| julinclude("gotree.jl") | |
| Array{CountTrie,1} | |
| accept rate = 850654/850654 = 1.000 | |
| elapsed time: 33.986743839 seconds (5921922372 bytes allocated) | |
| .ITER 1 | |
| accept rate = 809118/850654 = 0.951 | |
| elapsed time: 36.449559574 seconds (6175698392 bytes allocated) | |
| .ITER 2 | |
| accept rate = 796254/850654 = 0.936 | |
| elapsed time: 30.21721326 seconds (6166426280 bytes allocated) |
| # http://mikelove.wordpress.com/2013/11/07/empirical-bayes/ | |
| # Stein's estimation rule and its competitors - an empirical Bayes approach | |
| # B Efron, C Morris, Journal of the American Statistical, 1973 | |
| n <- 1000 | |
| sigma.means <- 5 | |
| means <- rnorm(n, 0, sigma.means) | |
| # sigma.y <- 5 | |
| library(manipulate) | |
| manipulate({ |
| package nlp; | |
| import java.io.IOException; | |
| import java.io.StringReader; | |
| import edu.stanford.nlp.io.IOUtils; | |
| import edu.stanford.nlp.ling.CoreAnnotations; | |
| import edu.stanford.nlp.ling.CoreLabel; | |
| import edu.stanford.nlp.trees.LabeledScoredTreeFactory; | |
| import edu.stanford.nlp.trees.PennTreeReader; |
In Thomas Bass's The Predictors,
In one scene they are talking to a potential investor who kept wanting to talk about their earlier complexity theory work,
Marrin wanted chaos and fractals, and we were offering engineering and statistics.
I remember reading that and thinking, wait, but isn't that why I'm reading this book, and why the book is supposed to be interesting? I stopped reading the book at some point after that.
| This is a edu.stanford.nlp.trees.Tree | |
| tree.setSpans(); // these are 0-indexed, inclusive-inclusive | |
| tree.indexSpans(); // yup, this saves stuff to a different place. apparently 0-indexed inclusive-exclusive | |
| tree.indexLeaves(); // these are 1-indexed (!!) stanfordnlp coref code heavily uses them |
| === | |
| [Update July 25... and after https://gist.github.com/leondz/6082658 ] | |
| OK never mind the questions about cross-validation versus a smaller eval split and all that. | |
| We evaluated our tagger (current release, version 0.3.2), | |
| trained and evaluated on the same splits as the GATE tagger | |
| (from http://gate.ac.uk/wiki/twitter-postagger.html and specifically twitie-tagger.zip) | |
| and it gets 90.4% accuracy (significantly different than the GATE results). |
| """ | |
| Wrapper around morpha from | |
| http://www.informatics.sussex.ac.uk/research/groups/nlp/carroll/morph.html | |
| Vaguely follows edu.stanford.nlp.Morphology except we implement with a pipe. | |
| hacky. Would be nice to use cython/swig/ctypes to directly embed morpha.yy.c | |
| as a python extension. | |
| TODO compare linguistic quality to lemmatizer in python's "pattern" package |
| mcmc convergence diagnostics | |
| https://github.com/brendano/conplot | |
| ~/myutil % grep totalLL log|awk '{print $2}' | conplot | |
| -2.87e+06 o | |
| oooo | |
| o oooooooooooooooooooooooooooooooooooooo | |
| oooo | |
| -2.93e+06 ooooo |
| ---------- Forwarded message ---------- | |
| From: Daniel Bauer <@cs.columbia.edu> | |
| Date: Thu, Feb 21, 2013 at 6:19 PM | |
| Subject: Mon 2/25 - Brendan O'Connor | |
| To: nlp-announce | |
| Cc: Brendan O'Connor <brenocon@cmu.edu> | |
| Dear all, |
| --- CountryInfo.AllNations.txt 2013-01-23 12:27:37.000000000 -0500 | |
| +++ CountryInfo.CleanedNations.txt 2013-01-23 12:27:37.000000000 -0500 | |
| @@ -4458,10 +4458,6 @@ | |
| [USAVIR] VIRGIN_ISLANDS_OF_THE_U.S._ | |
| [USAVIR] VIRGIN_ISLANDS_OF_THE_UNITED_STATES_ | |
| [USAVIR] VIRGIN_ISLANDS_OF_THE_US_ | |
| -[USA] ALABAMA_ | |
| -[USA] ALASKA_ | |
| -[USA] ALOHA_STATE_ | |
| -[USA] AMERICALAND_ |