Lemmatization and Stemming
More technical than our previously discussed techniques, lemmatization and stemming are basically used to reduce the words to their base forms or root forms, converting them into more manageable data for text processing or text analysis.
Lemmatization is an advanced NLP technique that uses a lexicon or vocabulary to convert words into their base or dictionary forms called lemms. Now the lemmatized word is a valid words that represents base meaning of the original word. Lemmatization considers the part of speech (POS) of the words and ensures that the output is a proper words in the language.
Stemming is a faster and simpler NLP technique, that basically truncates words into their stems, the choice between lemmatization and stemming dependson the specific requirements of the NLP project or task, the language being used, and the desired level of accuracy required.
Natural Language Processing (NLP): 7 Key Techniques
Natural Language Processing (NLP) is a subfield in Deep Learning that makes machines or computers learn, interpret, manipulate and comprehend the natural human language. Natural human language comes under the unstructured data category, such as text and voice. Generally, computers can understand the structured form of data, such as tables and spreadsheets in the database, since the human language, like text and voice, comes under the category of unstructured data, and since computers can’t understand the unstructured textual or voice data, NLP comes into action.
In this article, we will explore about 7 Natural Language Processing Techniques that form the backbone of numerous applications across various domains.