I have two files check.txt and orig.txt. I want to check every word in check.txt and see if it matches with any word in orig.txt. If it does match then the code should replace that word with its first match otherwise it should leave the word as it is. But somehow its not working as required. Kindly help.
check.txt looks like this:
ukrain
troop
force
and orig.txt looks like:
ukraine cnn should stop pretending & announce: we will not report news while it reflects bad on obama #bostonglobe #crowleycnn #hardball
rt #cbcnews: breaking: .#vice journalist #simonostrovsky, held in #ukraine now free and safe http://t.co/sgxbedktlu http://t.co/jduzlg6jou
russia 'outraged' at deadly shootout in east #ukraine - moscow:... http://t.co/nqim7uk7zg
#groundtroops #russianpresidentvladimirputin
http://pastebin.com/XJeDhY3G
f = open('check.txt','r')
orig = open('orig.txt','r')
new = open('newfile.txt','w')
for word in f:
for line in orig:
for word2 in line.split(" "):
word2 = word2.lower()
if word in word2:
word = word2
else:
print('not found')
new.write(word)
There are two problems with your code:
when you loop over the words in f, each word will still have a new line character, so your in check does not work
you want to iterate orig for each of the words from f, but files are iterators, being exhausted after the first word from f
You can fix those by doing word = word.strip() and orig = list(orig), or you can try something like this:
# get all stemmed words
stemmed = [line.strip() for line in f]
# set of lowercased original words
original = set(word.lower() for line in orig for word in line.split())
# map stemmed words to unstemmed words
unstemmed = {word: None for word in stemmed}
# find original words for word stems in map
for stem in unstemmed:
for word in original:
if stem in word:
unstemmed[stem] = word
print unstemmed
Or shorter (without that final double loop), using difflib, as suggested in the comments:
unstemmed = {word: difflib.get_close_matches(word, original, 1) for word in stemmed}
Also, remember to close your files, or use the with keyword to close them automatically.
Related
I want to use Word2Vec to represent words by vectors.
If there are 2 identical words in the Word2Vec's input,
is it possible to get a different representation for them?
Are there different methods to solve this issue?
First of all, great question.
I found a link that explains how to avoid duplicates when representing words with vectors using python.
https://towardsdatascience.com/learn-word2vec-by-implementing-it-in-tensorflow-45641adaf2ac
You can split the text and avoid word duplication in the following way:
corpus_raw = 'He is the king . The king is royal . She is the royal queen '
# convert to lower case
corpus_raw = corpus_raw.lower()
# ---- dictionary words -> int and int -> words ----
words = []
for word in corpus_raw.split():
if word != '.': # because we don't want to treat . as a word
words.append(word)
words = set(words) # so that all duplicate words are removed
word2int = {}
int2word = {}
vocab_size = len(words) # gives the total number of unique words
for i,word in enumerate(words):
word2int[word] = i
int2word[i] = word
print(words)
How to remove nonwords from this without using any library in python?
By a word I mean strings containing only the English letters plus ”’” and ”-”.
Hence, we consider words like ”can’t”, ”John’s”, and ”full-time” as valid words.
Furthermore, a word doesn’t contain any digits, or symbols like ”.”, ”,”, ”!”, ”?”, etc.
The only single-letter words in English are ”a” and ”I”
99998 Zwizwai confirmed that there were attempts to place him, together with Mudzuri, on the
sanctions list, describing them as “a Nicodimous diploآکروجت تاج طلایی نیروی هوایی شاهنشاهی ایران ” by
some fellow MDC-T
______________________________________
| |
| The Cast: (in order of appearance) |
|______________________________________|
100000 在大會上，女神為女性安全再次發聲，代表"HeforShe"運動發表消除校園暴力，保障女性安全（calling
on universities to ensure the safety of women on campus）的演講。
this what i tried
for word in row.split():
if word.isalpha() == False:
word = word[:-1]
print (word)
words.append(word.lower())
return words
Try:
words = list()
with open("file.txt", errors="ignore") as infile:
for row in infile:
for word in row.split():
if word.strip('"(.,!?):').replace("'","").replace("-","").isalpha():
words.append(word.strip('"(.,!?):').lower())
>>> " ".join(words)
'zwizwai confirmed that there were attempts to place him together with mudzuri on the sanctions list describing them as nicodimous by some fellow mdc-t the cast in order of appearance on universities to ensure the safety of women on'
Try this:
for word in row.split():
word = word.replace("!", "").replace("?", "").replace(".", "") # since they are included in a word if it's splitted by spaces
if word.replace("'", "").replace("-", "").isalpha():
words.append(word.lower())
I'm attempting to capitalize all words in a section of text that only appear once. I have the bit that finds which words only appear once down, but when I go to replace the original word with the .upper version, a bunch of other stuff gets capitalized too. It's a small program, so here's the code.
from collections import Counter
from string import punctuation
path = input("Path to file: ")
with open(path) as f:
word_counts = Counter(word.strip(punctuation) for line in f for word in line.replace(")", " ").replace("(", " ")
.replace(":", " ").replace("", " ").split())
wordlist = open(path).read().replace("\n", " ").replace(")", " ").replace("(", " ").replace("", " ")
unique = [word for word, count in word_counts.items() if count == 1]
for word in unique:
print(word)
wordlist = wordlist.replace(word, str(word.upper()))
print(wordlist)
The output should be 'Genesis 37:1 Jacob lived in the land of his father's SOJOURNINGS, in the land of Canaan., as sojournings is the first word that only appears once. Instead, it outputs GenesIs 37:1 Jacob lIved In the land of hIs FATher's SOJOURNINGS, In the land of Canaan. Because some of the other letters appear in keywords, it tries to capitalize them as well.
Any ideas?
I rewrote the code pretty significantly since some of the chained replace calls might prove to be unreliable.
import string
# The sentence.
sentence = "Genesis 37:1 Jacob lived in the land of his father's SOJOURNINGS, in the land of Canaan."
rm_punc = sentence.translate(None, string.punctuation) # remove punctuation
words = rm_punc.split(' ') # split spaces to get a list of words
# Find all unique word occurrences.
single_occurrences = []
for word in words:
# if word only occurs 1 time, append it to the list
if words.count(word) == 1:
single_occurrences.append(word)
# For each unique word, find it's index and capitalize the letter at that index
# in the initial string (the letter at that index is also the first letter of
# the word). Note that strings are immutable, so we are actually creating a new
# string on each iteration. Also, sometimes small words occur inside of other
# words, e.g. 'an' inside of 'land'. In order to make sure that our call to
# `index()` doesn't find these small words, we keep track of `start` which
# makes sure we only ever search from the end of the previously found word.
start = 0
for word in single_occurrences:
try:
word_idx = start + sentence[start:].index(word)
except ValueError:
# Could not find word in sentence. Skip it.
pass
else:
# Update counter.
start = word_idx + len(word)
# Rebuild sentence with capitalization.
first_letter = sentence[word_idx].upper()
sentence = sentence[:word_idx] + first_letter + sentence[word_idx+1:]
print(sentence)
Text replacement by patters calls for regex.
Your text is a bit tricky, you have to
remove digits
remove punktuations
split into words
care about capitalisation: 'It's' vs 'it's'
only replace full matches 'remote' vs 'mote' when replacing mote
etc.
This should do this - see comments inside for explanations:
bible.txt is from your link
from collections import Counter
from string import punctuation , digits
import re
from collections import defaultdict
with open(r"SO\AllThingsPython\P4\bible.txt") as f:
s = f.read()
# get a set of unwanted characters and clean the text
ps = set(punctuation + digits)
s2 = ''.join( c for c in s if c not in ps)
# split into words
s3 = s2.split()
# create a set of all capitalizations of each word
repl = defaultdict(set)
for word in s3:
repl[word.upper()].add(word) # f.e. {..., 'IN': {'In', 'in'}, 'THE': {'The', 'the'}, ...}
# count all words _upper case_ and use those that only occure once
single_occurence_upper_words = [w for w,n in Counter( (w.upper() for w in s3) ).most_common() if n == 1]
text = s
# now the replace part - for all upper single words
for upp in single_occurence_upper_words:
# for all occuring capitalizations in the text
for orig in repl[upp]:
# use regex replace to find the original word from our repl dict with
# space/punktuation before/after it and replace it with the uppercase word
text = re.sub(f"(?<=[{punctuation} ])({orig})(?=[{punctuation} ])",upp, text)
print(text)
Output (shortened):
Genesis 37:1 Jacob lived in the land of his father's SOJOURNINGS, in the land of Canaan.
2 These are the GENERATIONS of Jacob.
Joseph, being seventeen years old, was pasturing the flock with his brothers. He was a boy with the sons of Bilhah and Zilpah, his father's wives. And Joseph brought a BAD report of them to their father. 3 Now Israel loved Joseph more than any other of his sons, because he was the son of his old age. And he made him a robe of many colors. [a] 4 But when his brothers saw that their father loved him more than all his brothers, they hated him
and could not speak PEACEFULLY to him.
<snipp>
The regex uses lookahead '(?=...)' and lookbehind '(?<=...)'syntax to make sure we replace only full words, see regex syntax.
I have a corpus of sentences in a specific domain.
I am looking for an open-source code/package, that I can give the data and it will generate a good, reliable language model. (Meaning, given a context, know the probability for each word).
Is there such a code/project?
I saw this github repo: https://github.com/rafaljozefowicz/lm, but it didn't work.
I recommend writing your own basic implementation. First, we need some sentences:
import nltk
from nltk.corpus import brown
words = brown.words()
total_words = len(words)
sentences = list(brown.sents())
sentences is now a list of lists. Each sublist represents a sentence with each word as an element. Now you need to decide whether or not you want to include punctuation in your model. If you want to remove it, try something like the following:
punctuation = [",", ".", ":", ";", "!", "?"]
for i, sentence in enumerate(sentences.copy()):
new_sentence = [word for word in sentence if word not in punctuation]
sentences[i] = new_sentence
Next, you need to decide whether or not you care about capitalization. If you don't care about it, you could remove it like so:
for i, sentence in enumerate(sentences.copy()):
new_sentence = list()
for j, word in enumerate(sentence.copy()):
new_word = word.lower() # Lower case all characters in word
new_sentence.append(new_word)
sentences[i] = new_sentence
Next, we need special start and end words to represent words that are valid at the beginning and end of sentences. You should pick start and end words that don't exist in your training data.
start = ["<<START>>"]
end = ["<<END>>"]
for i, sentence in enumerate(sentences.copy()):
new_sentence = start + sentence + end
sentences[i] = new_sentence
Now, let's count unigrams. A unigram is a sequence of one word in a sentence. Yes, a unigram model is just a frequency distribution of each word in the corpus:
new_words = list()
for sentence in sentences:
for word in sentence:
new_words.append(word)
unigram_fdist = nltk.FreqDist(new_words)
And now it's time to count bigrams. A bigram is a sequence of two words in a sentence. So, for the sentence "i am the walrus", we have the following bigrams: "<> i", "i am", "am the", "the walrus", and "walrus <>".
bigrams = list()
for sentence in sentences:
new_bigrams = nltk.bigrams(sentence)
bigrams += new_bigrams
Now we can create a frequency distribution:
bigram_fdist = nltk.ConditionalFreqDist(bigrams)
Finally, we want to know the probability of each word in the model:
def getUnigramProbability(word):
if word in unigram_fdist:
return unigram_fdist[word]/total_words
else:
return -1 # You should figure out how you want to handle out-of-vocabulary words
def getBigramProbability(word1, word2):
if word1 not in bigram_fdist:
return -1 # You should figure out how you want to handle out-of-vocabulary words
elif word2 not in bigram_fdist[word1]:
# i.e. "word1 word2" never occurs in the corpus
return getUnigramProbability(word2)
else:
bigram_frequency = bigram_fdist[word1][word2]
unigram_frequency = unigram_fdist[word1]
bigram_probability = bigram_frequency / unigram_frequency
return bigram_probability
While this isn't a framework/library that just builds the model for you, I hope seeing this code has demystified what goes on in a language model.
You might try word_language_model from PyTorch examples. There just might be an issue if you have a big corpus. They load all data in memory.
I am new to python and am trying to find the largest word in the alice_in_worderland.txt. I think I have a good system set up ("See Below"), but my output is returning a "word" with dashes connecting multiple words. Is there someway to remove the dashes in the input of the file? For the text file visit here
sample from text file:
That's very important,' the King said, turning to the jury. They were
just beginning to write this down on their slates, when the White
Rabbit interrupted: UNimportant, your Majesty means, of course,' he
said in a very respectful tone, but frowning and making faces at him
as he spoke. " UNimportant, of course, I meant,' the King hastily
said, and went on to himself in an undertone, important--unimportant--
unimportant--important--' as if he were trying which word sounded
best."
code:
#String input
with open("alice_in_wonderland.txt", "r") as myfile:
string=myfile.read().replace('\n','')
#initialize list
my_list = []
#Split words into list
for word in string.split(' '):
my_list.append(word)
#initialize list
uniqueWords = []
#Fill in new list with unique words to shorten final printout
for i in my_list:
if not i in uniqueWords:
uniqueWords.append(i)
#Legnth of longest word
count = 0
#Longest word place holder
longest = []
for word in uniqueWords:
if len(word)>count:
longest = word
count = len(longest)
print longest
>>> import nltk # pip install nltk
>>> nltk.download('gutenberg')
>>> words = nltk.corpus.gutenberg.words('carroll-alice.txt')
>>> max(words, key=len) # find the longest word
'disappointment'
Here's one way using re and mmap:
import re
import mmap
with open('your alice in wonderland file') as fin:
mf = mmap.mmap(fin.fileno(), 0, access=mmap.ACCESS_READ)
words = re.finditer('\w+', mf)
print max((word.group() for word in words), key=len)
# disappointment
Far more efficient than loading the file to physical memory.
Use str.replace to replace the dashes with spaces (or whatever you want). To do this, simply add another call to replace after the first call on line 3:
string=myfile.read().replace('\n','').replace('-', ' ')