Why can we process the word of

1. Artificial Intelligence.
2. How did Natural Language Processing come to exist?
3. How does Natural Language Processing work? 
4. Why Natural Language Processing is important?
5. Why is advancement in the field of Natural Language Processing necessary?
6. What can Natural Language Processing do?
7. What are some of the applications of NLP?
8. How to learn Natural Language Processing?
9. Available Open Source Softwares in NLP Domain
10. What are Regular Expressions?
11. What is Text Wrangling?
12. What factors decide the quality and quantity of text cleansing?
13. How do we define Text Cleansing?
14. What is Stemming?
15. What are the various types of Stemming?
16. What is Lemmatization in NLP?
17. What is Rare word removal?
18. What is Spell Correction?
19. What is Dependency Parsing?

What is Natural Language Processing?

Natural language processing is the application of computational linguistics to build real-world applications which work with languages comprising of varying structures. We are trying to teach the computer to learn languages, and then also expect it to understand it, with suitable efficient algorithms. 
All of us have come across Google’s keyboard which suggests auto-corrects, word predicts (words that would be used) and more. Grammarly is a great tool for content writers and professionals to make sure their articles look professional. It uses ML algorithms to suggest the right amounts of gigantic vocabulary, tonality, and much more, to make sure that the content written is professionally apt, and captures the total attention of the reader. Translation systems use language modelling to work efficiently with multiple languages.

Check out the free NLP course by Great Learning Academy to learn more.

How did Natural Language Processing come to exist?

People involved with language characterization and understanding of patterns in languages are called linguists. Computational linguistics kicked off as the amount of textual data started to explode tremendously. Wikipedia is the greatest textual source there is. The field of computational linguistics began with an early interest in understanding the patterns in data, Parts-of Speech(POS) tagging, easier processing of data for various applications in the banking and finance industries, educational institutions, etc. 

How does Natural Language Processing work?

NLP aims at converting unstructured data into computer-readable language by following attributes of natural language. Machines employ complex algorithms to break down any text content to extract meaningful information from it. The collected data is then used to further teach machines the logics of natural language. 
Natural language processing uses syntactic and semantic analysis to guide machines by identifying and recognising data patterns. It involves the following steps:

• Syntax:  Natural language processing uses various algorithms to follow grammatical rules which are then used to derive meaning out of any kind of text content. Commonly used syntax techniques are lemmatization, morphological segmentation, word segmentation, part-of-speech tagging, parsing, sentence breaking, and stemming.
• 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. Even though semantical analysis has come a long way from its initial binary disposition, there’s still a lot of room for improvement. NER or Named Entity Recognition is one of the primary steps involved in the process which segregates text content into predefined groups. Word sense disambiguation is the next step in the process, and takes care of contextual meaning. Last in the process is Natural language generation which involves using historical databases to derive meaning and convert them into human languages.  

Learn how NLP traces back to Artificial Intelligence. 

Why natural language processing is important?

The amount of data generated by us keep increasing by the day, raising the need for analysing and documenting this data. NLP enables computers to read this data and convey the same in languages humans understand. 
From medical records to recurrent government data, a lot of these data is unstructured. NLP helps computers to put them in proper formats. Once that is done, computers analyse texts and speech to extract meaning. Not only is the process automated, but also near-accurate all the time.  

Why is Advancement in the Field of NLP Necessary? 

NLP is the process of enhancing the capabilities of computers to understand human language. Databases are highly structured forms of data. Internet, on the other hand, is completely unstructured with minimal components of structure in it. In such a case, understanding human language and modelling it is the ultimate goal under NLP. For example, Google Duplex and Alibaba’s voice assistant are on the journey to mastering non-linear conversations. Non-linear conversations are somewhat close to the human’s manner of communication. We talk about cats in the first sentence, suddenly jump to talking tom, and then refer back to the initial topic. The person listening to this understands the jump that takes place. Computers currently lack this capability. 

Prepare for the top Deep Learning interview questions.

What can natural language processing do?

Currently, NLP professionals are in a lot of demand, for the amount of unstructured data available is increasing at a very rapid pace. Underneath this unstructured data lies tons of information that can help companies grow and succeed. For example, monitoring tweet patterns can be used to understand the problems existing in the societies, and it can also be used in times of crisis. Thus, understanding and practicing NLP is surely a guaranteed path to get into the field of machine learning. For beginners, creating a NLP portfolio would highly increase the chances of getting into the field of NLP.

Check out the top NLP interview question and answers.

What are some of the applications of NLP? 

1. Grammarly, Microsoft Word, Google Docs
2. Search engines like DuckDuckGo, Google
3. Voice assistants – Alexa, Siri
4. News feeds- Facebook,Google News
5. Translation systems – Google translate

How to learn Natural Language Processing (NLP)?

To start with, you must have a sound knowledge of programming languages like Python, Keras, NumPy, and more. You should also learn the basics of cleaning text data, manual tokenization, and NLTK tokenization. The next step in the process is picking up the bag-of-words model (with Scikit learn, keras) and more. Understand how the word embedding distribution works and learn how to develop it from scratch using Python. Embedding is an important part of NLP, and embedding layers helps you encode your text properly. After you have picked up embedding, it’s time to lean text classification, followed by dataset review. And you are good to go!
Great Learning offers a Deep Learning certificate program which covers all the major areas of NLP, including Recurrent Neural Networks, Common NLP techniques – Bag of words, POS tagging, tokenization, stop words, Sentiment analysis, Machine translation, Long-short term memory (LSTM), and Word embedding – word2vec, GloVe.

Available Open-Source softwares in NLP Domain

1. NLTK
2. Stanford toolkit
3. Gensim
4. Open NLP

We will understand traditional NLP, a field which was run by the intelligent algorithms that were created to solve various problems. With the advance of deep neural networks, NLP has also taken the same approach to tackle most of the problems today. In this article we will cover traditional algorithms to ensure the fundamentals are understood.
We look at the basic concepts such as regular expressions, text-preprocessing, POS-tagging and parsing.

What are Regular Expressions?

Regular expressions are effective matching of patterns in strings. Patterns are used extensively to get meaningful information from large amounts of unstructured data. There are various regular expressions involved. Let us consider them one by one:

• (a period): All characters except for n are matched
• w: All [a-z A-Z 0-9] characters are matched with this expression
• $: Every expression ends with $
• : used to nullify the speciality of the special character.
• W (upper case W) matches any non-word character.
• s: This expression (lowercase s) matches a single white space character – space, newline,

return, tab, form [nrtf].

• r: This expression is used for a return character.
• S: This expression matches any non-white space character.
• t: This expression performs a tab operation.
• n: Used to express a newline character.
• d: Decimal digit [0-9].
• ^: Used at the start of the string.

What is Text Wrangling?

We will define it as the pre-processing done before obtaining a machine-readable and formatted text from raw data. 
Some of the processes under text wrangling are:

1. text cleansing
2. specific pre-processing
3. tokenization
4. stemming or lemmatization 
5. stop word removal 

Text Cleansing

Text collected from various sources has a lot of noise due to the unstructured nature of the text. Upon parsing of the text from the various data sources, we need to make sense of the unstructured property of the raw data. Therefore, text cleansing is used in the majority of the cleaning to be performed. 

[optin-monster-shortcode id=”ehbz4ezofvc5zq0yt2qj”]

What factors decide the quality and quantity of cleansing?

1. Parsing performance
2. Source of data source
3. External noise

Consider a second case, where we parse a PDF. There could be noisy characters, non ASCII characters, etc. Before proceeding onto the next set of actions, we should remove these to get a clean text to process further. If we are dealing with xml files, we are interested in specific elements of the tree. In the case of databases we manipulate splitters and are interested in specific columns. We will look at splitters in the coming section.

How do we define text cleansing?

In conclusion, processes done with an aim to clean the text and to remove the noise surrounding the text can be termed as text cleansing. Data munging and data wrangling are also used to talk about the same. They are used interchangeably in a similar context.

Sentence splitter

NLP applications require splitting large files of raw text into sentences to get meaningful data. Intuitively, a sentence is the smallest unit of conversation. How do we define something like a sentence for a computer? Before that, why do we need to define this smallest unit? We need it because it simplifies the processing involved. For example, the period can be used as splitting tool, where each period signifies one sentence. To extract dialogues from a paragraph, we search for all the sentences between inverted commas and double-inverted commas. . A typical sentence splitter can be something as simple as splitting the string on (.), to something as complex as a predictive classifier to identify sentence boundaries:

Tokenization

Token is defined as the minimal unit that a machine understands and processes at a time. All the text strings are processed only after they have undergone tokenization, which is the process of splitting the raw strings into meaningful tokens. The task of tokenization is complex due to various factors such as

1. need of the NLP application
2. the complexity of the language itself

For example, in English it can be as simple as choosing only words and numbers through a regular expression. For dravidian languages on the other hand, it is very hard due to vagueness present in the morphological boundaries between words.

What is Stemming?

Stemming is the process of obtaining the root word from the word given. Using efficient and well-generalized rules, all tokens can be cut down to obtain the root word, also known as the stem. Stemming is a purely rule-based process through which we club together variations of the token. For example, the word sit will have variations like sitting and sat. It does not make sense to differentiate between sit and sat in many applications, thus we use stemming to club both grammatical variances to the root of the word. Stemming is in use for its simplicity. But in the case of dravidian languages with many more alphabets, and thus many more permutations of words possible, the possibility of the stemmer identifying all the rules is very low. In such cases we use the lemmatization instead. Lemmatization is a robust, efficient and methodical way of combining grammatical variations to the root of a word.

What are the various types of stemmers?

1. Porter Stemmer
2. Lovins Stemmer
3. Dawson Stemmer
4. Krovetz Stemmer
5. Xerox Stemmer

Porter Stemmer: Porter stemmer makes use of larger number of rules and achieves state-of-the-art accuracies for languages with lesser morphological variations. For complex languages, custom stemmers need to be designed, if necessary. On the contrary, a basic rule-based stemmer, like removing –s/es or -ing or -ed can give you a precision of more than 70 percent .
There exists a family of stemmers known as Snowball stemmers that is used for multiple languages like Dutch, English, French, German, Italian, Portuguese, Romanian, Russian, and so on. 
In modern NLP applications usually stemming as a pre-processing step is excluded as it typically depends on the domain and application of interest. When NLP taggers, like Part of Speech tagger (POS), dependency parser, or NER are used, we should avoid stemming as it modifies the token and thus can result in an unexpected result.

What is lemmatization in NLP?

Lemmatization is a methodical way of converting all the grammatical/inflected forms of the root of the word. Lemmatization makes use of the context and POS tag to determine the inflected form(shortened version) of the word and various normalization rules are applied for each POS tag to get the root word (lemma).
A few questions to ponder about would be.

• What is the difference between Stemming and lemmatization?
• What would the rules be for a rule-based stemmer for your native language?
• Would it be simpler or difficult to do so?

What is Stop Word Removal?

Stop words are the most commonly occurring words, that seldom add weightage and meaning to the sentences. They act as bridges and their job is to ensure that sentences are grammatically correct. It is one of the most commonly used pre-processing steps across various NLP applications. Thus, removing the words that occur commonly in the corpus is the definition of stop-word removal. Majority of the articles and pronouns are classified as stop words. Many tasks like information retrieval and classification are not affected by stop words. Therefore, stop-word removal is not required in such a case. On the contrary, in some NLP applications stop word removal has a major impact. The stop word list for a language is a hand-curated list of words that occur commonly. Stop word lists for most languages are available online. Many ways exist to automatically generate the stop word list. A simple way to obtain the stop word list is to make use of the word’s document frequency. Words presence across the corpus is used as an indicator for classification of stop-words. Research has ascertained that we obtain the optimum set of stop words for a given corpus. NLTK comes with a loaded list for 22 languages.
One should consider answering the following questions.

• How does stop-word removal help?
• What are some of the alternatives for stop-word removal?

What is Rare Word Removal?

Some of the words that are very unique in nature like names, brands, product names, and some of the noise characters also need to be removed for different NLP tasks. Use of names in the case of text classification isn’t a feasible option to use. Even though we know Adolf Hitler is associated with bloodshed, his name is an exception. Usually, names, do not signify the emotion and thus nouns are treated as rare words and replaced by a single token. The rare words are application dependent, and must be chosen uniquely for different applications.

What is Spell Correction?

Finally, spellings should be checked for in the given corpus. The model should not be trained with wrong spellings, as the outputs generated will be wrong. Thus, spelling correction is not a necessity but can be skipped if the spellings don’t matter for the application.
In the next article, we will refer to POS tagging, various parsing techniques and applications of traditional NLP methods. We learned the various pre-processing steps involved and these steps may differ in terms of complexity with a change in the language under consideration. Therefore, understanding the basic structure of the language is the first step involved before starting any NLP project. We need to ensure, we understand the natural language before we can teach the computer.

What is Dependency Parsing?

Dependency parsing is the process of identifying the dependency parse of a sentence to understand the relationship between the “head” words. Dependency parsing helps to establish a syntactic structure for any sentence to understand it better. These types of syntactic structures can be used for analysing the semantic and the syntactic structure of a sentence. That is to say, not only can the parsing tree check the grammar of the sentence, but also its semantic format. The parse tree is the most used syntactic structure and can be generated through parsing algorithms like Earley algorithm, Cocke-Kasami-Younger (CKY) or the Chart parsing algorithm. Each of these algorithms have dynamic programming which is capable of overcoming the ambiguity problems.

Since any given sentence can have more than one dependency parse, assigning the syntactic structure can become quite complex. Multiple parse trees are known as ambiguities which need to be resolved in order for a sentence to gain a clean syntactic structure. The process of choosing a correct parse from a set of multiple parses (where each parse has some probabilities) is known as syntactic disambiguation. 

People in general have no difficulty coping the new words. We can very quickly understand a new word in our language (a neologism) and accept the use of different forms of that new word. This ability must derive in part from the fact that there is a lot of regularity in the word-formation process in our language.

In some aspects the study of the processes whereby new words come into being language like English seems relatively straightforward. This apparent simplicity however masks a number of controversial issues. Despite the disagreement of scholars in the area, there don´t seem to be a regular process involved.

These processes have been at work in the language for some time and many words in daily use today were, at one time, considered barbaric misuses of the language.

What is Coinage?

Coinage is a common process of word-formation in English and it is the invention of totally new terms. The most typical sources are invented trade names for one company´s product which become general terms (without initial capital letters) for any version of that product.

For example: aspirin, nylon, zipper and the more recent examples kleenex, teflon.

This words tend to become everyday words in our language.

What is Borrowing?

Borrowing is one of the most common sources of getting new words in English. That is the taking over of words from other languages. Throughout history the English language has adopted a vast number of loan words from other languages. For example:

• Alcohol (Arabic)
• Boss (Dutch)
• Croissant (French)
• Piano (Italian)
• Pretzel (German)
• Robot (Czech)
• Zebra (Bantu)

Etc…

A special type of borrowing is the loan translation or calque. In this process, there is a direct translation of the elements of a word into the borrowing language. For example: Superman, Loan Translation of Übermensch, German.

What is Compounding?

The combining process of words is technically known as compounding, which is very common in English and German. Obvious English examples would be:

• Bookcase
• Fingerprint
• Sunburn
• Wallpaper
• Textbook
• Wastebasket
• Waterbed

What is Blending?

The combining separate forms to produce a single new term, is also present in the process of blending. Blending, takes only the beginning of one word and joining it to the end of the other word.  For instance, if you wish to refer to the combined effects of smoke and fog, there´s the term smog. The recent phenomenon of fund rising on television that feels like a marathon, is typically called a telethon, and if you´re extremely crazy about video, you may be called a videot.

What is Clipping?

Clipping is the process in which the element of reduction which is noticeable in blending is even more apparent. This occurs when a word of more than one syllable is reduced to a shorter form, often in casual speech. For example, the term gasoline is still in use but the term gas, the clipped form is used more frequently. Examples

• Chem.
• Gym
• Math
• Prof
• Typo

What is Backformation?

Backformation is a very specialized type of reduction process. Typically a word of one type, usually noun, is reduced to form another word of a different type, usually verb. A good example of backformation is the process whereby the noun television first came into ude and then the term televise is created form it.

More examples:

• Donation – Donate
• Option – Opt
• Emotion – Emote
• Enthusiasm – Enthuse
• Babysit – Babysitter

What is Conversion?

Conversion is a change in the function of a word, as for example, when a noun comes to be used as a verb without any reduction. Other labels of this very common process are “category change” and “functional shift”. A number of nouns such as paper, butter, bottle, vacation and so on, can via the process of conversion come to be used as verbs as in the following examples:

• My brother is papering my bedroom.
• Did you buttered this toast?
• We bottled the home brew last night.

What is an Acronym?

Some new words known as acronyms are formed with the initial letters of a set of other words. Examples:

• Compact Disk – CD
• Video Cassette Recorder – VCR
• National Aeronautics and Space Administration – NASA
• The United Nations Educational, Scientific and Cultural Organization – UNESCO
• Personal Identification Number –PIN
• Women against rape – WAR

What is Derivation?

Derivation is the most common word formation process and it accomplished by means of a large number of small bits of the English language which are not usually given separate listings in dictionaries. These small bits are called affixes. Examples:

• Unhappy
• Misrepresent
• Prejudge
• Joyful
• Careless
• Happiness

Prefixes and Suffixes

In the preceding group of words, it should be obvious that some affixes have to be added to the beginning of a word. These are called prefixes: unreliable. The other affix forms are called suffixes and are added at the end of the word: foolishness.

Infixes

One of the characteristics of English words is that any modifications to them occur at the beginning or the end; mix can have something added at the beginning re-mix or at the end, mixes, mixer, but never in the middle, called infixes.

Activities – WORDS AND WORD FORMATION PROCESSES

Activity 1

Identify the word formation process involved in the following sentences:

1. My little cousin wants to be a footballer
2. Rebecca parties every weekend
3. I will have a croissant for breakfast.
4. Does somebody know where is my bra?
5. My family is vacationing in New Zealand
6. I will babysit my little sister this weekend
7. Would you give me your blackberry PIN?
8. She seems really unhappy about her parents’ decision.
9. I always have kleenex in my car.

10.  A carjacking was reported this evening.

(To check your answers, please go to home and check the link: Activities Keyword)

*You may require checking other sources

‘In the beginning was the Word, and the Word was with God, and the Word was God.’
We could all learn something from this well-known Bible verse. Looking beyond the religious overtones, there is a message to be found in this for everyone. Everything begins with a word.

Words consist of vibration and sound. It is these vibrations that create the very reality that surrounds us. Words are the creator; the creator of our universe, our lives, our reality. Without words, a thought can never become a reality. This is something that we have been taught throughout history, as far back as the Bible, which writes of ‘God’ – whatever that word may mean to you – saying ‘let there be light’ and as a result creating light.

So what can we learn from this? If our words and thoughts are the very tools with which we create our reality, then surely they are our most powerful tool yet? Surely we should only pick the very best words in order to create our very best reality?

The Power Of Words And Affirmations

Our thoughts also impact what we manifest in our lives. But it can be argued that the real power lies in our words. It is our words that provide a bold affirmation of our innermost thoughts. They are a confirmation to the world of how we see others, our lives and ourselves. It is this powerful affirmation that our words provide which enables our thoughts to manifest into a reality. So why do we choose to misuse our most powerful asset?

3 Ways To Use Words

1. Choosing Your Words Wisely

As a society, we have become conditioned to talk about our misfortunes and problems. We take our interpretations of events, people and ourselves and communicate them to the world, bringing them into existence.

So by that admission, when we moan or complain about our lives to others, we are putting those negative words out there to become a reality. When you say something out loud enough times your words become the truth not only in your own mind but in the minds of everyone you are saying them too.

If this is really so, then ask yourself – do you really want to tell yourself and everybody that you know that you are unlucky in love, unsuccessful, miserable, bored or whatever else you have been complaining about? Especially now that you know that it is these exact words that are creating the life that you live?

Begin to choose the words that you speak consciously. Practice improved self-awareness over the words that you use to describe yourself and your life. Negative, powerless words such as ‘can’t’, ‘shouldn’t’, ‘need’, ‘won’t’ should all be avoided. They strip you of your ability to manifest a life that you want to live.

As the creator of your universe, what you say goes. Therefore, next time you catch yourself about to use negative words, regain control and frame your word choices so that they have a much more positive impact on your world.

For example, if you would usually say something such as ‘I am unhealthy and overweight’ then why not turn this into a more positive, constructive statement such as ‘I am in the process of becoming healthier and every day I get closer and closer to my ideal weight’.

Your words are the paint with which you paint your reality. Choose those words wisely and positively to create a reality that is good for you.

2. ‘I Am What I Am’

Affirm who you are, your dreams, your hopes and your successes with two of the most powerful words that a person can ever utter – ‘I am’.

These two small but incredibly powerful words should be considered the most precious words that you have in your entire vocabulary. How we end the sentence ‘I am…’ defines who we are to ourselves and to everybody around us. So, when you say ‘I am…fat/lazy/shy’ or ‘I am…beautiful/confident/successful/happy’ this is the exact truth that you are creating for yourself. It doesn’t even matter if there is any truth in the words that you are saying, how you finish those two little words is how you define your reality.

So why not choose a higher expression for yourself? Remind yourself of what you are and what you wish to be by starting each morning with a positive affirmation beginning with those magical words ‘I Am’.

3. Speak From The Heart

When we complain about our lot in life, speak anxiously or use hateful words, we usually do so from a place of fear. So, the first step that you need to take in order to conquer this is to practice better self-awareness over the words that you are using.

Next time you open your mouth to complain or put yourself or others down, ask yourself:

1. ‘Why am I about to say this?’
2. ‘How is this going to serve me or my happiness?’

Ask yourself these two important questions and you will no doubt discover that you are in fact speaking out of fear. This is the fear that you are not good enough, fear that you are in the wrong relationship, the wrong career etc. Most importantly of all, you will realize that by voicing these fears you will be doing nothing for your happiness. Your words can only make you feel worse, manifesting these fears into your life with greater intensity.

So choose your words bravely, consciously and lovingly. Always speak from a place of love; for yourself, for your life and for others. Your words equal your world, so use them wisely.

Learn More About The Power Of Words

If this has made you reconsider your thought patterns and the way you use words, you probably are interested in how affirmations can play a part in your manifestation journey.

Get your FREE Law Of Attraction toolkit which contains a detailed guide to affirmation. It includes affirmation lists AND lots more of manifestation tools.

Click here to download your free copy of our toolkit now!

The typewriter arrived on the scene formally around 1870 and quickly became indispensable the world over for almost all writing tasks. The click-clack of keys was common chatter in the background of many businesses, and students—hoping to craft a “professional-looking” final draft—dropped the pen and instead painstakingly typed their work, with a whiteout wand in hand for mistakes.

Then, in the 1980s, after a brief flirtation with word processors, we were introduced to the now widespread desktop computer. Humongous at first, this impressive machine began replacing typewriters for various tasks, eventually showing up—more reasonably sized—in homes as a bulky desktop or in shiny rows in schools’ computer labs.

But that wasn’t all: a part of the word processing… well, process, was spell-check, a function widely available on mainframe computers in the late 1970s. Far from the whiteout lines of the typewriter era, writers could now type in their words and have the computer suggest a revision. Fast-forward to 2009, when solutions to check the very grammar of a written piece became available and now, in 2020, spell-check and grammar-check are as much a part of the word processing process as hitting save or print.

So: why isn’t plagiarism checking a part of the word processing process, too? Naysayers might say, “But wait! How would students learn how to cite correctly if they are automatic?” Even as we advance in our technology and learning tools, we need instructors who can teach the basics, so students can learn foundational concepts in order to utilize the tools efficiently. Virginia Berninger, a professor emeritus of education at the University of Washington, said it well: “You need a certain level of spelling proficiency to even recognize whether the computer understands your intentions.”

If you consider features built into the computer process as expressions of what a culture values, then you’ll understand why we think that academic integrity is so important, it should be built into the process. Here at Turnitin, that is why we’ve integrated with Microsoft Teams (MSTeams) to make the originality check a little more organic.

The Microsoft Teams integration will enable customers to seamlessly access Turnitin’s similarity checking service right within Teams. Unlimited submissions are automatically available to students, which means they can see their Similarity Score as many times as needed and make revisions, prior to the deadline. Instead of an adversarial, teacher vs. student situation where an incorrect citation causes chaos, the Similarity Check in Microsoft Teams is a formative process, a chance to reflect and redirect as a project unfurls, instead of being a score that is received after the writing process is complete. Even better, this integration will check Microsoft Word, OneNote, PowerPoint and Excel files submitted through Microsoft Teams, which allows students across subject areas to turn in their best, original work.

From typewriters and computers to iPads, spell-check and beyond, we’re excited to be a part of the evolution of the word processing process. And who knows? Maybe sooner than we imagine, automatic plagiarism checking will be as normal to our word processing process as whiteout wands used to be.

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Interested in learning more? Join us at BETT 2020 from January 22 — 25 in London, UK, where we’ll be joined by Rob Whitehead and Rob Lea from Barnsley College. There, they’ll be sharing how our MSTeams integration has helped to make teaching and learning even easier.

Not in the London area? You can still learn about our Microsoft Teams integration with our webinar: Owning Originality: How to Empower Students with Turnitin and Microsoft Teams.

Tuesday, February 4th, at 8 pm PST/Wednesday, February 5th, at 3 pm Melbourne Time

Thursday, February 6th at 8 am PST/4 pm GMT