Word Replacement and Correction with NLTK in Python

When we talk about natural language processing (NLP), one of the most important tasks is replacing and correcting words. This involves techniques such as stemming, lemmatization, spelling correction, and word replacement based on synonyms and antonyms. Using these techniques can greatly improve the quality of text analysis, whether for search engines, chatbots or sentiment analysis. Let's explore how the NLTK library in Python helps with these tasks.

Stemming: Cutting Suffixes

Stemming is a technique that removes suffixes from words, leaving only the root. For example, the word "running" has the root "corr". This is useful for reducing the amount of words a search engine needs to index.

In NLTK, we can use PorterStemmer to do stemming. Let's see how it works:

from nltk.stem import PorterStemmer

stemmer = PorterStemmer()
print(stemmer.stem("correndo"))  # Saída: corr
print(stemmer.stem("correção"))  # Saída: correc

Here, we saw that stemming cuts the suffixes and leaves only the root of the words. This helps you stay focused on the main meaning of the words, without worrying about their variations.

Lemmatization: Returning to Base Form

Lemmatization is similar to stemming, but instead of cutting suffixes, it converts the word to its base form, or lemma. For example, "running" becomes "run". This is a little smarter than stemming, because it takes into account the context of the word.

To do lemmatization in NLTK, we use WordNetLemmatizer:

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()
print(lemmatizer.lemmatize("correndo", pos='v'))  # Saída: correr
print(lemmatizer.lemmatize("correções"))  # Saída: correção

In this example, we use the lemmatize function and, for verbs, we specify the part of speech (pos) as 'v'. This helps NLTK better understand the context of the word.

Regular Expressions for Replacement

Sometimes, we want to replace specific words or patterns in the text. For this, regular expressions (regex) are very useful. For example, we can use regex to expand contractions, like "no" to "no".

Here is how we can do this with NLTK:

import re

texto = "Eu não posso ir à festa. Você não vai?"
expansoes = [("não", "não")]

def expandir_contracoes(texto, expansoes):
    for (contraido, expandido) in expansoes:
        texto = re.sub(r'\b' + contraido + r'\b', expandido, texto)
    return texto

print(expandir_contracoes(texto, expansoes))  # Saída: Eu não posso ir à festa. Você não vai?

In this example, the expand_contracoes function uses regex to find and replace contracted words in the text.

Spell Check with Enchant

Another important task is spelling correction. Sometimes texts have typing or spelling errors, and correcting these is essential for text analysis. The pyenchant library is great for this.

First, we need to install the pyenchant library:

pip install pyenchant

Afterwards, we can use Enchant to correct words:

import enchant

d = enchant.Dict("pt_BR")
palavra = "corrigindo"
if d.check(palavra):
    print(f"{palavra} está correta")
else:
    print(f"{palavra} está incorreta, sugestões: {d.suggest(palavra)}")

If the word is incorrect, Enchant suggests corrections.

Synonym Replacement

Replacing words with their synonyms can enrich a text, avoiding repetitions and improving the style. With WordNet, we can find synonyms easily.

Here's how we can do it:

from nltk.corpus import wordnet

def substituir_sinonimos(palavra):
    sinonimos = []
    for syn in wordnet.synsets(palavra, lang='por'):
        for lemma in syn.lemmas():
            sinonimos.append(lemma.name())
    return set(sinonimos)

print(substituir_sinonimos("bom"))  # Saída: {'bom', 'legal', 'ótimo', 'excelente'}

In this example, the replace_synonyms function returns a list of synonyms for the given word.

Replacing Antonyms

Like synonyms, antonyms are also useful, especially for tasks such as sentiment analysis. We can use WordNet to find antonyms:

def substituir_antonimos(palavra):
    antonimos = []
    for syn in wordnet.synsets(palavra, lang='por'):
        for lemma in syn.lemmas():
            if lemma.antonyms():
                antonimos.append(lemma.antonyms()[0].name())
    return set(antonimos)

print(substituir_antonimos("bom"))  # Saída: {'mau', 'ruim'}

This function finds antonyms for the given word.

Practical Applications

Let's see some practical applications of these techniques.

Sentiment Analysis

Sentiment analysis involves determining the polarity (positive, negative or neutral) of a text. Word replacement can improve this analysis.

texto = "Eu adorei o filme, mas a comida estava ruim."
palavras = word_tokenize(texto, language='portuguese')
polaridade = 0

for palavra in palavras:
    sinsets = wordnet.synsets(palavra, lang='por')
    if sinsets:
        for syn in sinsets:
            polaridade += syn.pos_score() - syn.neg_score()

print("Polaridade do texto:", polaridade)  # Saída: Polaridade do texto: 0.25 (por exemplo)

Text Normalization

Text normalization involves transforming text into a consistent form. This may include correcting spelling, removing stopwords, and replacing synonyms.

stopwords = set(stopwords.words('portuguese'))
texto = "A análise de textos é uma área fascinante do PLN."
palavras = word_tokenize(texto, language='portuguese')
palavras_filtradas = [w for w in palavras se não w in stopwords]

texto_normalizado = " ".join(palavras_filtradas)
print(texto_normalizado)  # Saída: "análise textos área fascinante PLN"

Improved Text Search

In search engines, replacing synonyms can improve search results by finding documents that use synonyms for the searched keywords.

consulta = "bom filme"
consulta_expandidas = []

for palavra em consulta.split():
    sinonimos = substituir_sinonimos(palavra)
    consulta_expandidas.extend(sinonimos)

print("Consulta expandida:", " ".join(consulta_expandidas))  # Saída: "bom legal ótimo excelente filme"

Conclusion

In this text, we explore various word replacement and correction techniques using the NLTK library in Python. We saw how to do stemming, lemmatization, use regular expressions to replace words, spell correction with Enchant, and replacing synonyms and antonyms with WordNet. We also discuss practical applications of these techniques in sentiment analysis, text normalization and search engines.

Using these techniques can significantly improve the quality of text analysis, making results more accurate and relevant. NLTK offers a powerful range of tools for those working with natural language processing, and understanding how to use these tools is essential for any NLP project.

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