Understanding Natural Language Processing

Natural Language Processing (NLP) is an interdisciplinary domain that merges linguistics, computer science, and artificial intelligence to facilitate interactions between computers and human language. It focuses on programming machines to handle and analyze extensive sets of natural language data.

If you're looking to specialize in NLP within the broader field of Data Science, it's vital to familiarize yourself with essential concepts that can advance your career. Unfortunately, many online resources presuppose a certain level of familiarity with specific terminology, which may not be the case for everyone.

1. Corpus

The term "corpus," derived from Latin meaning "body," signifies a collection of documents. Just as a body comprises various parts, a corpus consists of multiple texts or documents. For instance, a corpus could consist of several religious texts where each individual text is considered a document.

2. Tokenization

Tokenization is a crucial preprocessing step in NLP, aimed at breaking down phrases, sentences, or entire documents into smaller units known as tokens.

3. Stemming

Stemming is a technique in linguistic morphology where variations of a word are reduced to their base form. For example, in Python, the NLTK library can be used for stemming:

import nltk

from nltk.stem.porter import PorterStemmer

words = ["walk", "walking", "walked", "walks", "ran", "run", "running", "runs"]

stemmer = PorterStemmer()

for word in words:

print(word + " ---> " + stemmer.stem(word))

4. Lemmatization

Lemmatization groups inflected forms of a word, allowing them to be analyzed as a single entity identified by its lemma. Although stemming and lemmatization have similar aims, they employ different methodologies. An example using NLTK is as follows:

import nltk

from nltk.stem import WordNetLemmatizer

words = ["walk", "walking", "walked", "walks", "ran", "run", "running", "runs"]

lemmatizer = WordNetLemmatizer()

for word in words:

print(word + " ---> " + lemmatizer.lemmatize(word))

5. Stopwords

Stopwords are common words in any language that typically do not contribute significant meaning to text. While these words are often removed in processing, caution is advised as their elimination may negatively impact classification results.

import nltk

from nltk.corpus import stopwords

nltk.download("stopwords")

print(set(stopwords.words("english")))

6. N-grams

In computational linguistics, an n-gram refers to a contiguous sequence of 'n' items from a sample of text or speech. These items can be phonemes, syllables, letters, words, or base pairs.

from nltk import ngrams

sentence = "If you find this article useful then make sure you don't forget to clap"

unigrams = ngrams(sentence.split(), 1)

bigrams = ngrams(sentence.split(), 2)

trigrams = ngrams(sentence.split(), 3)

n_grams = [unigrams, bigrams, trigrams]

for idx, ngram in enumerate(n_grams):

print(f"nN-grams: {idx +1}")

for gram in ngram:

print(gram)

7. Regex

"Regex" stands for Regular Expression, a powerful tool for defining text patterns to match, locate, and manipulate text.

8. Normalization

Text normalization involves transforming text into a consistent form, which is crucial for effective processing. Common normalization techniques include stemming, lemmatization, and tokenization.