feat(blogs-corpus): new corpus for word2vec

This commit is contained in:
Mahdi Dibaiee 2016-09-19 16:00:45 +04:30
parent f16cc26798
commit d9d24f69a6
6 changed files with 62 additions and 13 deletions

3
.gitmodules vendored
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@ -4,3 +4,6 @@
[submodule "examples/notMNIST"]
path = examples/notMNIST
url = git@github.com:mdibaiee/notMNIST
[submodule "examples/blogs-corpus"]
path = examples/blogs-corpus
url = git@github.com:mdibaiee/blogs-corpus

1
examples/blogs-corpus Submodule

@ -0,0 +1 @@
Subproject commit f242d6e602cacf41cd662bfca57501638bcf7c2a

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@ -12,27 +12,45 @@ module Main where
import Data.Function (on)
import Numeric.LinearAlgebra
import System.IO
import System.Directory
import Data.List.Split
import Control.Exception (evaluate)
import Debug.Trace
import Data.Char
import System.Random
rf :: FilePath -> IO String
rf p = do
hs <- openFile p ReadMode
hSetEncoding hs latin1
content <- evaluate =<< hGetContents hs
length content `seq` hClose hs
return content
main = do
setStdGen (mkStdGen 100)
sws <- lines <$> readFile "examples/stopwords"
{-ds <- do
content <- readFile "examples/doc-classifier-data/data-reuters"
let splitted = splitOn (replicate 10 '-' ++ "\n") content
d = concatMap (tail . lines) (take 100 splitted)
return $ removeWords sws d-}
--let ds = ["I like deep learning", "I like NLP", "I enjoy flying"]
-- real data, takes a lot of time to train
{-ds <- do-}
{-files <- filter ((/= "xml") . take 3 . reverse) <$> listDirectory "examples/blogs-corpus/"-}
{-contents <- mapM (rf . ("examples/blogs-corpus/" ++)) files-}
{-let texts = map (unwords . splitOn "&nbsp;") contents-}
{-let tags = ["<Blog>", "</Blog>", "<date>", "</date>", "<post>", "</post>", "&nbsp;"]-}
{-return $ map cleanText $ removeWords (sws ++ tags) texts-}
let ds = ["the king loves the queen", "the queen loves the king",
"the dwarf hates the king", "the queen hates the dwarf",
"the dwarf poisons the king", "the dwarf poisons the queen"]
let session = def { learningRate = 5e-2
let session = def { learningRate = 1e-1
, batchSize = 1
, epochs = 100
, epochs = 200
, debug = True
} :: Session
w2v = def { docs = ds
, dimensions = 50
, dimensions = 25
, method = SkipGram
, window = 2
} :: Word2Vec
@ -49,10 +67,25 @@ module Main where
return ()
cleanText :: String -> String
cleanText string =
let notag = unwords $ filter ((/= "<date>") . take 6) (words string)
ws = unwords $ filter (`notElem` ["urlLink"]) (words notag)
spacify = foldl (\acc x -> replace x ' ' acc) (trim ws) [',', '/', '-', '\n', '\r', '?', '.', '(', ')', '%', '$', '"', ';', ':', '!', '\'']
nonumber = filter (not . isNumber) spacify
lower = map toLower nonumber
in unwords . words $ lower
where
trim = f . f
where
f = reverse . dropWhile isSpace
replace needle replacement =
map (\c -> if c == needle then replacement else c)
removeWords :: [String] -> [String] -> [String]
removeWords ws documents =
map (rm ws) documents
map rm documents
where
rm list text =
unwords $ filter (`notElem` list) (words text)
rm text =
unwords $ filter (`notElem` ws) (words text)

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@ -16,6 +16,7 @@ cabal-version: >=1.10
library
hs-source-dirs: src
exposed-modules: Sibe, Sibe.NaiveBayes, Sibe.NLP, Sibe.Word2Vec, Sibe.Utils
ghc-options: -threaded -rtsopts -with-rtsopts=-N
build-depends: base >= 4.7 && < 5
, hmatrix
, random
@ -53,6 +54,8 @@ executable example-word2vec
, data-default-class
, split
, vector
, directory
, random
default-language: Haskell2010
executable example-424

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@ -26,6 +26,7 @@ module Sibe
sigmoid',
softmax,
softmax',
sampledSoftmax,
relu,
relu',
crossEntropy,
@ -181,6 +182,12 @@ module Sibe
where
sig x = 1 / max (1 + exp (-x)) 1e-10
-- used for negative sampling
sampledSoftmax :: Int -> Vector Double -> Vector Double
sampledSoftmax n x = cmap (\a -> exp a / s) x
where
s = V.sum . exp $ V.take n x
relu :: Vector Double -> Vector Double
relu = cmap (max 0.1)

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@ -14,6 +14,7 @@ module Sibe.Word2Vec
import Data.Default.Class
import Data.Function (on)
import Control.Monad
import System.Random
data W2VMethod = SkipGram | CBOW
data Word2Vec = Word2Vec { docs :: [String]
@ -27,8 +28,9 @@ module Sibe.Word2Vec
}
word2vec w2v session = do
seed <- newStdGen
let s = session { training = trainingData
, network = randomNetwork 0 (-1, 1) v [(dimensions w2v, (id, one))] (v, (softmax, one))
, network = randomNetwork 0 (-1, 1) v [(dimensions w2v, (id, one))] (v, (softmax, crossEntropy'))
}
when (debug s) $ do