NLTK Python

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                    NLTK Python 

NLTK Python:

 

NLTK, or Natural Language Toolkit, is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, semantic reasoning, and wrappers for industrial-strength NLP libraries.

Here are a few examples of how to use NLTK:

  1. Tokenization – Breaking down text into words, sentences, etc.
python
import nltk nltk.download('punkt') # download the tokenizer model text = "This is a sentence. This is another sentence." sentences = nltk.sent_tokenize(text) # Sentence tokenization print(sentences) words = nltk.word_tokenize(text) # Word tokenization print(words)
  1. Part of Speech (POS) Tagging – Assigning word types to tokens, like verb or noun.
python
nltk.download('averaged_perceptron_tagger') # download the POS tagger model sentence = nltk.word_tokenize("This is a sentence") tagged = nltk.pos_tag(sentence) print(tagged)
  1. Stemming – Reducing words to their root (stem).
python
from nltk.stem import PorterStemmer stemmer = PorterStemmer() print(stemmer.stem('running'))
  1. Lemmatization – Reducing words to their base form (lemma), considering the context.
python
nltk.download('wordnet') # download Wordnet, a lexical database for English from nltk.stem import WordNetLemmatizer lemmatizer = WordNetLemmatizer() print(lemmatizer.lemmatize('running', pos='v')) # Lemmatize as verb
  1. Named Entity Recognition (NER) – Classifying named entities in text.
python
nltk.download('maxent_ne_chunker') # download the NER model nltk.download('words') # download the words corpus sentence = nltk.word_tokenize("Apple Inc. is planning to open a new office in San Francisco") sentence = nltk.pos_tag(sentence) named_entities = nltk.ne_chunk(sentence) print(named_entities)
  1. Stop words – Filtering common words that typically don’t contain useful information for NLP tasks.
python
nltk.download('stopwords') # download the stopwords corpus from nltk.corpus import stopwords stop_words = set(stopwords.words('english')) words = nltk.word_tokenize("This is a sentence") filtered_words = [w for w in words if not w.lower() in stop_words] print(filtered_words)

These are just a few examples. NLTK provides a wide range of functionalities for natural language processing and understanding.

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