What is Lemmatization in NLP?
Lemmatization in NLP is a text normalization technique that switches any kind of a word to its base root mode. Lemmatization is responsible for grouping different inflected forms of words into the root form, having the same meaning.
Tagging systems, indexing, SEOs, information retrieval, and web search all use lemmatization to a vast extent. Lemmatization usually involves using a vocabulary and morphological analysis of words, removing inflectional endings, and returning the dictionary form of a word (the lemma).
The morphological analysis would need the extraction of the correct lemma of every word.
To simplify it, let's just say that lemmatization in NLP is a linguistic term refers to the act of grouping together words that have the same root or lemma but have different inflections or derivatives of meaning so they can be analyzed as one item. The process of lemmatization seeks to get rid of inflectional suffixes and prefixes for the purpose of bringing out the word’s dictionary form.
What is Lemmatization in NLP used for?
Lemmatization is one of the best ways to help chatbots understand your customers’ queries to a better extent. Since this involves a morphological analysis of the words, the chatbot can understand the contextual form of the words in the text and can gain a better understanding of the overall meaning of the sentence that is being lemmatized.
Lemmatization is also used to enable robots to speak and converse. This makes lemmatization a rather important part of natural language understanding (NLU) and natural language processing (NLP) in artificial intelligence.
What is the difference between Stemming and Lemmatization in NLP?
While stemming and lemmatization both focus on attempting to reduce the inflectional form of each word into a common base or root, they are not the same.
They work in different ways, which means that the result that they return differs.
In stemming, the end or beginning of a word is cut off, keeping common prefixes and suffixes that can be found in inflected words in mind. Lemmatization uses dictionaries to conduct a morphological analysis of the word and link it to its lemma. Lemmatization always returns the dictionary meaning of the word while converting into root-form.
Lemmatization in NLP involves greater complexity than stemming. This is because the process needs the words to be classified by a part-of-speech and the inflected form. This can be quite a difficult task in any language other than English.
One stem can be common for inflectional forms of many lemmas and the same lemma can be linked to forms with different stems.
Stemming tends to be a faster process than lemmatization because it chops words without knowing the context of the word in the sentences which they are in. Lemmatization, on the other hand, is a slower process than stemming, it knows the context of the word before proceeding.
While stemming is a rule-based approach, lemmatization is a dictionary-based approach. The process of stemming also has a lower degree of accuracy as compared to lemmatization.
Why is Lemmatization in NLP important?
Lemmatization is extremely important because it is far more accurate than stemming. This brings great value when working with a chatbot where it is crucial to understand the meaning of a user’s messages.
The major disadvantage to lemmatization algorithms, however, is that they are much slower than stemming algorithms.
What are the applications of Lemmatization in NLP?
Here are some of the areas in which lemmatization can be used, other than in chatbots. The process of lemmatization is also used rather extensively in text mining. The text mining process, through lemmatization, enables computers to extract relevant information from a particular set of text.
Here are some of the other ways and areas in which lemmatization is used:
1. Sentiment analysis
Sentiment analysis refers to an analysis of people’s messages, reviews, or comments to understand how they feel about something. Before the text is analyzed, it is lemmatized.
2. Information Retrieval Environments
Lemmatizing is used for the purpose of mapping documents to common topics and displaying search results. To do so, it indexes when documents are increasing to large numbers.
Lemmatization can be used while morphologically analyzing biomedical literature. The Biolemmatizer tool has been been for this very purpose. It pulls lemmas based on the use of a word lexicon. But if the word is not found in the lexicon, it defines rules that turn the word into a lemma. This tool has been 97.5% accurate in its attempts to lemmatize an evaluation set prepared from the CRAFT corpus.
4. Document clustering
Document clustering (or text clustering) is a practice of group analysis conducted on text documents. Topic extraction and rapid information retrieval are vital applications of it.
Both stemming and lemmatization are used to diminish the number of tokens to transfer the same information and thereby boost up the entire method. After the pre-processing is carried out, features are estimated via determining the frequency of each token, and then clustering methods are implemented.
5. Search engines
Search engines like Google make use of lemmatization so that they can provide better, more relevant results to their users. When users enter queries in the search engine, the search engine will automatically lemmatize the words in the queries to make sense of the search term and return relevant and comprehensive results. Lemmatization even allows search engines to map documents, making it possible for search engines to display relevant results and even expand them to include other information that readers may find useful as well.
What is the advantage and disadvantage of Lemmatization in NLP?
The obvious advantage of lemmatization is that it is more accurate. It is useful to get root words from the dictionary, unlike just cutting words like stemming. Lemmatization gives more context to chatbot conversations as it recognizes words based on their exact and contextual meaning.
On the other hand, lemmatization is a time-consuming and slow process. As it extracts the root words and meaning of the words from the dictionary. So most lemmatization algorithms are slower compared to their stemming counterparts.
Which is an example of Lemmatization in NLP?