Lemmatization is a linguistic term that means grouping together words with the same root or lemma but with different inflections or derivatives of meaning so they can be analyzed as one item. The aim is to take away inflectional suffixes and prefixes to bring out the word’s dictionary form.
For example, to lemmatize the words “cats,” “cat’s,” and “cats’” means taking away the suffixes “s,” “’s,” and “s’” to bring out the root word “cat.” Lemmatization is used to train robots to speak and converse, making it important in the field of artificial intelligence (AI) known as “natural language processing (NLP)” or “natural language understanding.”
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In general, lemmatization converts words into their base forms. In linguistics, lemmatization helps a reader consider a word’s intended meaning instead of its literal meaning. Because of that, lemmatization is often confused with stemming.
Differences between Lemmatization and Stemming
In stemming, a computer algorithm often cuts off the ending or beginning of the word being analyzed. The cut thus takes out prefixes and suffixes, which can lead to errors. Let’s take the words “studies” as an example. A stemming algorithm would drop the suffix “es,” thus arriving at the root word “studi,” which we all know is not right. There’s no such word.
Lemmatization, on the other hand, lets a word like “studies” undergo a morphological analysis based on a dictionary that the algorithm can consult to produce the correct root word. As such, a lemmatization-capable machine would know that “studies” is the singular verb form of the word “study” in the present tense.
Practical Applications of Lemmatization
As we said earlier, lemmatization is a crucial component of NLP. It is widely applied in text mining, which involves text analysis of data written in the natural language. This process allows computers to extract relevant information from a given set of text.
One widely known application of lemmatization is information retrieval for search engines. Lemmatization allows systems to map documents to topics, allowing search engines to display relevant results and even expanding them to include other information that readers may find useful, too.
Lemmatization is also used in sentiment analysis, which includes text preparation before examination. The concept is also applied in document clustering, where users need to extract topics and retrieve information.
Lemmatization is also useful in improving search engine optimization (SEO) results. Search engines like Google employ the technology to provide highly relevant results to users. Note that when users type in queries, a search engine automatically lemmatizes words to make sense of the search term and give relevant and comprehensive results.
Some examples of lemmatization tools currently out in the market include:
- BioLemmatizer: Helps computers make sense of biomedical literature.
- Lemmatization API: Automatically obtains the root of any given word.
- Trinker/Textstem: Functions much like Lemmatization API.
Lemmatization, in a nutshell, is the process of obtaining the root of any word to make sense of a phrase, clause, sentence, or any kind of content.