Lemmatization Vs Tokenization: Differences Explained In NLP

In this article, we will explore the lemmatization advantages and disadvantages, lemmatization vs tokenization, and highlight when and why you should use each method in your NLP workflow.

Lemmatization advantages and disadvantages

Lemmatization advantages

Linking Variants in Morphology to a Canonical Form (Lemma): Determining that several word forms share the same root or canonical form, known as the lemma, despite their outward distinctions is the essence of lemmatization. Using “appeared” or “appears,” it links different word forms to “appear.” We also map “sang,” “sung,” and “sings” to “sing,” “am,” “are,” and “is” to “be,” and “mice” to “mouse.” The lemma is frequently regarded as the word’s dictionary entry form. In this procedure, morphological variations are linked to a lemma from a lemma dictionary.
Finding Invariant Information: In a lemma dictionary, the lemma is accompanied by invariant syntactic and semantic information. Applications such as Machine Translation (MT) benefit from having access to the lexical semantics of word strings through the lemma dictionary. Lemmatization aids in producing the morphosyntactic representation of strings for syntactic analysis for transfer models in MT; therefore, lemmas must be provided with both semantic and morphosyntactic information.
Improving Information Retrieval (IR):
- Lemmatization is useful for compiling lists of important concepts since it enables morphological variants to be collapsed under a single lemma.
- Since a lemma in IR has invariant semantics, a search’s semantic requirements are satisfied by discovering any morphological variation of the lemma. By treating words with the same root (lemma) as referring to the same notion or concept for indexing purposes, this aids in classifying various inflected variants of a word.
- Texts can be searched using a lemma list to lemmatization.
- By collapsing related forms, lemmatized tagged texts can help minimize “noise” in linguistic enquiries. The distinction between the French verb “faire” and the noun “fait” is given as an example.
Producing Complete, Valid Words: One important advantage of lemmatisation is that, in contrast to stemming, which may only produce a fragment, it produces a complete English word that makes sense on its own. For instance, a stemmer may yield “leav,” yet the lemmatised version of “leaves” is “leaf,” which is a legitimate word. Likewise, ‘blending’ becomes ‘blend’. Lemmatisation must produce a legitimate word.
Linking Words to Their Essential Meaning: Lemmatizing, like stemming, distils words to their essential meaning. It facilitates the relationship between words and their lemma, which stands for a comprehensive lexicon.
Supporting Morphosyntactic Analysis: In the majority of NLP applications, morphological analysis, also known as lemmatization, is a prelude to parsing techniques. In order to link the form with the required syntactic and semantic information, it aids in breaking it down into a stem and affixes. A string can be given POS tags for syntactic analysis by being parsed into morphosyntactic categories and subcategories.
Managing Ambiguity: When a term is provided in context, lemmatization is more accurate. To differentiate between homonymous forms, such as the verbs “lie-lay” and “lie-lied” from the word “lying,” the term “lemmatization” suggests disambiguation at the lexeme level.
Advantageous Word Embeddings: Pre-processing by lemmatizing words can be advantageous for tasks utilising word embeddings in situations with a lack of training instances. When employing word embeddings, using lemmas rather than inflected forms can assist reduce morphological inflection problems.
Use in Lexical Resources: According to lexical resources such as WordNet, a lemma is the combination of a word and a synset. For the purpose of gathering vocabularies of valid lemmas, lemmatizes such as WordNet lemmatize are helpful.
Essential for Morphologically Rich Languages Since many other languages have far more complex systems of inflection and derivation than English, which has comparatively little morphology, it is more urgent than ever to handle morphology intelligently, including through lemmatization. Processing morphologically complicated languages like Arabic is said to require lemmatization.

Lemmatization disadvantages

Not as Fast as Stemming To make sure the final term is in its dictionary, lemmatization specifically, the WordNet lemmatize described in one source involves an extra testing step. Compared to more straightforward stemming techniques, this causes the lemmatize to proceed more slowly.

Not All Irregular Forms May Be Handled Certain implementations may not handle all irregular forms correctly, even while lemmatisation attempts to handle a variety of morphological variants. As an example, the term “lying” is not handled by the WordNet lemmatizer under discussion.

Semantic Relatedness Is Not Always the Same as Morphological Relatedness Lemmatisation relies on morphological relatedness, which maps variants to a common lemma, but this does not necessarily translate into semantic relatedness. This is a major difficulty that is brought to light. For example, “awe” and “awful” are not the same thing. In applications like information retrieval, where the objective is to group semantically comparable terms, this can result in disappointments and possibly reduced precision rates. The usefulness of morphological analysis (including lemmatisation) for consistently classifying semantically related concepts based only on morphology is nevertheless limited by the fact that inflectional forms are said to be more transparent than derivational forms.

Specificity of Language Lemmatization and stemming are both language-specific procedures. Generally speaking, a lemmatize made for one language like English is not very useful for processing text in another language. This implies that for multilingual NLP applications, distinct lemmatization resources and techniques for each language must be developed or acquired.

Implementation Complexity for Difficult Morphology It can be difficult to accurately execute morphological analysis, also known as lemmatisation, particularly for languages with rich morphology or phenomena that defy straightforward affix-stripping assumptions (such as non-isomorphism or non-contiguity). Although there are other methods, such as paradigm-based analysis or treating particular cases with zero affixes or stem classes, it can be difficult to achieve accurate lemmatisation for all forms in complicated languages, such as Arabic or Finnish, and may necessitate extensive analysis.

There is still room for ambiguity in the output lemma (polysemy). Lemmatization is the process of reducing various word forms to its lemma, which is usually the form used in dictionary entries. However, polysemy is the ability of a single lemma to have more than one interpretation or sense. The lemma “mouse,” which can refer to either a rodent or a cursor control device, is used as an example. Therefore, additional word sense disambiguation is frequently necessary; lemmatization alone does not address the ambiguity of a word’s meaning in context. The generated lemma embeddings will conflate all of a word’s meanings when employed as a pre-processing step for word embedding models that combine all of the word’s senses into a single representation. This is a drawback if the downstream task calls for differentiating between word senses.