The real difference between stemming and lemmatization is that Stemming reduces word-forms to (pseudo)stems which might be meaningful or meaningless, whereas lemmatization. The NLTK Lemmatization method is based on WorldNet’s built-in morph function. Lemmatization is the process of converting a word to its base form, e. However, what makes it different is that it finds the dictionary word instead of truncating the original word. The most common stemmer is the Porter Stemmer (a Porter stemmer implementation is also provided by Lucene library), which works. Examples of how Lemmatization is applied:The preprocessing process includes (1) unitization and tokenization, (2) standardization and cleansing or text data cleansing, (3) stop word removal, and (4) stemming or lemmatization. OR Stemming is the process in which the affixes of words are removed and the words are converted to their base form. Stemming refers to the practice of cutting off or slicing any pattern of string-terminal characters that is a suffix, thereby. Stemming vs. In search queries, lemmatization allows end users to query any version of a base word and get relevant results. All of the above. Tokenization using Python’s split () function. Lemmatization usually refers to the morphological analysis of words, which aims to remove inflectional endings. Lemmatization seeks to address this issue. Stochastic models. However, lemmatization is more context-sensitive and linguistically informed, lemmatization uses a dictionary or a corpus to find the lemma or the canonical form of each word. Lemmatization: The process of obtaining the Root Stem of a word. First, you want to install NLTK using pip (or conda). “Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word…” 💡 Inflected form of a word has a changed spelling or ending. Stemming is a simple rule-based approach, while. Lemmatization. We can morphologically analyse the speech and target the words with inflected endings so that we can remove them. This way, the stemmer can grasp more information about the word being stemmed, and use that to group similar words. spaCy provides two pipeline components for lemmatization: The Lemmatizer component provides lookup and rule-based lemmatization methods in a configurable component. their lemma. In lemmatization, a root word is called. " In WordNet, a satellite adjective--more broadly referred to as a satellite synset--is more of a semantic label used elsewhere in WordNet than a special part-of-speech in nltk. ; The lemma of ‘was’ is ‘be’, the lemma of “rats”. This confusion occurs because both techniques are usually employed to reduce words. Before we dive deeper into different spaCy functions, let's briefly see how to work with it. Lemmatization makes use of the vocabulary, parts of speech tags, and grammar to remove the inflectional part of the word and reduce it to lemma. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that focuses on the interaction between computers and humans using natural language. For our purpose, we will use the following library-a. g. Lemmatization. " Following is the same sentence after lemmatization:Lemmatization. Stemming and lemmatization both involve the process of removing additions or variations to a root word that the machine can recognize. For example, talking and talking can be mapped to a single term, walk. Stemming uses the stem of the word, while lemmatization uses the context in which the word is being used. For lemmatization algorithms to perform accurately, they need to. Introduction to NLTK: Tokenization, Stemming, Lemmatization, POS Tagging. Lemmatization is the process where we take individual tokens from a sentence and we try to reduce them to their base form. Lemmatization: Lemmatization in NLP is a type of normalization used to group similar terms to their base form based on the parts of speech. Well, there are differences between lemma and lexeme in NLP. Lemmatization. In modern natural language processing (NLP), this task is often indirectly. Let’s look at some examples to make more sense of this. Stemming. For example, “systems” becomes “system” and “changes” becomes “change”. Lemmatization: Lemmatization aims to achieve a similar base “stem” for a word, but it derives the proper dictionary root word, not just a truncated version of the word. Also, we’ve already discussed lemmatization. lemmatize()’ method to build a new list called LEM tokens. Here we will download WordNetLemmatizer package to perform Lemmatization preprocessing. Lemmatization. So it links words with similar meanings to one word. Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. The following command downloads the language model: $ python -m spacy download en. In this case, the transformation actually uses a dictionary to map different variants of a word to its root. The root word is referred to as a stem in the stemming process and a lemma in the lemmatization process. From the NLTK docs: Lemmatization and stemming are special cases of normalization. Stemming is faster because it chops words without knowing the context of the word in given sentences. To return the word to its original form, these algorithms make use of linguistic rules and patterns. So it will not work correctly for verbs. NLTK is a short form for natural language toolkit which aids the research work in NLP, cognitive science, Artificial Intelligence, Machine learning, and more. This confusion occurs because both techniques are usually employed to reduce words. It is a rule-based approach. So the output we get after Lemmatization is called ‘lemma. It’s usually more sophisticated than stemming, since stemmers works on an individual word without knowledge of the context. For example, lemmatization can convert irregular plurals, like “feet” to “foot”, or the French “œil” to “yeux”. It is the driving force behind things like virtual assistants , speech. It involves longer processes to calculate than Stemming. 0. An individual language can extend the. Stems need not be dictionary words but lemmas always are. Lemmatization. Published on Mar. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma. Creating a blank language object gives a tokenizer and an empty. Lemmatization: It is a process of grouping together the inflected forms of a word so they can be analyzed as a single item, identified by the word’s lemma, or dictionary form. Stemming: Stemming is also a type of normalization similar to lemmatization. Description. For Example, there are some tags that always define the low frequency / less important words of a language. Before we dive deeper into different spaCy functions, let's briefly see how to work with it. After a morphological analysis of the word, the lemmatization process returns the word's root or the dictionary word. Lemmatization is a process in NLP that involves reducing words to their base or dictionary form, which is known as the lemma. Lemmatization. With. Lemmatization Actually, Lemmatization is a systematic way to reduce the words into their lemma by matching them with a language dictionary. The word “Lemmatization” is itself made of the base word “Lemma”. Stemming vs Lemmatization, Image from Author. Lemmatization is similar to stemming. To show how you can achieve lemmatization and how it works, we are going to use spaCy. NLTK Lemmatization is the process of grouping the inflected forms of a word in order to analyze them as a single word in linguistics. It’s a crucial step for building an amazing NLP application. This linguistic process of grouping the inflected forms of an expression may only remove a small amount of the carried information but disturb the model of handling natural language. the process of reducing the different forms of a word to one single form, for example, reducing…. We would first find out the POS tag for each token using NLTK, use that to find the corresponding tag in WordNet and then use the lemmatizer to lemmatize the token based on the tag. Something that has happened in the past might have a different sentiment than the same thing happening in the present. What is Lemmatization? Lemmatization is a linguistic process that involves reducing words to their base or dictionary form, which is known as a lemma. Stems need not be dictionary words but lemmas always are. The purpose of lemmatization is the same as that of stemming. What is ML lemmatization? Lemmatization is the grouping together of different forms of the same word. It is a technique used to extract the base form of the. Illustration of word stemming that is similar to tree pruning. It helps in returning the base or dictionary form of a word, which is known as the lemma. Lemmatization considers the context and converts the word to its meaningful base form, whereas stemming just removes the last few characters, often leading to incorrect meanings and spelling errors. Unlike stemming, which only removes suffixes from words to derive a base form, lemmatization considers the word's context and applies morphological analysis to produce the most appropriate base form. “Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word…” 💡 Inflected form of a word has a changed spelling or ending. To enable machine learning (ML) techniques in NLP,. e. a lemmatizer, which needs a complete vocabulary and morphological analysis. In English, we usually identify nine parts of speech, such as noun, verb, article, adjective,. Time-consuming: Compared to stemming, lemmatization is a slow and time-consuming process. It returns the base or dictionary form of a word, also known as the lemma. Identify the POS family the token’s POS tag belongs to — NN, VB, JJ, RB and pass the correct argument for lemmatization. Lemmatization. We’ll later go into more detailed explanations and examples. These various text preprocessing steps are widely used for dimensionality reduction. It often results in words that have no meaning to the users. reduces to a root synonym. In linguistics, it is the process of grouping together the different inflected forms of a word so they can be analyzed as a single item. In lemmatization, on the other hand, the algorithms have this knowledge. It is based on Artificial intelligence. Lemmatization, on the other hand, takes into consideration the morphological analysis of the words. Stemming. Lemmatization considers the context and converts the word to its meaningful base form. Lemmatization. Lemmatizers are similar to Stemmer methods but it brings context to the words. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma . Here loving is as in the sentence "I'm loving it". For example, the lemma of the words “analyzed” and “analyzing” is “analyze. Technique A – Lemmatization. Lemmatization has applications in: What is Lemmatization? This approach of text normalization overcomes the drawback of stemming and hence is perfect for the task. Lemmatization is the process of reducing a word to its word root, which has correct spellings and is more meaningful. Lemmatization is almost like stemming, in that it cuts down affixes of words until a new word is formed. It is different from Stemming. This algorithm collects all inflected forms of a word in order to break them down to their root dictionary form or lemma. We will also see. Contents hide. nltk. setInputCols (Array ("token")) . It's used in computational linguistics, natural language processing and chatbots. Get the stems of the lemmatized tokens. Lemmatization returns the lemma, which is the root word of all its inflection forms. Lemmatization has applications in:Lemmatization is a text normalization technique in natural language processing. Tokenization in NLP: Types, Challenges, Examples, Tools. Lemmatization is a more advanced form of stemming and involves converting all words to their corresponding root form, called “lemma. 2. Lemmatization uses vocabulary and morphological analysis to remove affixes of words. It identifies how a word is produced through the use of morphemes. Lemmatization: Lemmatization is the process of converting a word to its base form. We write some code to import the WordNet Lemmatizer. Semantics: This is a comparatively difficult process where machines try to understand the meaning of each section of any content, both separately and in context. Tokens can be individual words, phrases or even whole sentences. When working on the computer, it can understand that these words are used for the same concepts when there are multiple words in the sentences having the same base words. The words “playing”, “played”, and “plays” all have the same lemma of the word. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words. * Lemmatization is another technique used to reduce words to a normalized form. So it links words with similar meanings to one word. Unlike stemming, lemmatization reduces words to their base word, reducing the inflected words properly and ensuring that the root word belongs to the language. To understand the feature engineering task in NLP, we will be implementing it on a Twitter dataset. Stemming: Strip suffixes. As a result, lemmatization aids in developing more effective machine learning features. Humans communicate through “text” in a different language. It doesn’t just chop things off, it actually transforms words to the actual root. And a lemma is an actual. remove extra whitespaces from words, e. Lemmatization c. These tokens are useful in many NLP tasks such as Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and text classification. Disadvantages of Lemmatization . Instead of sentiment analysis, we're more interested in what technical remarks are most common. Stemmer — It is an algorithm to do stemming 1. I found out you can disable the parser portion of the spacy pipeline as well, as long as you add the sentence segmenter. Lemmatization is the process of joining the different inflected terms to be considered as one thing. It is intended to be implemented by using computer algorithms so that it can be run on a corpus of documents quickly and reliably. Stemming and lemmatization via Python is a bit more obtuse than the three previous techniques. On the contrary, stemming can reduce words to a stem that. There are also multi word expressions (MWEs) that count as multiple lemmas. Prior to feeding the text or data to a predictive model for analysis purposes, the words within the sentences are reduced down to their core root word. For example, “went” is turned into “go” and “joyful” is. The two popular techniques of obtaining the root/stem words are Stemming and Lemmatization. 1. Before we dive deeper into different spaCy functions, let's briefly see how to work with it. Returns the input word unchanged if it cannot be found in WordNet. After lemmatization, stop-word filtering was further conducted to yield a list of lemmatized tokens in each document. (b) What is the major di erence between phrase queries and boolean queries? We discussedFor reference, lemmatization per dictinory. NLTK has different lemmatization algorithms and functions for using different lemma determinations. Lemmatization is typically more Accurate. It can convert any word’s inflections to the base root form. lemma definition: 1. The aim of text normalization is to reduce the amount of information that a machine has to handle thus improving the efficiency of the machine learning process. Target audience is the natural language processing (NLP) and information retrieval (IR) community. Part of speech tagger and vocabulary words helps to return the dictionary form of a word. By default it is 'n' (standing for noun). For example, the words sang, sung, and sings are forms of the verb sing. In simple words, “ NLP is the way computers understand and respond to human language. The following command downloads the language model: $ python -m spacy download en. What is lemmatization itself? Lemmatization is the process of obtaining the lemmas of words from a corpus. Lemmatization is similar to Stemming but it brings context to the words. For example, the word 'cook' is the lemma of the word 'cooking'. e. Lemmatization. Therefore, Vectorization or word embedding is the process of converting text data to numerical vectors. That depends on what you want to do. Lemmatization returns the lemma, which is the root word of all its inflection forms. lemmatize: [transitive verb] to sort (words in a corpus) in order to group with a lemma all its variant and inflected forms. So it's better not to convert running into run because, in some NLP problems, you need that information. The fourth. 2. For example, if we. Lemmatization makes use of the vocabulary, parts of speech tags, and grammar to remove the inflectional part of the word and reduce it to lemma. While not always true, a sentence containing the word, planting, is often talking about something similar to another sentence containing the word, plant. It makes use of vocabulary, word structure, part of speech tags, and grammar relations. Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Lemmatization is a technique to reduce words to their base form, or lemma. '] Hmmm…the lemmatized version is identical to the original phrase. Lemmatization is similar to stemming but is different in a complex way. If this does not work, try taking a look at this page from the documentation. A lemma is the dictionary form or citation form of a set of words. A lemma is the dictionary form or citation form of a set of words. Stemming and Lemmatization are algorithms that are used in Natural Language Processing (NLP) to normalize text and prepare words and documents for further processing in Machine Learning. This is, for the most part, how stemming differs from lemmatization, which is reducing a word to its dictionary root, which is more complex and needs a very high degree of knowledge of a language. What I am a little fuzzy about is stemming and lemmatizing. Assigned Attributes . This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification,. Lemmatization on the other hand does morphological analysis, uses dictionaries and often requires part of speech information. It is an integral tool of NLP and is used to categorize inflected words found in a speech. It is an integral tool of NLP and is used to categorize inflected words found in a speech. Lemmatization. Text preprocessing is an essential step in natural language processing (NLP) that involves cleaning and transforming unstructured text data to prepare it for analysis. . You don't need to make preprocessing as I understand, and the reason for this is that the Transformer makes an internal "dynamic" embedding of words that are not the same for every word; instead, the coordinates change depending on the sentence being tokenized due to the positional encoding it makes. A search involving any of these words should treat them as the same word which is the root worLemmatize definition: . For example, “visits”, “visiting”, and “visited” are all forms of “visit” (lemma). According to Wikipedia, inflection is the process through which a word is modified to communicate many grammatical categories, including tense, case. The root word is called a ‘lemma’. The command for this is pretty straightforward for both Mac and Windows: pip install nltk . Tokenisation is the process of breaking up a given text into units called tokens. The Lemmatization Method − In situations where an immediate query is unimaginable or the token is absent in the lexical asset, lemmatization calculations become possibly the most important factor. 이. Stemming vs. Lemmatization is a systematic process of removing the inflectional form of a token and transform it into a lemma. g. By utilizing a knowledge base of word synonyms and endings, a. Stemming is (usually) a short procedure which uses string matching to remove parts of a string. They don't make sense to do together; it's one or the other. For this post, we’ll stick to stemming and see a few examples. Lemmatization and Stemming are the foundation of derived (inflected) words and hence the only difference between lemma and stem is that lemma is an actual word whereas, the stem may not be an actual language word. For example: ‘Caring’ -> Lemmatization -> ‘Care’ Python NLTK provides WordNet Lemmatizer that uses the WordNet Database to lookup lemmas of words. At last, this research provides the comparison of lemmatization and stemming, attempting to find which one is the best. corpus import wordnet #example text text = 'What can I say about this place. In linguistics, it is the process of grouping together the different inflected forms of a word so they can be analyzed as a single item. In lemmatization, a root word is called lemma. The only difference is that lemmatization tries to do it the proper way. The task is to classify the tweet as Fake or Real. As a first step, you need to import the library as follows: Next, we need to load the spaCy language model. The only difference is that, lemmatization tries to do it the proper way. To overcome this problem Lemmatization comes into picture. Lemmatization is the process of replacing a word with its root or head word called lemma. This process helps simplify textual analysis by grouping together variants of. ” B is. Python Stemming and Lemmatization - In the areas of Natural Language Processing we come across situation where two or more words have a common root. Example text normalizationTokenization and lemmatization are essential for text preprocessing, where raw text is prepared for further analysis. Lemmatization is another, more extensive normalization technique down to the semantic root of a word — its lemma. However, Stemming does not always result in words that are part of the language vocabulary. For example, “building has floors” reduces to “build have floor” upon lemmatization. Latent Dirichlet Allocation (LDA) LDA stands for Latent Dirichlet Allocation. , the lemma for ‘going’ and ‘went’ will be ‘go’. Lemmatization aims to achieve a similar base “stem” for a specified word. Python NLTK. Lemmatization is a Natural Language Processing technique that proposes to reduce a word to its Lemma, or Canonical Form. Stemming in Python uses the stem of the search query or the word, whereas lemmatization uses the context of the search query that is being used. A dictionary word. In order to overcome this drawback, we shall use the concept of Lemmatization. Lemmatization is preferred over the former. For example, the lemma of the word “was” is “be,” the lemma of the word “rats” is “rat,” and the lemma. nltk. As a result, lemmatization aids in the formation of superior machine. Lemmatization is more accurate. Lemmatization tries to achieve a similar base “stem” for a word. 또한 이 둘의 결과가 어떻게 다른지 이해합니다. Python NLTK is an acronym for Natural Language Toolkit. It just chops off the part of word by assuming that the result is the expected word. Lemmatization usually refers to doing things properly using vocabulary and morphological analysis of words. Lemmatization is one of the text normalization techniques that reduce words to their base forms. This technique is similar to stemming, but it is more accurate as it considers the context of the word. Lemmatization considers the context and converts the word to its meaningful base form, whereas stemming just removes the last few characters, often leading to incorrect meanings and spelling errors. Lemmatization is closely related to stemming. Preprocessing input text simply means putting the data into a predictable and analyzable form. The ultimate goal of NLP is to help computers understand language as well as we do. What Does Lemmatization Mean? The process of lemmatization in natural language processing involves working with words according to their root lexical. Information Retrieval: (a) Describe the main problems of using boolean search for information retrieval. Lemmatization is a text normalization technique in natural language processing. What is Lemmatization? Lemmatization is the process of reducing a word to its base form, or lemma. Stemming and Lemmatization are techniques used in text processing. Stemming. This algorithm learns from tables of inflected word forms. This is because lemmatization involves performing morphological analysis and deriving the meaning of words from a dictionary. The lemmatizer takes into consideration the context surrounding a word to determine. If the lemmatization mode is set to "rule", which requires coarse-grained POS (Token. Lemmatization on the surface is very similar to stemming, where the goal is to remove inflections and map a word to its root form. Lemmatization is a text normalization technique of reducing inflected words while ensuring that the root word belongs to the language. Annotator class name. Lemmatisation is linguistically motivated, and generally more reliable to give a correct result when reducing an inflected word to its base form. Lemmatization, on the other hand, is slower because it knows the context before proceeding. This research paper aims to provide a general perspective on Natural Language processing, lemmatization, and Stemming. The NLTK Lemmatization method is based on WordNet’s built-in morph function. Later those vectors are used to build various machine learning models. The text/document is represented as a vector in the multi-dimensional. After lemmatization, stop-word filtering was further conducted to yield a list of lemmatized tokens in each document. are removed. This reduced form, or root word, is called a lemma. Unlike machine learning, we work on textual rather than. Lemmatization is the process of converting a word to its base form. It is an important technique in natural language processing (NLP) for text preprocessing, reducing the complexity of the text and improving the accuracy of NLP models. lemmatization — will be a dictionary word. 4. Inflected words example — read , reads , reading , reader. We’ll talk about lemmatization in another post, maybe. Accuracy is less. Learn how to perform lemmatization. Lemmatization. Stemming – Stemming means mapping a group of words to the same stem by removing prefixes or suffixes without giving any value to the “grammatical meaning” of the stem formed after the process. For example, “reading” and “reader”, are based on the root word “read”. What does lemmatisation mean? Information and translations of lemmatisation in the most. Tal Perry. Valid options are `"n"` for nouns, `"v"` for verbs, `"a"` for adjectives, `"r"`. If the lemmatization mode is set to "rule", which requires coarse-grained POS (Token. stemming or lemmatization : Bert uses BPE ( Byte- Pair Encoding to shrink its vocab size), so words like run and running will ultimately be decoded to run + ##ing. Definition of lemmatisation in the Definitions. We have the WordNet corpus and the lemma generated will be available in this corpus. It is a dictionary-based approach. Stemming is important in natural language understanding ( NLU) and natural language processing ( NLP ). The root word is called a ‘lemma’. By understanding suffixes, and the rules by which they. This process uses a data structure that relates all forms of a word back to its simplest form, or lemma. The approach of the greedy. In Natural Language Processing (NLP), text processing is needed to normalize the text. sp = spacy. Keywords: Natural Language processing, lemmatization, and Stemming. Lemmatization. Lemmatization technique is like stemming. As this is done without any. In linguistics, lemmatization is the process of removing those inflections from a word in order to identify the lemma (dictionary form/word). Lemmatization is another way to normalize words to a root, based on language structure and how words are used in their context. For example cars, car’s will be lemmatized into car. This process involves. This is done by considering the word’s context and morphological analysis. Commonly used syntax techniques are lemmatization, morphological segmentation, word segmentation, part-of-speech tagging, parsing, sentence breaking, and stemming. Lemmatization is closely related to stemming, but there are differences: Lemmatization reduces inflected words to their lemma, which is an existing word. We will be using COVID-19 Fake News Dataset. In this section, you will know all the steps required to implement spacy lemmatization. For lemmatization algorithms to perform accurately, they need to. Named Entity Recognition (NER) Labelling named “real-world” objects, like persons, companies or locations. Lemmatization is the process of turning a word into its lemma. , the dictionary form) of a given word. Lemmatization is the process of reducing words to their base or dictionary form, known as the lemma. Essentially, lemmatization looks at a word and determines its dictionary form, accounting for its part of speech and tense. Abstract and Figures. The output of lemmatization is a root word called a lemma. To make the lemmatization better and context dependent, we would need to find out the POS tag and pass it on to the lemmatizer. Lemmatization is similar to stemming which also functions to reduce inflections in words. Process followed to convert text into tokens. stem. The act of lemmatization is, for example, replacing the word cooking with cook after you have tokenized your text data. 10. Third, lemmatization is a text data normalization technique to map different inflected forms of a word into one common root form or lemma. WordNetLemmatizer. 3. In case we want to find all the negative tweets during the pandemic, each tweet here is a document. Eg- “increases” word will be converted to “increase” in case of lemmatization while “increase” in case of stemming. The root of a word in lemmatization is called lemma. Lemmatization is the process wherein the context is used to convert a word to its meaningful base or root form. Lemmatization, in Natural Language Processing (NLP), is a linguistic process used to reduce words to their base or canonical form, known as the lemma. The method entails assembling the inflected parts of a word in a way that can be recognised as a single element. The Wikipedia definition of Lemmatization says, “ Lemmatisation (or lemmatization) in linguistics is the process of grouping together the inflected forms of a word so they can be analyzed as a single item, identified by the word’s lemma, or. pos) to be assigned, make sure a Tagger, Morphologizer or another component assigning POS is available in the pipeline and runs before the lemmatizer. Lemmatization: This step is very important, as in lemmatization, the rules of conjugating nouns and verbs based on gender, tense, etc. A lemma is usually the dictionary version of a word, it’s. The process is what we call lemmatization in NLP. What is Lemmatization and Stemming in NLP? Lemmatization is a pattern that NLP uses to identify word variations and determine the root of a word in natural language. Stemming and Lemmatization are algorithms that are used in Natural Language Processing (NLP) to normalize text and prepare words and documents for. " Following is the same sentence after lemmatization: Lemmatization. This way, we can reach out to the base form of any word which will be meaningful in nature. Usually, Lemmatization is preferred over Stemming because it is a contextual analysis of words instead of using a hard-coded rule to chop off suffixes. It involves longer processes to calculate than Stemming. Lemmatization is about extracting the basic form of a word (typically the kind of work you could find in a dictionnary).