The right tree is the actual edit tree we use in our model, the left tree visualizes. Lemmatization is more accurate than stemming, which means it will produce better results when you want to know the meaning of a word. As with other attributes, the value of . We write some code to import the WordNet Lemmatizer. In NLP, for example, one wants to recognize the fact. Lemmatization (also known as morphological analysis) is, for current purposes, the process of identifying the dictionary headword and part of speech for a corpus instance. A morpheme is often defined as the minimal meaning-bearingunit in a language. Lemmatization. 7. Abstract and Figures. Ans – TRUE. Given that the process to obtain a lemma from an inflected word can be explained by looking at its morphosyntactic category, in the corpus, that is, words that occur often in the same sentence are likely to belong to the same latent topic. To have the proper lemma, it is necessary to check the morphological analysis of each word. This helps in reducing the complexity of the data, making it easier for NLP. Lemmatization. Assigning word types to tokens, like verb or noun. However, there are. Morphological synthesis is a beneficial tool for various linguistic tasks and domains that require generating or modifying words. Lemmatization is the process of reducing words to their base or dictionary form, known as the lemma. ” Also, lemmatization leads to real dictionary words being produced. Normalization, namely, word lemmatization is a one of the main text preprocessing steps needed in many downstream NLP tasks. Traditionally, word base forms have been used as input features for various machine learning tasks such as parsing, but also find applications in text indexing, lexicographical work, keyword extraction, and numerous other language technology-enabled applications. 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. asked May 15, 2020 by anonymous. Lemmatization is a more powerful operation as it takes into consideration the morphological analysis of the word. Training BERT is usually on raw text, using WordPeace tokenizer for BERT. Here are the levels of syntactic analysis:. 03. ”. In contrast to stemming, Lemmatization looks beyond word reduction and considers a language’s full vocabulary to apply a morphological analysis to words. words ('english')) stop_words = stopwords. In this paper, we present an open-source Java code to ex-tract Arabic word lemmas, and a new publicly available testset for lemmatization allowing researches to evaluate analysis of each word based on its context in a sentence. Ans – False. This helps ensure accurate lemmatization. For example, the lemmatization of the word bicycles can either be bicycle or bicycle depending upon the use of the word in the sentence. They are used, for example, by search engines or chatbots to find out the meaning of words. 1 Morphological analysis. Related questions 0 votes. Besides, lemmatization algorithms may improve the performance results understudy, lemma is defined as the original of a word. Standard Arabic Language Morphological Analysis (SALMA) is a morphological analyzer proposed by Sawalha et al. Like word segmentation in Chinese, there are ambiguities in morphological analysis. This paper proposed a new method to handle lemmatization process during the morphological analysis. Lemmatization is a Natural Language Processing (NLP) technique used to normalize text by changing morphological derivations of words to their root forms. Get Help with Text Mining & Analysis Pitt community: Write to. However, stemming is known to be a fairly crude method of doing this. For text classification and representation learning. Stemming programs are commonly referred to as stemming algorithms or stemmers. Lemmatization is a text normalization technique in natural language processing. dicts tags for each word. The approach is to some extent language indpendent and language models for more langauges will be added in future. Omorfi (the open morphology of Finnish) is a package that has been licensed by version 3 of GNU GPL. Lemmatization Helps In Morphological Analysis Of Words lemmatization-helps-in-morphological-analysis-of-words 4 Downloaded from ns3. Lemmatization is a morphological analysis that uses dictionaries to find the word's lemma (root form). It identifies how a word is produced through the use of morphemes. the corpora with word tokens replaced by their lemmas. First, we have developed an initial Somali lexicon for word lemmatization with the consid-eration of the language morphological rules. Figure 4: Lemmatization example with WordNetLemmatizer. So it links words with similar meanings to one word. Chapter 4. (B) Lemmatization. Likewise, 'dinner' and 'dinners' can be reduced to. **Lemmatization** is a process of determining a base or dictionary form (lemma) for a given surface form. Lemmatization is one of the basic tasks that facilitate downstream NLP applications, and is of particu-lar importance for high-inflected languages. 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. The. Implementation. A major goal of the current revision of the Latin Dependency Treebank is to also document annotation choices for lemmatization. Mor-phological analyzers should ideally return all the possible analyses of a surface word (to model am-biguity), and cover all the inflected forms of a word lemma (to model morphological richness), cover-ing all related features. First, Arabic words are morphologically rich. Refer all subject MCQ’s all at one place for your last moment preparation. . Lemmatization reduces the number of unique words in a text by converting inflected forms of a word to its base form. Lemmatization in NLTK is the algorithmic process of finding the lemma of a word depending on its meaning and context. Lemmatization often involves part-of-speech (POS) tagging, which categorizes words based on their function in a sentence (noun, verb, adjective, etc. 3. The BAMA analysis that mostIt helps learners understand deep representations in downstream tasks by taking the output from the corrupt input. 1. Lemmatization is the algorithmic process of finding the lemma of a word depending on its meaning. 58 papers with code • 0 benchmarks • 5 datasets. 2 NLP systems for morphological analysis Lemmatization is part of morphological analysis, which forms the basis for many ap- plications in NLP systems, such as syntax parsing, machine translation and automatic indexing (Lezius et al. Natural Lingual Protocol. ART 201. , 2019), morphological analysis Zalmout and Habash, 2020) and part-of-speech tagging (Perl. Morph morphological generator and analyzer for English. 0 Answers. Lemmatization เป็นกระบวนการที่ใช้คำศัพท์และการวิเคราะห์ทางสัณฐานวิทยา (morphological analysis) ของคำเพื่อลบจุดสิ้นสุดที่ผันกลับมาเพื่อให้ได้. 0 Answers. which analysis is the most probable for each word, given the word’s context. Lemmatization and stemming are text. Following is output after applying Lemmatization. Lemmatisation, which is one of the most important stages of text preprocessing, consists in grouping the inflected forms of a word together so they can be analysed as a single item. As an example of what can go wrong, note that the Porter stemmer stems all of the. First one means to twist something and second one means you wear in your finger. Stemming calculation works by cutting the postfix from the word. 4. 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 a more powerful operation, and takes into consideration morphological analysis of the words. Technically, it refers to a process of knowing the internal structures to words by performing some decomposition operations on them to find out. While lemmatization (or stemming) is often used to preempt this problem, its effects on a topic model areMorphological processing of words involves the analysis of the elements that are used to form a word. (136 languages), word embeddings (137 languages), morphological analysis (135 languages), transliteration (69 languages) Stanza For tokenizing (words and sentences), multi-word token expansion, lemmatization, part-of-speech and morphology tagging, dependency. 1998). For example, it would work on “sticks,” but not “unstick” or “stuck. Watson NLP provides lemmatization. Discourse Integration. Lemmatization is a Natural Language Processing (NLP) task which consists of producing, from a given inflected word, its canonical form or lemma. Lemmatization reduces the text to its root, making it easier to find keywords. “Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove. 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. asked May 15, 2020 by anonymous. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words,. 0 Answers. Natural language processing (NLP) is a methodology designed to extract concepts and meaning from human-generated unstructured (free-form) text. look-up can help in reducing the errors and converting . NLTK Lemmatization is called morphological analysis of the words via NLTK. Given that the process to obtain a lemma from an inflected word can be explained by looking at its morphosyntactic category,in the corpus, that is, words that occur often in the same sentence are likely to belong to the same latent topic. Background The wide variety of morphological variants of domain-specific technical terms contributes to the complexity of performing natural language processing of the scientific literature related to molecular biology. Lemmatization, on the other hand, is a tool that performs full morphological analysis to more accurately find the root, or “lemma” for a word. 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. The standard practice is to build morphological transducers so that the input (or domain) side is the analysis side, and the output (or range) side contains the word forms. So, lemmatization and stemming are two methods for analyzing words for HLT enhancements in search technology. It aids in the return of a word’s base or dictionary form, known as the lemma. This is so that words’ meanings may be determined through morphological analysis and dictionary use during lemmatization. Lemmatization is a central task in many NLP applications. The stem of a word is the form minus its inflectional markers. Lemmatization is a process that identifies the root form of words in a given document based on grammatical analysis (e. The NLTK Lemmatization method is based on WordNet’s built-in morph function. Lemmatization and Stemming. In this paper, we present an open-source Java code to ex-tract Arabic word lemmas, and a new publicly available testset for lemmatization allowing researches to evaluateanalysis of each word based on its context in a sentence. However, there are some errors identified during the processLemmatization in NLTK is the algorithmic process of finding the lemma of a word depending on its meaning and context. In this paper we discuss the conversion of a pre-existing high coverage morphosyntactic lexicon into a deterministic finite-state device which: preserves accurate lemmatization and anno- tation for vocabulary words, allows acquisition and exploitation of implicit morphological knowledge from the dictionaries in the form of ending guessing rules. Computational morphological analysis Computational morphological analysis is an important first step in the auto-matic treatment of natural language. “ Stemming is a general operation while lemmatization is an intelligent operation where the proper form will be searched in the dictionary; as a result thee later makes better machine learning features. To extract the proper lemma, it is necessary to look at the morphological analysis of each word. Stemming and lemmatization shares a common purpose of reducing words to an acceptable abstract form, suitable for NLP applications. Lemmatization is a more effective option than stemming because it converts the word into its root word, rather than just stripping the suffices. ”. Lemmatization is more accurate than stemming, which means it will produce better results when you want to know the meaning of a word. Therefore, it comes at a cost of speed. Stemming. It is a study of the patterns of formation of words by the combination of sounds into minimal distinctive units of meaning called morphemes. 65% accuracy on part-of-speech tagging, The morphological tagging rate was 85. This is why morphology, and specifically diacritization is vital for applications of Arabic Natural Language Processing. 31 % and the lemmatization rate was 88. Lemmatization is commonly used to describe the morphological study of words with the goal of. Unlike stemming, which clumsily chops off affixes, lemmatization considers the word’s context and part of speech, delivering the true root word. Morphological analysis and lemmatization. Steps are: 1) Install textstem. words ('english') output = [w for w in processed_docs if not w in stop_words] print ("n"+str (output [0])) I have used stop word function present in the NLTK library. Consider the words 'am', 'are', and 'is'. It is an important step in many natural language processing, information retrieval, and information extraction. 5 million words forms in Tamil corpus. Using lemmatization, you can search for different inflection forms of the same word. While inflectional morphology is minimal in English and virtually non. Natural Lingual Processing. Based on the held-out evaluation set, the model achieves 93. From the NLTK docs: Lemmatization and stemming are special cases of normalization. lemmatization is one of the most effective ways to help a chatbot better understand the customers’ queries. Lemmatization can be done in R easily with textStem package. Stemming and Lemmatization . of noise and distractions. Stemming is the process of producing morphological variants of a root/base word. Lemmatization considers the context and converts the word to its meaningful base form, which is called Lemma. Meanwhile, verbs also experience changes in form because verbs in German are flexible. For example, the lemma of “was” is “be”, and the lemma of “rats” is “rat”. g. For instance, the word cats has two morphemes, cat and s, the cat being the stem and the s being the affix representing. 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. For morphological analysis of these texts, lemmatization has been actively applied in the recent biomedical research [2,11,12]. A good understanding of the types of ambiguities certainly helps to solve the ambiguities. Specifically, we focus on inflectional morphology, word internal. Natural Lingual Processing. Lemmatization uses vocabulary and morphological analysis to remove affixes of words. lemmatization can help to improve overall retrieval recall since a query willStemming works by removing the end of a word. In this paper, we explore in detail each of these tasks of. Lemmatization. Cmejrek et al. Q: lemmatization helps in morphological. It is a low-resource language that, to our knowledge, lacks openly available morphologically annotated corpora and tools for lemmatization, morphological analysis and part-of-speech tagging. Morphology is the study of the way words are built up from smaller meaning-bearing MORPHEMES units, morphemes. It helps in understanding their working, the algorithms that . Morphological analysis is the process of dividing words into different morphologies or morphemes and analyzing their internal structure to obtain grammatical information. In [20, 52] researchers presented Bengali stemmers based on longest suffix matching technique, distance based statistical technique and unsupervised morphological analysis technique. Essentially, lemmatization looks at a word and determines its dictionary form, accounting for its part of speech and tense. It helps in returning the base or dictionary form of a word, which is known as the lemma. Morphology captured by the part of speech tagset: Part of Speech tagset capture information that helps us to perform morphology. Lemmatization generally alludes to the morphological analysis of words, which plans to eliminate inflectional endings. Natural Lingual Protocol. The root node stores the length of the prefix umge (4) and the suffix t (1). Q: Lemmatization helps in morphological analysis of words. The advantages of such an approach include transparency of the. A related problem is that of parsing an inflected form, that is of performing a morphological analysis of that word. 4. While in stemming it is having “sang” as “sang”. To reduce a word to its lemma, the lemmatization algorithm needs to know its part of speech (POS). The best analysis can then be chosen through morphological. Lemmatization takes into consideration the morphological analysis of the words. In contrast to stemming, lemmatization looks beyond word reduction and considers a language’s full vocabulary to apply a morphological analysis to words. Arabic corpus annotation currently uses the Standard Arabic Morphological Analyzer (SAMA)SAMA generates various morphological and lemma choices for each token; manual annotators then pick the correct choice out of these. import nltk from nltk. accuracy was 96. use of vocabulary and morphological analysis of words to receive output free from . Morphology looks at both sides of linguistic signs, i. The best analysis can then be chosen through morphological disam-1. Lemmatization can be implemented using packages such as Wordnet (nltk), Spacy, textblob, StanfordCoreNlp, etc. 1. from polyglot. In contrast to stemming, lemmatization looks beyond word reduction and considers a language’s full vocabulary to apply a morphological analysis to words. Many lan-guages mark case, number, person, and so on. In modern natural language processing (NLP), this task is often indirectly. indicating when and why morphological analysis helps lemmatization. It makes use of vocabulary (dictionary importance of words) and morphological analysis (word structure and grammar. Lemmatization is a vital component of Natural Language Understanding (NLU) and Natural Language Processing (NLP). 7) Lemmatization helps in morphological analysis of words. It's often complex to handle all such variations in software. The small set of rules and fewer inflectional classes are of great help to lexicographers and system developers. Conducted experiments revealed, that the accuracy of automatic lemmatization of MWUs for the Polish language according to. 2. The key feature(s) of Ignio™ include(s) _____ Ans – All the options. Learn more. The morphological analysis of words is done in lemmatization, to remove inflection endings and outputs base words with dictionary. ac. Surface forms of words are those found in natural language text. g. The method consists three layers of lemmatization. Q: lemmatization helps in morphological analysis of words. Related questions 0 votes. Variations of the same word, or inflections, such as plurals, tenses, etc are grouped together to simplify the analysis of word frequencies, patterns, and relationships within a corpus of text. “Automatic word lemmatization”. The poetic texts pose a challenge to full morphological tagging and lemmatization since the authors seek to extend the vocabulary, employ morphologically and semantically deficient forms, go beyond standard syntactic templates, use non-projective constructions and non-standard word order, among other techniques of the. Since the process. Lemmatization; Stemming; Morphology; Word; Inflection; Corpus; Language processing; Lexical database;. Morphological analysis is always considered as an important task in natural language processing (NLP). This will help us to arrive at the topic of focus. 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. Lemmatization. , 2019;Malaviya et al. •The importance of morphology as a problem (and resource) in NLP •What lemmatization and stemming are •The finite-state paradigm for morphological analysis and lemmatization •By the end of this lecture, you should be able to do the following things: •Find internal structure in words •Distinguish prefixes, suffixes, and infixes Morphological analysis and lemmatization. asked May 14, 2020 by anonymous. 5 Unit 1 . [11]. Illustration of word stemming that is similar to tree pruning. These groups are. Omorfi (the open morphology of Finnish) is a package that has been licensed by version 3 of GNU GPL. morphological information must be always beneficial for lemmatization, especially for highlyinflectedlanguages,butwithoutanalyzingwhetherthatistheoptimuminterms. Abstract and Figures. Therefore, showed that the related research of morphological analysis has also attracted the attention of most. Stemming and Lemmatization help in many of these areas by providing the foundation for understanding words and their meanings correctly. Time-consuming: Compared to stemming, lemmatization is a slow and time-consuming process. For the Arabic language, many attempts have been conducted in order to build morphological analyzers. Morphology and Lemmatization Morphology concerns itself with the internal structure of individual words. After converting the text data to numerical data, we can build machine learning or natural language processing models to get key insights from the text data. Thus, we try to map every word of the language to its root/base form. ). Actually, lemmatization is preferred over Stemming because lemmatization does morphological analysis of the words. mohitrohit5534 mohitrohit5534 21. Based on that, POS tags are suggested to words in a sentence. So it links words with similar meanings to one word. 1 Introduction Morphological processing of words involves the analysis of the elements that are used to form a word. using morphology, which helps discover theThis helps to deal with the so-called out of vocabulary (OOV) problem. “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. Natural Language Processing. Lemmatization helps in morphological analysis of words. This means that the verb will change its shape according to the actor's subject and its tenses. This is an example of. The Morphological analysis would require the extraction of the correct lemma of each word. The root of a word is the stem minus its word formation morphemes. e. This paper pioneers the. Purpose. The same sentence in the example above reduces to the following form through lemmatization: Other approach to equivalence class include stemming and. It plays critical roles in both Artificial Intelligence (AI) and big data analytics. For instance, it can help with word formation by synthesizing. Lemmatization is the process of reducing words to their base or dictionary form, known as the lemma. Question 191 : Two words are there with different spelling but sound is same wring (1) and wring (2). Lemmatization and stemming both reduce words to their base forms but oper-ate differently. Question _____helps make a machine understand the meaning of a. For instance, the word "better" would be lemmatized to "good". It produces a valid base form that can be found in a dictionary, making it more accurate than stemming. For instance, the word forms, introduces, introducing, introduction are mapped to lemma ‘introduce’ through lemmatizer, but a stemmer will map it to. 4. 2. In languages that exhibit rich inflectional morphology, the signal becomes weaker given the proliferation of unique tokens. Stemming. Morphological analysis, especially lemmatization, is another problem this paper deals with. This process is called canonicalization. lemmatizing words by different approaches. This article analyzes the issue of creating morphological analyzer and morphological generator for languages other than English using stemming and. ; The lemma of ‘was’ is ‘be’,. isting MA/LN methods for non-general words and non-standard forms, indicating that the corpus would be a challenging benchmark for further research on UGT. Lexical and surface levels of words are studied through morphological analysis. MADA (Morphological Analysis and Disambiguation for Arabic) makes use of up to 19 orthogonal features to select, for each word, a proper analysis from a list oflation suggest that morphological analysis may be quite productive for this highly in ected language where there is only a small amount of closely trans-lated material. g. For example, the lemma of the word “cats” is “cat”, and the lemma of “running” is “run”. Part-of-speech tagging helps us understand the meaning of the sentence. Artificial Intelligence<----Deep Learning None of the mentioned All the options. To correctly identify a lemma, tools analyze the context, meaning and the. , person, number, case and gender, on the word form itself. Sometimes, the same word can have multiple different Lemmas. Lemmatization and POS tagging are based on the morphological analysis of a word. Lemmatization Drawbacks. 1992). The process transforms words into a standard form in order to analyze the underlying morphology and extract meaningful insights. Since the process may involve complex tasks such as understanding context and determining the part of speech of a word in a sentence (requiring, for example, knowledge of the grammar of a. Our purpose in this article is to provide a systematic review of the evidence about the effects of instruction about the morphological structure of words on lit-eracy learning. edited Mar 10, 2021 by kamalkhandelwal29. The analysis also helps us in developing a morphological analyzer for Hindi. Keywords: meta-analysis, instructional practices, literacy, reading, elementary schools. When working with Natural Language, we are not much interested in the form of words – rather, we are concerned with the meaning that the words intend to convey. importance of words) and morphological analysis (word structure and grammar relations). Lemmatization always returns the dictionary meaning of the word with a root-form conversion. It helps in returning the base or dictionary form of a word, which is known as the lemma. The words ‘play’, ‘plays. Lemmatization and Stemming. Second, undiacritized Arabic words are highly ambiguous. Results: In this work, we developed a domain-specific lemmatization tool, BioLemmatizer, for the morphological analysis of biomedical literature. Clustering of semantically linked words helps in. While lemmatization (or stemming) is often used to preempt this problem, its effects on a topic model are Abstract. R. This year also presents a new second challenge on lemmatization and. 0 Answers. The lemmatization process in these words can be done by reducing suffixes or other changes by analyzing the word level or its morphological process. Stemming programs are commonly referred to as stemming algorithms or stemmers. This task is achieved by either ranking the output of a morphological analyzer or through an end-to-end system that generates a single answer. Lemmatization is one of the basic tasks that facilitate downstream NLP applications, and is of particular importance for high. 4) Lemmatization. g. The tool focuses on the inflectional morphology of English and is based on. Lemmatization is an important data preparation step in many natural language processing tasks such as machine translation, information extraction, information retrieval etc. py. [1] 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 . This is done by considering the word’s context and morphological analysis. It helps in returning the base or dictionary form of a word, which is known as the lemma. In other words, stemming the word “pies” will often produce a root of “pi” whereas lemmatization will find the morphological root of “pie”. For example, saying that 'hominis' is genitive singular of lemma 'homo, -inis'. Morphology captured by the part of speech tagset: Part of Speech tagset capture information that helps us to perform morphology. In order to assist in efficient medical text analysis, lemmas rather than full word forms in input texts are often used as a feature for machine learning methods that detect medical entities . It helps in returning the base or dictionary form of a word, which is known as. It helps in returning the base or dictionary form of a word known as the lemma. We leverage the multilingual BERT model and apply several fine-tuning strategies introduced by UDify demonstrating exceptional. In Watson NLP, lemma is analyzed by the following steps:Lemmatization: This process refers to doing things correctly with the use of vocabulary and morphological analysis of words, typically aiming to remove inflectional endings only and to return the base or dictionary form. This process helps ac a better understanding of the text and provides accurate results by understanding the context in which the words are used. The problem is, there are dozens of choices for each tokenThe meaning of LEMMATIZE is to sort (words in a corpus) in order to group with a lemma all its variant and inflected forms. , producing +Noun+A3sg+Pnon+Acc in the first example) are. 1 Answer. This paper reviews the SALMA-Tools (Standard Arabic Language Morphological Analysis) [1]. g. “ Stemming is a general operation while lemmatization is an intelligent operation where the proper form will be searched in the dictionary; as a result thee later makes better machine learning features. These come from the same root word 'be'. 1. 2. Training data is used in model evaluation. Morphological Analysis is a central task in language processing that can take a word as input and detect the various morphological entities in the word and provide a morphological representation of it. For morphological analysis of these texts, lemmatization has been actively applied in the recent biomedical research. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. On the Role of Morphological Information for Contextual Lemmatization. The system can be evaluated simply in every feature except the lexeme choice and dia- by comparing the chosen analysis to the gold stan- critics. Technique A – Lemmatization. The second step performs a fine-tuning of the morphological analysis of the highest scoring lemmatization obtained in the first step. It consists of several modules which can be used independently to perform a specific task such as root extraction, lemmatization and pattern extraction. 0 votes. g. Lemmatization and stemming both reduce words to their base forms but oper-ate differently. Lemmatization is a more effective option than stemming because it converts the word into its root word, rather than just stripping the suffices. dep is a hash value. Technique B – Stemming. Stemming is a simple rule-based approach, while. Two other notions are important for morphological analysis, the notions “root” and “stem”. Answer: B.