Check out this DataCamp. Also, stemming may or may not return a valid stem or root, whereas lemmatization will return a linguistically correct root. Lemmatization. For example, the input sequence “I ate an apple” will be lemmatized into “I eat a apple”. Stemming follows an algorithm with steps to perform on the words which makes it faster. In the case of a chatbot, lemmatization is one of the best methods to assist a chatbot in recognizing the customers’ queries. When people use the word “stemming” in natural language processing, they typically mean a system like the one we’ve been describing in this chapter, with rules, conditions, heuristics, and lists of word endings. As a result, NLTK Lemmatization is critical for comprehending a text and applying it to Natural Language Processing and. Part of speech tagger and vocabulary words helps to return the dictionary form of a word. It works by progressively applying a set of rules, until the normalized form is obtained. Lemmatization. Its goal is to combine semantically similar words based on context, so it actually doesn't have a problem with the kind of variation you see in English. Walking, when used as an adjective, is. g. It often results in words that have no meaning to the users. It doesn’t just chop things off, it actually transforms words to the actual root. This research paper aims to provide a general perspective on Natural Language processing, lemmatization, and Stemming. These processes are an essential part of the NLP pipeline. To lemmatize a single word, you can simply pass the word to the lemmatize method of the lemmatizer object. We would like to show you a description here but the site won’t allow us. A prototype search. Lemmatization can not find the core of the word happiness. The idea of this paper is to explain how a stemming. This usually involves stripping off any affixes in the word. The Stanford CoreNLP Java library contains a lemmatizer that is a little resource intensive but I have run it on my laptop with <512MB of RAM. Lemmatization. A custom function has been created for lemmatization and stemming with NLTK which is “lemme_stem”. In this process, the inflected word is converted to their stem word. Comments (0) Run. Lemmatization is the process of converting a word to its base form. In language, inflection is how different grammatical categories such as tense, mood, or gender can be expressed by modifying a common root word. Lemmatization (or less commonly lemmatisation) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. Below is an example of the plain usage of the CountVectorizer:. . So it goes a steps further by linking words with similar meaning to one word. Define a function called performStemAndLemma, which takes a parameter. However, it is more resource intensive. In order to overcome this drawback, we shall use the concept of Lemmatization. Lemmatization can be done in R easily with textStem package. Stemming and Lemmatization are techniques used in text processing. The problem with stemming, lemmatization, and spelling regularization is that they have the same objective as the topic model itself. Part of speech tagger and vocabulary words helps to return. 3. This confusion occurs because both techniques are usually employed to reduce words. Lemmatization. stem. Stemming is a text normalization technique used in NLP. Tasks such as Text classification or spam filtering makes use of NLP along with deep learning libraries such as Keras and Tensorflow. Different stemming approaches exist, but we will focus on the most commonly known for English: PorterStemmer, developed in 1980 by Martin Porter. Christopher D. Such conversion of words restricts the use of porter and snowball stemming methods to search engines, n-gram context, and text classification problems. 1 Answer. Stemming and lemmatization. Logs. Steps are: 1) Install textstem. Lemmatization has higher accuracy than stemming. 4. It is often stored without a predefined format and can be hard to obtain and process. Example: After stemming, the sentence, "the fishermen fished for fish", can be represented in a bag of words like this. g. For example, inflected forms of a word, say ‘warm’, warmer’, ‘warming’, and ‘warmed,’ are represented by a single token ‘warm’, because they all represent the same meaning. To associate your repository with the stemming topic, visit your repo's landing page and select "manage topics. Taking on the previous example, the lemma of cars is car, and the lemma of replay is replay itself. WordNetLemmatizer(). In lemmatization, we need to know the part of speech of the tokens like. Stemming is the rule-based technique for. In many situations, it seems as if it would be useful. Stemming is a process that removes endings such as affixes. Additionally, there are families of derivationally related words. Lemmatization is different from stemming, which is another process used in NLP to reduce words to their root form. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order. To use it: Download the jar files; Create a new project in your editor of choice/make an ant script that includes all of the jar files contained in the archive you just downloaded;Hello All,In this video, we will be understanding the meaning of Stemming and Lemmatization in NLP. It often results in roots or word parts that are not actual words, whereas lemmatization always returns valid dictionary words. Step 4: Lemmatization is identical to stemming except that it removes endings only if the base form is present in a dictionary. For example, converting the word “walking” to “walk”. Lemmatization method has analyzed the structure of words, the relationship between words and parts of words to accurately identify the root word. MADA operates by examining a list of all possible analyses for each word, and then selecting the analysis that matches the current context best by means of support vector machine models classifying for 19 distinct. 4. If you want a base form, you need a lemmatizer. Stemming and lemmatization are two language modeling techniques used to improve the document retrieval precision performances. Stemming, in Natural Language Processing (NLP), refers to the process of reducing a word to its word stem that affixes to suffixes and prefixes or the roots. Stemming may be seen as a crude heuristic process that simply chops off ends of words. While a stemming algorithm is a linguistic normalization process in which the variant forms of a word are reduced to a standard form. Stemming allows each string of text to be represented in a smaller bag of words. 6 Lemmatization and stemming. Lemmatization is the process of grouping inflected forms together as a single base form. In lemmatization, the word we get after affix removal (also known as lemma) is a meaningful one. Stemming and lemmatization via Python is a bit more obtuse than the three previous techniques. Lemmatization already takes care of stemming so you don't have to do both. 2) Load the package by library (textstem) 3) stem_word=lemmatize_words (word, dictionary = lexicon::hash_lemmas) where stem_word is the result of lemmatization and word is the input word. Stemming is derived from stem, and the stem of a word is the unit to which affixes are attached. It plays critical roles in both Artificial Intelligence (AI) and big data analytics. Lemmatization takes more time as compared to stemming because it finds meaningful word/ representation. Lemmatization vs. In lemmatization, rather than just removing the suffix and the prefix, the process tries to find out the root word with its. '] vec = CountVectorizer(). Notebook. However, stemming may not give the actual word, whereas lemmatization generates a meaningful word. , the dictionary form) of a given word. The stems returned through lemmatization are actual dictionary words and are semantically complete unlike the words returned by stemmer. fr 2 École Polytechnique de Montréal, CP. To be precise, an integrated stemming-lemmatization (S-L) model was developed and its retrieval performance was compared at three document levels, that is, at top 5, 10 and 15. For Stemming: NLTK has Porter Stemmer which is widely used. Stemming and lemmatization are techniques used to reduce words to their base or root form, which helps simplify text analysis and reduce the dimensionality of the data. In many situations, it seems as if it would be useful. Build Fast and Accurate Lemmatization for Arabic. For stemming English words with NLTK, you can choose between the PorterStemmer or the LancasterStemmer. De-Capitalization - Bert provides two models (lowercase and uncased). This is done to make interpretation of speech consistent across different words that all mean essentially the same thing, which makes NLP processing faster. Stemming and lemmatization. The first parameter, textcontent, is a string. This character uses the phonetic sound for horse but the gender indicator of female. Stemming is a procedure to strip inflectional and derivational suffixes from index and search terms with the aim to merge different word forms into one canonical form, called stem or root. The stemming process just follows the step-by-step implementation of algorithms like SnowBall, Porter, etc. 1. Examples of lemmatization and stemming are shown below. ) Cancel NLP Stemming and Lemmatization using Regular expression tokenization: The question discusses the different preprocessing steps and does stemming and lemmatization separately. Add this topic to your repo. Stemming vs Lemmatization. 4. 1. ตามหลักตามไวยากรณ์ภาษาอังกฤษ คำหนึ่งคำจะแปร. Like stemming and lemmatization, named entity recognition, or NER, NLP's basic and core techniques are. After stemming we get “Hi team are not winn ” . The result of lemmatization is called a ‘lemma,’ which is a root word rather than a root stem, which is the result of stemming. Let’s start with the split () method as it is the most basic one. はい，英語の 形態素 は" " (スペース)区切りで簡単だよって言いますね．. Tokenize all the words given in textcontent. The current study proposes to compare document retrieval precision performances based on language modeling techniques, particularly stemming and lemmatization. "Lemmatization: The goal is same as with stemming, but stemming a word sometimes loses the actual meaning of the word. The most famous stemmer is called the Porter stemmer, published by Martin Porter in 1980. Though we could not perform stemming with spaCy, we can perform lemmatization using spaCy. It does so by considering the context and morphological basis of each word. It involves longer processes to calculate than Stemming. This character uses the phonetic sound for horse but the gender indicator of female. Stemming is the process of reducing a word to its stem that affixes to suffixes and prefixes or to the roots of words known as "lemmas". However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. Stemming คืออะไร. One problem with streaming is that chopping words may. arrow_right_alt. In this article, we learned about different normalization techniques: Case folding, stemming, and lemmatization. Such conversion of words restricts the use of porter and snowball stemming methods to search engines, n-gram context, and text classification problems. Stemming is a. These are widely used systems for tagging, SEO, web search results, and information retrieval. Additionally, there are families of derivationally related words with similar meanings, such as democracy, democratic, and democratization. Stemming & Lemmatization. Stemming: Stemming is a rudimentary rule-based process of stripping the suffixes (“ing”, “ly”, “es”, “s” etc) from a word. For example, the stem of the word ‘happy’ is ‘happi’, but its lemma is ‘happy’, which is linguistically valid. Text preprocessing includes both Stemming as well as Lemmatization. Perbedaannya adalah bahwa Stemming mungkin bukan kata yang sebenarnya sedangkan Lemmatization adalah kata. For Russian, someone seems to have used Snowball Stemmer. 56. Lemmatization is the process of finding the base form (or lemma) of a word by considering its inflected forms. 'universal' and 'university' result in same stem. snowball stemmer is defined as Stemmer () and WordNetLemmatizer is defined as lemmatizer () def find_roots (token_list, n): n = 2. Stemming and lemmatization are algorithmic adjustments built into a database platform. We can now define a TfidfVectorizer with our custom callable! ngram_range = ( 1, 1 ) max_features = 1000 use_idf = True tfidf = TfidfVectorizer (tokenizer = self. Stemming is somewhat a make-do method for cataloging related words. lemmatization. For instance, the radicals for female and horse come together for the character mother. Though the goals of stemming are similar to those of lemmatization, an important distinction is that stemming does not aim to generate a naturally occurring, dictionary form of a word - for instance, the stem of "regulated" would be "regul" rather than the base verb form "regulate". word_tokenize (norm_corpus [i]) words = [stemmer. These techniques are used by chatbots and search engines to analyze the meaning behind the search queries. A stem is the largest part of a word that does not contain prefixes or suffixes. Stemming and lemmatization are vital techniques in NLP for transforming words into their base or root forms. If possible you can try to lemmatize/stem the strings on your input "Utterance" string field, before creating the DV. Snowball. If you have large dataset and performance is an issue, go with Stemming. Though we could not perform stemming with spaCy, we can perform lemmatization using spaCy. Stemming is a process of removing and replacing word suffixes to arrive at a common root form of the word. I think stemming a lemmatized word is redundant if you get the same result than just stemming it (which is the result I expect). Apply lemmatization/stemming before creating the input DataView. snowball stemmer is defined as Stemmer () and WordNetLemmatizer is defined as lemmatizer () def find_roots (token_list, n): n = 2. Python NLTK. The Porter Stemming Algorithm is the oldest. Name. Lemmatization is a systematic process of removing the inflectional form of a token and transform it into a. Technique A – Lemmatization. Stemming and lemmatization are algorithmic adjustments built into a database platform. Use stemming or lemmatization (remember proper lemmatization requires POS tagging) Depending on dataset size/goal/memory availability you can check the following: Most popular words; Common n-grams; Look for specific grammar chunks; Further Work. Stemming is the process of reducing the inflected forms of a word to its root form also known as the stem. Stemming and Lemmatization both generate the foundation sort of the inflected words and therefore the only difference is. Lemmatization on the surface is very similar to stemming, where the goal is to remove inflections and map a word to its root form. Explore and run machine learning code with Kaggle Notebooks | Using data from Natural Language Processing with Disaster TweetsText preprocessing is an essential step in natural language processing (NLP) that involves cleaning and transforming unstructured text data to prepare it for analysis. Stemming and lemmatization were developed in the 1960s. The word generated after lemmatization is also called a lemma. Stemming and lemmatization are 2 popular techniques in NLP. Stemming and Lemmatization are text normalization techniques within the field of Natural language Processing that are used to prepare text, words, and documents for further processing. Lemmatization is more accurate than stemming, which means it will produce better results when you want to know the meaning of a word. e. It is similar to stemming, in turn, it gives the stripped word that. Problem 6: Hands on Stemming and Lemmatization. They don't make sense to do together; it's one or the other. Text data is a common type of unstructured data found in analytics. Stemming is cheap, nasty and fallible. Step 5: Obtaining the stem words. Stemming and Lemmatization. Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes. 6 Lemmatization and stemming. Additionally, there are families of derivationally related words with similar meanings, such as democracy, democratic, and democratization. Python Stemming and Lemmatization - In the areas of Natural Language Processing we come across situation where two or more words have a common root. A related approach to lemmatization, stemming, is based on simple heuristic rules. Whereas lemmatization makes use of a lookup database like WordNet to derive. What follows after text normalization is creating a bag-of-words (BOW). Consider the sentence ” His teams are not winning”. Michael here, and today’s lesson will cover stemming and lemmatization in Python NLP (natural language processing). This process is generally. License. Lemmatization is a technique to reduce words to their base form, or lemma. Part of NLP Collective. 1. In lemmatization, the word we get after affix removal (also known as lemma) is a meaningful one. Output. Lemmatization’ı kullanmaya başlamadan önce Python ile aşağıdaki kaynakları local’imize indirmemiz gerekebilir(Ben yine Jupyter Notebook ile kullanmaya devam edeceğim. Stemming and lemmatization differ in their approach and sophistication but serve the same objective. Both NumPy and Pandas are imported in case you have a preference when manipulating your data. nlp. Stemming and Lemmatization are broadly utilized in Text mining where Text Mining is the method of text analysis written in natural language and extricate high-quality information from text. In order to get correct form of words in text. This process of normalization is called stemming or lemmatization. The lemmatization of walking is ambiguous. It is different from Stemming. 31. Also, stemming may or may not return a valid stem or root, whereas lemmatization will return a linguistically correct root. . Lemma is also called dictionary form, or citation. In lemmatization, the word that is generated after chopping off the suffix is always meaningful and belongs to the dictionary that means it does not produce any incorrect word. updat-e, or updat-ing. Stemming is (usually) a short procedure which uses string matching to remove parts of a string. I was wondering if anybody had experience in lemmatizing the corpus before training word2vec and if this is a useful preprocessing step to do. As previously mentioned, stemming is a rule-based text normalization technique that eliminates the prefix and suffix of a word to attain its root form. A stem is a part of a word responsible for its lexical meaning. It looks beyond word reduction and considers a language’s full. Notice that the keyword winn is not a regular word. According to UNESCO, the Arabic language is spoken by more than 422 million native. Stemming and lemmatization attempts to get root word (for eg rain) for different word inflections (raining, rained etc). It looks beyond word reduction and considers a language’s full vocabulary to apply a morphological analysis to words, aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma. 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. Lemmatization is the process of grouping inflected forms together as a single base form. FAQs on Stemming in NLP 1) What is the difference between Lemmatization and Stemming? In stemming, there is no need of a dictionary of words unlike lemmatization that requires a dictionary. Lemmatization. My intuition said that steamming increses recall and lowers precision and the opposite for a lemmatization. Truncation and wildcards are simple modifications you incorporate into a term you type. Stemming involves the removal of a word’s suffix to reduce the size of the vocabulary (Porter 1980 ). It works by progressively applying a set of rules, until the normalized form is obtained. their lemma. It is different from Stemming. Either Stemming or Lemmatization can be used. Let’s check it out. A Word Stemming Algorithm for Hausa Language. In this article we saw what Stemming and Lemmatization are all about. So, by using stemming, one can accurately get the stems of different words from the search engine index. Hence, Lemmatization helps in forming better features. Algorithms that do this are called stemmers. Stemming returns words which are not really dictionary. Unlike stemming , lemmatization depends on correctly identifying the intended part of speech and meaning of a word in a sentence, as well as within the larger context surrounding that sentence, such as. Stemming and lemmatization are two common techniques for reducing the number of words in natural language processing (NLP) applications. The purpose of lemmatization is the same as that of stemming. and the values being the nth word transformed in that way. This paper presents a new customized Bert method based sentiment analysis classification. Thus stemming & lemmatization help reduce words like ‘studies’, ‘studying’ to a common base form or root word ‘study’. Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of. These techniques normalize the text, allowing for more accurate analysis, information retrieval. Both normalizes a word but in different ways. Lemmatization reduces the word to its stem as it appears in the dictionary. Both in stemming and in. This is a disadvantage of stemming. Stem and lemmatization# def stem (self, string: str): """ Stem a string using Regex pattern. Installing Spark-NLP. In this tutorial you will use the process of lemmatization, which normalizes a word with the context of vocabulary and morphological analysis of words in text. Answer: b) The statement describes the process of tokenization and not stemming, hence it is. Stemming and lemmatization lemmatization Stemming and lemmatization lemmatizer Stemming and lemmatization length-normalization Dot products Levenshtein distance Edit distance lexicalized subtree A vector space model lexicon An example information retrieval likelihood Review of basic probability likelihood ratio Finite automata and language. Many. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of. Stemming is usually faster than. For example, the words “friends,” “friendship,” “friendships” will be reduced to “friend. Tokenization can be a part of a preprocessing process before or after (or both) lemmatization and stemming. A better efficient way to proceed is to first lemmatise and then stem, but stemming alone is also fine for few problems statements, here we will not. Consider the word “better” which mapped to “good” as its lemma. Stemming and lemmatization are methods used by search engines and chatbots to analyze the meaning behind a word. Walking, when used as an adjective, is its own baseform (rather than walk). Knowing how they work, and how you work them, gives you an easy way improve your literature searches. In most natural languages, a root word can have many variants. If you are using Tensorflow 2, make sure Tensorflow Addons already installed,Answer: (c) Lemmatization and Stemming. 在英文語句中，同一個單詞的拼法可能會隨著時態、單複數、主被動等狀況而有所改變，如 speaking / speak. Lemmatizer. edureka! Stemming Lemmatization 1960’s 12. Lemmatization is the process of finding the form of the related word in the dictionary. Stemming & Lemmatization. Lemmatization. Stemming is a related concept that simply. ) CancelNLP Stemming and Lemmatization using Regular expression tokenization: The question discusses the different preprocessing steps and does stemming and lemmatization separately. Lemmatization uses morphological analysis and vocabulary to convert a word from its surface form to root form. g. The distinction between stemming and lemmatization is while stemming changes a word into a root word without knowing the context of the word like cutting off the ends of words, lemmatization. jump, jumps, jumping) and in other cases, words may derive from a common meaning (e. or in literal. If you want more coding experience, here are a few ideas to consider:Stemming and Lemmatization. For example, walking and walked can be stemmed to the same root word: walk. Therefore, procedures like stemming and lemmatization are not useful for Chinese text data because seperating the radicals. Both the stemming and the lemmatization processes involve morphological analysis where the stems and affixes (called the morphemes) are extracted and used to reduce inflections to their base form. Lemmatization and stemming are text normalization techniques used in Natural Language Processing (NLP). Difference between Stemming and Lemmatisation – A stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words which have different meanings depending on part of speech. Stemming is a procedure to reduce all words with the same stem to a common form whereas lemmatization removes inflectional endings and returns the base or dictionary form of a word. Whereas lemmatization is used when it comes to chatbots and displaying the reviews of the site, services, or products. Stemming and lemmatization both involve the process of removing additions or variations to a root word that the machine can recognize. Stemming uses the stem of the word, while lemmatization uses the context in which the word is being used. Natural Language toolkit has very important module NLTK tokenize sentences which further comprises of sub-modules. We will receive a legitimate term that signifies the same thing. Stemming and Lemmatization are two different approaches for stripping a term within a document so that a document matrix reduces and the complexity of data decreases. 6. 2) Load the package by library (textstem) 3) stem_word=lemmatize_words (word, dictionary = lexicon::hash_lemmas) where stem_word is the result of lemmatization and word is the input word. For example, the word. edureka! miss 13. Stemming and Lemmatization are both text normalization techniques in Natural Language Processing. lemmatization which reduce s words to dictionary roo ts which . 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. Stemming and Lemmatization . 4 is the only supported version): $ conda install pyspark==2. Once stemmed, an occurrence of either word would match the other in a search. ( **Natural Language Processing Using Python: - ** )This video will provide you with a deta. For our purpose, we will use the following library-a. Stemming and Lemmatization are both text normalization techniques in Natural Language Processing. NLTK edureka! NLTK 17. 6128 succursale Centre-ville, Montréal, Québec,. Each approach provides some benefits by reducing the vocabulary size, allowing for. stemming and lemmatization in detail along with codes will be discussed. Unlike stemming, lemmatization reduces words to their base word, reducing the inflected words properly and ensuring that the root word belongs to the language. While both techniques are similar, they produce different results so it is important to determine the proper one for the. Lemmatization aims to achieve a similar base “stem” for a specified word. Lemmatization is much more costly and advanced relative to stemming. Stemming. 1. In Stanza, lemmatization is performed by the LemmaProcessor and can be invoked with the. RDocumentation. On the other hand, lemmatization produces valid and. The blank space removal method, stop word removal, and stemming methods were used in. Stemming and Lemmatization are algorithms that are used in Natural Language Processing (NLP) to normalize text and prepare words and documents for. Christopher D. The main way a researcher can optimize their search is with truncation. Hence. Lemmatization and stemming are implemented in this case. The stem of a word update is indeed "updat". 24. " GitHub is where people build software. Lemmatization is the process of determining what is the lemma (i. The tokenization process splits the stream of text into words . Both the techniques break down the search queries into their root. a. Stemming and Lemmatization are text preprocessing methods within the field of NLP that are used to standardize text, words, and documents for further analysis. Stemming is a rule-based approach, whereas lemmatization is a canonical dictionary-based approach. Stemming is a process to remove affixes from a word, ending up with the stem. Visualization Three – Bar Chart: Click on the Stacked Bar Chart in the Visualizations pane, to add it to the page. Reducing words to their stem decreases sparsity and makes it easier to find patterns and make predictions. If accuracy is paramount and dataset isn't humongous, go with Lemmatization. Stemming is derived from stem, and the stem of a word is the unit to which affixes are attached. 7) Stemming and Lemmatization Stemming is a process to reduce the word to its root stem for example run, running, runs, runed derived from the same word as run. 56. In linguistics, lemmatization is closely related to stemming, as both strip prefixes and suffixes that have been added to a word's base form. Stemming and Lemmatization are text/word normalization techniques widely used in text pre-processing. The result of lemmatization is called a ‘lemma,’ which is a root word rather than a root stem, which is the result of stemming. For instance, the word cats has two morphemes, cat and s, the cat being the stem and the s being the affix representing plurality. Lemmatization is preferred for. Applications include high-accuracy part-of-speech tagging, diacritization, lemmatization, disambiguation, stemming, and glossing. Several Arabic light and heavy stemmers as well as lemmatization algorithms are used in this study, with a total of 10 algorithms. Though stemming and lemmatization both generate the root form of inflected/desired words, but lemmatization is an advanced form of stemming. For example, web pages contain text data that data analysts collect through web scraping and pre-process using lowercasing, stemming, and lemmatization. However, it is more resource intensive. Stemming programs are commonly referred to as stemming algorithms or stemmers. NLTK edureka! 16. I am applying Latent Dirichlet Allocation to 230k texts in order to organize the data presented. The only difference is that, lemmatization tries to do it the proper way. ”. Lemmatization is often confused with another technique called stemming. Learn the difference between lemmatization and stemming, two methods of normalizing words in natural language processing. Lemmatization concept is used to make dictionary or WordNet kind of dictionary. The root word is called a stem in the. Stemming any word means returning stem of the word. You can find more info about stemming and lemmatization in this post from Stanford. .