-11-05-Obama street celebrations in San Francisco
15 0.1348471 188 brendan oconnor ai-2012-10-02-Powerset’s natural language search system
16 0.130466 158 brendan oconnor ai-2010-03-31-How Facebook privacy failed me
17 0.12959877 61 brendan oconnor ai-2007-05-24-Rock Paper Scissors psychology
19 0.12814367 88 brendan oconnor ai-2008-01-05-Indicators of a crackpot paper
20 0.12708433 133 brendan oconnor ai-2009-01-23-SF conference for data mining mercenaries