Lemmatization - iffatAGheyas/NLP-handbook GitHub Wiki

Lemmatization Overview

Lemmatization is a fundamental technique in natural language processing (NLP) that reduces words to their base or dictionary form, known as the lemma. Unlike stemming, which simply chops off word endings and may produce non-words, lemmatization uses linguistic knowledge to ensure that the resulting form is an actual word found in the language’s vocabulary.

How Lemmatization Works

  • Context-Aware
    Lemmatization analyses the context and part of speech (POS) of each word to accurately determine its lemma. For example, the word saw could be the past tense of see or a noun referring to a tool; lemmatization uses context to choose the correct lemma.

  • Morphological Analysis
    It examines the structure and form of words, considering inflections (such as tense, number, or case) to group different forms under a single lemma. For instance:

    • running, ran → run
    • better → good
    • rocks → rock

Techniques

  • Dictionary-based approaches
    Mapping words to their lemmas using extensive lexical databases.

  • Rule-based approaches
    Applying linguistic rules to derive the lemma from inflected forms.

Why Lemmatization Matters

  • Improved Text Analysis
    By reducing word variants to a single form, lemmatization enables more accurate text analysis, search, and information retrieval. For example, a search for run will also match running, ran, and runs.

  • Accuracy
    Lemmatization produces valid words, making it preferable for applications where correct language use is essential, such as chatbots, search engines, and text classification.

  • Efficiency
    It reduces the dimensionality of text data, allowing NLP algorithms to process input more efficiently and with better generalisation.

Example

Word Form Lemma Part of Speech
running run verb
better good adjective
rocks rock noun
was be verb

Lemmatization ensures that words are grouped and analysed according to their true meaning, not just their surface form, making it a cornerstone of modern NLP systems.```