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Have you ever thought about how computers understand human language?
The answer lies in a special secret: NLP.
Most of the NLP models talk to us fluently, all thanks to careful text refinement.
Were going to talk about the crucial steps that NLP takes in this article.
Well talk about things like punctuation, special characters, stopwords, stemming, and even lemmatization.
From breaking down sentences to fixing spelling mistakes were here to make these ideas easy to understand.
Each part of our article will show you the hidden work that makes NLP shine.
Get ready to learn how text cleaning and preprocessing make NLP work its wonders in ways you never imagined.
What is NLP?
Natural Language Processing(NLP)is like combining computer science, human language, and artificial intelligence together.
This helps computers talk to us better and feel more like real conversations.
This special area has led to cool things liketranslating languages, helpful chatbots, and understanding feelings in text.
Its where technology and language meet.
This process ensures that analyses are based on meaningful content, free from distracting or inconsequential elements.
One part of this process is cleaning out special characters and punctuation.
These are the not-normal letters and symbols, like smiley faces or foreign letters.
They also include those dots, commas, and marks that help sentences look organized.
Heres why this matters These characters and marks can make reading words tricky.
They can confuse machines that make a run at understand words or figure out what they mean.
Since NLP aims to uncover meaningful insights from text data, removing stopwords is crucial.
To enhance the effectiveness of NLP tasks, various methods are used to identify and remove stopwords.
One common approach involves utilizing predefined lists of stopwords that are specific to a language.
These lists contain words that are generally considered to have little semantic value.
Lets move forward towards Spelling correction!
Spell checking plays a crucial role in text cleaning by identifying and correcting spelling mistakes in written content.
Inaccurate spelling can create confusion and negatively impact the credibility of the text.
Automated spell-checking ensures that text is error-free and communicates the intended message effectively.
Several techniques are employed for automatic spell correction.
One common approach is using pre-built dictionaries or language models that contain a list of correctly spelled words.
Encoding means transforming the data into a representation that is suitable for processing or analysis.
Normalizing means converting the data into a standard or consistent form easier to compare or manipulate.
It aids in recognizing numerical and date data within text.
(As mentioned previously)
Parsing: Analyzing text structure and meaning using grammar and logic.
It clarifies the context of numerical and date data, resolving ambiguity.
Conversion: Altering numerical and date formats for consistency.
It standardizes information using a common system.
Extraction: Identifying and isolating numerical and date data through patterns or rules.
It captures relevant information for analysis or processing.
This code will extract numerical values using regular expressions.
We are using dateutil library to extract data:
Such that we can extract numerical entities from our text.
Expanding them back is important for NLP systems.
It helps clean up the text, making it easier to understand and avoiding any mix-ups or confusion.
We can add or remove abbreviations and contractions as per our requirements.
HTML tags are codes that format web pages, influencing how content is displayed.
They can complicate text analysis by introducing noise and altering structure.
However, they offer semantic cues helpful for tasks like summarization.
HTML Parser: Accurately understands tags but might be slower and more intricate.
Web Scraper: Easily fetches plain text from web pages, but availability can be limited.
With the help of BeautifulSoup, you will fetch the content from that website.
After web scrapping, you will remove HTML tags using regular expressions.
Now, lets move toward Text Preprocessing.
Text Preprocessing
In any NLP project, the initial task is text preprocessing.
Preprocessing involves organizing input text into a consistent and analyzable format.
This step is essential for creating a remarkable NLP program.
There are many open-source tools available to carry out the tokenization process.
Tokenization
Tokenization serves as the initial phase in any NLP process and significantly influences the entire pipeline.
By employing a tokenizer, unstructured data, and natural language text are segmented into manageable fragments.
These fragments, known astokens, can be treated as distinct components.
In a document, the frequency of tokens can be harnessed to create a vector that represents the document.
Tokens possess the potential to directly instruct computers to initiate valuable actions and responses.
Alternatively, they can function as attributes in a machine learning sequence, sparking more intricate decisions or behaviors.
Tokenization involves the division of text into sentences, words, characters, or subwords.
When we segment the text into sentences, its referred to as sentence tokenization.
On the other hand, if we break it down into words, its known as word tokenization.
Its like cutting a sentence into pieces wherever theres a gap.
While this approach is straightforward, it might not handle punctuation or special cases effectively.
Example of WhiteSpace Tokenization:
Natural language processing is amazing!
[Natural, language, processing, is, amazing!].
Regular expression tokenization involves using patterns to define where to split text into tokens.
This allows for more precise tokenization, handling punctuation, and special cases better than simple whitespace splitting.
Example of Regular Expression Tokenization:
Email me atjack.sparrow@blackpearl.com.
This approach focuses on preserving punctuation marks as separate tokens.
Its particularly useful when maintaining the meaning of punctuation is crucial, such as in sentiment analysis.
Subword tokenization breaks words into smaller meaningful units, such as syllables or parts of words.
Its especially helpful for languages with complex word structures.
[Wow, !, This, is, incredible, .]
This is particularly useful for handlingrareor out-of-vocabulary words.
Treebank tokenization employs predefined rules based on linguistic conventions to tokenize text.
It considers factors like contractions and hyphenated words.
Example: I cant believe its August 23rd!
[I, can, t, believe, it, s, August, 23rd, !
So, these are the types of Tokenization.
Now, we will move toward Standardizing text.
This is useful in things like analyzing language, where we use computers to understand words and sentences.
When we do this, were like tidying up the text so that computers can understand it better.
Doing this helps to make the text more consistent and removes confusion.
We dont want the computer to treat them as different words just because of the capital letter.
Its like being fair to all the words!
But there are times when we dont follow this rule.
Sometimes, big capital letters are important.
Also, if someone writes in BIG letters, like STOP!
or SOS the meaning is different or they might showing their emotions.
Another thing to know is that different writing styles exist.
Sometimes, in computer code, words arejoinedLikeThisorseparated_by_underscores.
In short, standardizing text case is like giving text a nice haircut so computers can understand it.
But remember, there are special cases when we break the rule for good reasons.
This code checks if two words, Apple and apple, are the same by converting words to lowercase.
If they are, itll print theyre the same; otherwise, it prints theyre different.
Lets move toward normalization.
Normalization
Normalization is the process where tokens convert into their base form.
In normalization, the inflection is removed from the word to obtain its base form.
Different forms of normalization are used to address specific challenges in text processing.
Before diving into stemming, lets get familiar with the term stem.
Think of word stems as the basic form of a word.
When we add extra parts to them, its calledinflection, and thats how we make new words.
Stem words are those words that remain after removing prefixes and suffixes from a word.
Sometimes, stemming may produce words that are not in the dictionary or without meaning.
Therefore, stemming is not as good as a lemmatization technique in various tasks.
There are many other libraries to do the same.
Lemmatization converts words intomeaningfulbase forms.
Lemmatization is a way of changing a word to its basic or normal form, called thelemma.
Unlike cutting off word endings, lemmatization tries to choose the right normal form depending on the situation.
Now, time to wrap up!
Conclusion
In this article, weve explored the basics of Natural Language Processing (NLP).
We covered removing special characters, tokenizing text, and more.
We also understood concepts like normalization, stemming, lemmatization, and handling numbers and dates.
Additionally, we got a glimpse of dealing with HTML content.
However, our journey continues.
Theres a whole world of advanced text-cleaning methods to discover.
Well dive into Part-of-Speech tagging, explore different tools and libraries, and dive into exciting NLP projects.
This article is just the start; be ready for Part 2, where more knowledge awaits you.
If you would like to learn Natural Language Processing, here are some of the best NLP courses.
