¿Qué es Unigram en Python?

What is Unigram in Python?

A 1-gram (or unigram) is a one-word sequence. For the above sentence, the unigrams would simply be: “I”, “love”, “reading”, “blogs”, “about”, “data”, “science”, “on”, “Analytics”, “Vidhya”. A 2-gram (or bigram) is a two-word sequence of words, like “I love”, “love reading”, or “Analytics Vidhya”.

We can use build in functions in Python to generate n-grams quickly. Let’s take the following sentence as a sample input: s = “”” Natural-language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages. “””

1. Read the dataset. df = pd.read_csv(‘dataset.csv’, skiprows = 6, index_col = “No”)
2. Collect all available months. df[“Month”] = df[“Date(ET)”].apply(lambda x : x.split(‘/’)[0])
3. Create tokens of all tweets per month.
4. Create bigrams per month.
5. Count bigrams per month.
6. Wrap up the result in neat dataframes.

The unigram model is also known as the bag of words model. Estimating the relative likelihood of different phrases is useful in many natural language processing applications, especially those that generate text as an output.

An N-gram means a sequence of N words. So for example, “Medium blog” is a 2-gram (a bigram), “A Medium blog post” is a 4-gram, and “Write on Medium” is a 3-gram (trigram). Well, that wasn’t very interesting or exciting. True, but we still have to look at the probability used with n-grams, which is quite interesting.

An N-gram model is built by counting how often word sequences occur in corpus text and then estimating the probabilities. Since a simple N-gram model has limitations, improvements are often made via smoothing, interpolation and backoff.

In natural language processing, an n-gram is an arrangement of n words. For example “Python” is a unigram (n = 1), “Data Science” is a bigram (n = 2), “Natural language preparing” is a trigram (n = 3) etc.

Language models are used in speech recognition, machine translation, part-of-speech tagging, parsing, Optical Character Recognition, handwriting recognition and information retrieval. Traditional language models have performed reasonably well for many of these use cases.

Parameters are the key to machine learning algorithms. They’re the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well.

For example, to compute a particular bigram probability of a word y given a previous word x, you can determine the count of the bigram C(xy) and normalize it by the sum of all the bigrams that share the same first-word x.

For example “Python” is a unigram (n = 1), “Data Science” is a bigram (n = 2), “Natural language preparing” is a trigram (n = 3) etc.Here our focus will be on implementing the unigrams (single words) models in python. 1. It depends on the occurrence of the word among all the words in the dataset.

NLTK Everygrams NTK provides another function everygrams that converts a sentence into unigram, bigram, trigram, and so on till the ngrams, where n is the length of the sentence. In short, this function generates ngrams for all possible values of n. Let us understand everygrams with a simple example below.

If efficiency is an issue and you have to build multiple different n-grams, but you want to use pure python I would do:

Final AI course of CE department at Amirkabir University of Technology (Tehran Polytechnic) – Winter 2020. Sentiment Classification exercise with perceptron, feed-forward multilayer net, LSTM RNN, and RCNN! Simple language model for computing unigram frequencies.