gavinmh

asdasdasdasdas

gavinmh

# -*- coding: utf-8 -*-
"""
This is an example of a basic optical character recognition system.
Some components, such as the featurizer, are missing, and have been replaced
with data that I made up.

This system recognizes words produced from an alphabet of 2 letters: 'l' and 'o'.
Words that can be recognized include, 'lol', 'lolol', 'and loooooll'.
We'll assume that this system is used to digitize hand-written notes by Redditors,
or something. 

gavinmh

from __future__ import division
from nltk.corpus import wordnet as wn
from nltk.corpus import wordnet_ic
from nltk.metrics import edit_distance
from nltk.corpus.reader.wordnet import WordNetError
import numpy as np
import logging, os
import Alignment_sub

gavinmh

# -*- coding: utf-8 -*-
"""
Created on Fri Nov 23 11:25:40 2012

@author: gavin
"""
import logging
from nltk.corpus import wordnet as wn

class Alignment_sub:

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try:
    import cPickle as pickle
except:
    import pickle
    
from sklearn.ensemble import RandomForestClassifier
import logging, os


class Lexent_classifier_sub:

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import logging
import numpy as np
import Alignment_sub
import lexent_featurizer_sub

try:
    import cPickle as pickle
except:
    import pickle
    

gavinmh

Section "Device"
Identifier      "intel"
driver          "intel"
EndSection
 
Section "Device"
Identifier      "dl1"
driver          "displaylink"
Option  "fbdev" "/dev/fb1"
EndSection

gavinmh

# -*- coding: utf-8 *-*

'''
The following is a naive, unsupervised text summarizer.
It extracts N of the text's most salient sentences.
Salience is defined as the average of the tf-idf weights of the words in a sentence.
'''
from nltk import sent_tokenize, word_tokenize
from collections import Counter
from math import log10

gavinmh

# -*- coding: utf-8 -*-
'''

'''
from nltk import sent_tokenize, word_tokenize, pos_tag, ne_chunk


def extract_entities(text):
	entities = []
	for sentence in sent_tokenize(text):

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