How to separate two concatenaded words - python-3.x

Question

I have a review dataset and I want to process it using NLP techniques. I did all the preprocessing stages (remove stop words, stemming, etc.). My problem is that there are some words, which are connected to each other and my function doesn't understand those. Here is an example:
Great services. I had a nicemeal and I love it a lot.
How can I correct it from nicemeal to nice meal?

Answer 1

Peter Norvig has a nice solution to the word segmentation problem that you are encountering. Long story short, he uses a large dataset of word (and bigram) frequencies and some dynamic programming to split long strings of connected words into their most likely segmentation.
You download the zip file with the source code and the word frequencies and adapt it to your use case. Here is the relevant bit, for completeness.
def memo(f):
"Memoize function f."
table = {}
def fmemo(*args):
if args not in table:
table[args] = f(*args)
return table[args]
fmemo.memo = table
return fmemo
#memo
def segment(text):
"Return a list of words that is the best segmentation of text."
if not text: return []
candidates = ([first]+segment(rem) for first,rem in splits(text))
return max(candidates, key=Pwords)
def splits(text, L=20):
"Return a list of all possible (first, rem) pairs, len(first)<=L."
return [(text[:i+1], text[i+1:])
for i in range(min(len(text), L))]
def Pwords(words):
"The Naive Bayes probability of a sequence of words."
return product(Pw(w) for w in words)
#### Support functions (p. 224)
def product(nums):
"Return the product of a sequence of numbers."
return reduce(operator.mul, nums, 1)
class Pdist(dict):
"A probability distribution estimated from counts in datafile."
def __init__(self, data=[], N=None, missingfn=None):
for key,count in data:
self[key] = self.get(key, 0) + int(count)
self.N = float(N or sum(self.itervalues()))
self.missingfn = missingfn or (lambda k, N: 1./N)
def __call__(self, key):
if key in self: return self[key]/self.N
else: return self.missingfn(key, self.N)
def datafile(name, sep='\t'):
"Read key,value pairs from file."
for line in file(name):
yield line.split(sep)
def avoid_long_words(key, N):
"Estimate the probability of an unknown word."
return 10./(N * 10**len(key))
N = 1024908267229 ## Number of tokens
Pw = Pdist(datafile('count_1w.txt'), N, avoid_long_words)
You can also use the segment2 method as it uses bigrams and is much more accurate.