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- NLP (Natural Language Processing)
- Emulated Natural Language
- NLTK wordnet
- NLTK Rephrasing
In this project, we will use data from scraping negative reviews of electronic goods provided by customers on Amazon. Data courtesy of http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html.
Because the data are in the format of negatif.review, we open it using BeautifulSoup library.
negative_reviews = BeautifulSoup(open('data_input/negative.review').read())
negative_reviews = negative_reviews.findAll('review_text')
The important thing to note, as for any other artificial intelligence or machine learning model, we neew to train the model with a huge corpus data. Once we do that, the system, or the NLP model will have a pretty good idea of the “probability” of the occurrence of a word after a certain word. So hoping that we have trained our model with a huge corpus of data, we’ll assume that the model gave us the correct answer.
N-gram probabilities are used to predict the next word given a sequence of words in a sentence. N-gram probabilities are calculated and associated models to give the most possible word fill in the next word.
Intuitive Formulation
Let’s start with equation P(w|h), the probability of word w, given some history, h.
Steps:
- Label our words into:
w(1), w(2), w(3), ..., w(N) - Model
w(i)usingw(1), ..., w(i-1)andw(i+1), ..., w(N) - Probabilistic
P{w(i)|w(1), ..., w(i-1), w(i+1), ..., w(N)}
We will specific instance of "N-grams", where N=3. So the probabilities model is P{w(i)|w(i-1), w(i+1)}. We will use dictionary with {w(i-1), w(i+1)} as the key, and lookup w(i). It is sort of like Markov model where P{w(i)|w(i-1)}. This are "unsupervised learning" because the are no labels.
We will extract trigrams and insert into dictionary. Where (w1, w3) is the key, [w2] are the values:
trigrams = {}
tokens = nltk.tokenize.word_tokenize(text_article)
for i in range(len(tokens) - 2):
key_word = (tokens[i], tokens[i+2])
if key_word not in trigrams:
trigrams[key_word] = []
trigrams[key_word].append(tokens[i+1])
Next, we will turn each array of middle-words into probability vector
trigram_probabilities = {}
for key_word, words in iteritems(trigrams):
# create a dictionary of word -> count
if len(set(words)) > 1:
# only do this when there are different possibilities for a middle word
d = {}
n = 0
for w in words:
if w not in d:
d[w] = 0
d[w] += 1
n += 1
for w, c in iteritems(d):
d[w] = float(c) / n
trigram_probabilities[key_word] = d
Choose a random sample from dictionary where values are the probabilities:
r = random.random()
cumulative = 0
for w, p in iteritems(d):
cumulative += p
if r < cumulative:
return w
- https://web.stanford.edu/~jurafsky/slp3/3.pdf
- http://www.phon.ox.ac.uk/jcoleman/old_SLP/Lecture_6/trigram-modelling.html
- https://towardsdatascience.com/introduction-to-language-models-n-gram-e323081503d9
- https://towardsdatascience.com/understanding-word-n-grams-and-n-gram-probability-in-natural-language-processing-9d9eef0fa058