Tokenization

Tokenization is the process of breaking down raw text into smaller parts, called tokens, such as words, phrases, or sentences. It is an important step in natural language processing (NLP) because it helps to analyze the semantic and syntactic structure of text. Tokenization is used in various applications such as machine translation, sentiment analysis, and speech recognition.

Word Tokenization

Word tokenization is the most common type of tokenization. In this process, the text is split into tokens, where each token represents a single word. The simplest way to tokenize text is to split it by space or punctuation marks. However, this approach may not be sufficient for certain languages such as Chinese or Japanese, where words do not have delimiters.

Here is an example of word tokenization:

import nltk
from nltk.tokenize import word_tokenize

text = "This is a sample sentence."
tokens = word_tokenize(text)
print(tokens)

Output:

['This', 'is', 'a', 'sample', 'sentence', '.']

Sentence Tokenization

Sentence tokenization is the process of dividing text into separate sentences. This is useful for applications that require working with individual sentences, such as text summarization or machine translation.

In English, sentence boundaries are typically marked by punctuation marks such as periods, exclamation points, or question marks. However, achieving sentence tokenization is not always easy as sentence endings can be ambiguous. For example, abbreviations or acronyms, as well as rhetorical questions, may not end with punctuation marks.

Here is an example of sentence tokenization:

import nltk
from nltk.tokenize import sent_tokenize

text = "This is the first sentence. The second sentence follows."
sentences = sent_tokenize(text)
print(sentences)

Output:

['This is the first sentence.', 'The second sentence follows.']

Other Tokenization Techniques

Besides word and sentence tokenization, there are other tokenization techniques used, depending on the type of text and specific application.

Subword Tokenization

Subword tokenization is the process of breaking words down into smaller, more manageable units that can be used in NLP tasks such as machine translation or speech recognition. It is particularly useful for languages with complex inflectional morphology such as Finnish or Hungarian.

Examples of subword tokenization include byte-pair encoding (BPE) or wordpiece tokenization used in Google's BERT model.

Part-of-Speech (POS) Tagging

POS tagging is the process of labeling the part of speech of each word in a sentence. It is often used as part of the preprocessing stage of NLP tasks such as text classification or sentiment analysis.

import nltk
from nltk.tokenize import word_tokenize
from nltk import pos_tag

text = "This is a sentence."
tokens = word_tokenize(text)
tags = pos_tag(tokens)
print(tags)

Output:

[('This', 'DT'), ('is', 'VBZ'), ('a', 'DT'), ('sentence', 'NN'), ('.', '.')]

In the above example, 'DT' stands for determiner, 'VBZ' is a verb in the present tense, and 'NN' is a noun.

Conclusion

Tokenization is an essential preprocessing step in NLP because it breaks down raw text into smaller units that can be analyzed and processed further. It helps to extract important features and improve the accuracy of NLP models.