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Natural language processing (NLP) is a type of technology within artificial intelligence (AI) that uses various techniques to interpret human language and respond in a meaningful way. Within NLP, lemmatization and stemming are fundamental methods for text analysis, helping deep learning methods recognize words and process them for analysis. In this article, we will explore each method, the differences between them, and the pros and cons associated with each.

What is natural language processing?

NLP is a complex field that combines computer science, artificial intelligence, and linguistics. NLP algorithms enable computers to communicate with humans using human language in a conversational way. By analyzing and processing natural language data, NLP systems can extract insights, automate text functions, and respond to user input and requests.

Computers need to know how to break down and process text when extracting language data. Two of these methods are lemmatization and stemming, each focusing on different aspects of natural language and how to recognize the root meaning of words.

Both methods help reduce the dimensionality of large bodies of text and make it easier for machines to group related words. However, stemming takes a simpler approach that鈥檚 more prone to errors, while lemmatization is more computationally intensive yet may return more accurate results.

What is stemming and what does stemming do?

This simple form of word reduction focuses on removing word endings (suffixes) to obtain a base form, often resulting in non-dictionary words. As a method, though less precise than lemmatization, stemming is quick and efficient when processing large volumes of text.

Stemming algorithms work by having a defined set of suffixes and a required word length. If the word is a reasonable length, the stemming algorithm will scan the word for one of the predefined suffixes (such as 鈥�-ed鈥� or 鈥�-ing鈥�) and remove the suffix to return the root of the word. Having a required word length helps to prevent errors in this method, such as shortening 鈥渇ish鈥� to 鈥渇.鈥� This method is highly efficient and helps computers process written text in an organized way.

If you choose stemming, your algorithm will return stems such as:

• 鈥淲alk,鈥� 鈥渨alking,鈥� and 鈥渨alks鈥� will become 鈥渨alk.鈥�

• 鈥淲rite,鈥� 鈥渨ritten,鈥� and 鈥渨riter鈥� would become 鈥渨rit.鈥�

• 鈥淩equirement鈥� and 鈥渞equire鈥� would become 鈥渞equir.鈥�

Advantages of stemming

• Speed and efficiency: Stemming algorithms are generally faster as they follow simple rule-based approaches.

• Simplicity: The algorithms for stemming use simple heuristic rules, so they are less complex to implement and understand than other methods.

• Improved search performance: In search engines and information retrieval systems, stemming helps connect different word forms, potentially increasing the breadth of search results.

Disadvantages of stemming

• Over-stemming and under-stemming: Stemming can often be imprecise, leading to over-stemming (where words are overly reduced and unrelated words are conflated) and under-stemming (where related words don鈥檛 appear related).

• Language limitations: The effectiveness of a stemming algorithm reduces if words appear in irregular formats (i.e., irregular conjugated forms).

What is lemmatization?

Lemmatization goes beyond truncating words and analyzes the context of the sentence, considering the word's use in the larger text and its inflected form. After determining the word's context, the lemmatization algorithm returns the word's base form (lemma) from a dictionary reference.

This technique effectively handles different grammatical categories and tenses, providing a more accurate language representation. For example:

• 鈥淪aw鈥� would return as 鈥渟ee鈥� or 鈥渟aw鈥� depending on the context of the word (i.e., whether it is a noun or verb in the sentence).

• 鈥淧onies鈥� would return 鈥減ony.鈥�

• 鈥淩equirement鈥� and 鈥渞equired鈥� would return separate words.

Advantages of lemmatization

• Accuracy and contextual understanding: Lemmatization is more accurate as it considers words' context and the morphological analysis. It can distinguish between different word uses based on their part of speech.

• Reduced ambiguity: By converting words to their dictionary form, lemmatization reduces ambiguity and enhances the clarity of text analysis.

• Language and grammar compliance: Lemmatization adheres more closely to the grammar and vocabulary of the target language, leading to linguistically meaningful outputs.

Disadvantages of lemmatization

• Computational complexity: Lemmatization algorithms are more complex and computationally intensive than stemming. They require more processing power and time.

• Dependency on language resources: Lemmatization depends on extensive language-specific resources like dictionaries and morphological analyzers, making it less flexible for use with certain languages, such as Arabic.

What is an example of stemming?

A stem, or the output of stemming, is a shortened version of a word after removing affixes. For example, stemming would turn the words 鈥渋nvestment, invest, investing鈥� to the shortened stem 鈥渋nvest.鈥� Stems are only based on removing letters, which can sometimes result in tokens that aren鈥檛 complete words. For example, the stem of the word 鈥減onies鈥� could return 鈥減oni鈥�.鈥澛�

A lemma, on the other hand, is a shortened version of the word without affixes after normalizing the word to something you could look up in the dictionary. To compare how this is different from a stem in practice, consider the word 鈥渁pplied.鈥� The stem of 鈥渁pplied鈥� would be 鈥渁ppli鈥�,鈥� while the lemma would return as 鈥渁pplied.鈥澛�

When to use stemming vs. lemmatization

When deciding between lemmatization and stemming, consider the type of output you want from your text and the strengths and limitations of each method. Lemmatization is a more resource-intensive process because it requires comprehensive linguistic knowledge. Stemming is a simpler and faster method. While lemmatization provides high accuracy and context relevance, stemming offers greater speed, so it is up to you to determine your priority.

You can see the differences between stemming and lemmatization in the output. For example, let鈥檚 say your text has the words 鈥渟ung,鈥� 鈥渟ang,鈥� 鈥渟ings,鈥� 鈥渟inger鈥� and 鈥渟inging.鈥� Stemming would recognize only three of the five words as a conjugation of 鈥渟ing鈥� and return 鈥渟ing鈥� for 鈥渟ings,鈥� 鈥渟inger,鈥� and 鈥渟inging.鈥� Because 鈥渟ung鈥� and 鈥渟ang鈥� do not have a recognized suffix, they wouldn鈥檛 appear in the same category as the others.

Lemmatization, conversely, would recognize the irregular conjugation of the words and output the root word 鈥渟ing鈥� for all five words.

Another example would be words that share the same stem but have different meanings. For example, 鈥渞equirement鈥� and 鈥渞equires鈥� mean different things, but stemming algorithms would return 鈥渞equir鈥� for both. Lemmatization algorithms would recognize them in context and return each word separately.聽

Consider the precision of your desired output or whether you are just looking for the root of the word to choose the most suitable method.

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