Well, human beings are the most advanced species on earth. Our success as human beings is because of our ability to communicate and share information. Now that’s where the concept of developing a language comes in.
The Human Language
When we talk about the human language, It is one of the most diverse and complex parts of us, considering a total of 6500 languages that exist.
So coming to the 21st century, According to the industry estimates, only 21 percent of the available data is present in the structured form. By 2025, IDG projects that there will be 163 zettabytes of data in the world, and estimates indicate that 80% of this data is unstructured. Data is being generated as we speak, tweet, and send messages on WhatsApp or Facebook. The majority of the data exist in the textual form which is highly unstructured in nature. Now in order to produce significant and actionable insights from this data, it is important to get acquainted with the techniques of Text Analysis and Natural Language Processing.
Text Mining and NLP
So let’s understand what is text analysis, also known as text mining and natural language processing.
Text mining/Text Analytics is the process of deriving meaningful information from natural language text. It usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluating and interpreting the output.
Text mining refers to the process of deriving high-quality information from the text. The overall goal is here to essentially turn the text into data analysis via the application of natural language processing, that is why text mining and NLP goes hand-in-hand.
Applications of NLP
So let’s understand some of the applications of text mining or natural language processing.
So one of the first and the most important applications of natural language processing is sentiment analysis. Be it, Twitter Sentiment Analysis, or the Facebook Sentiment Analysis, as it’s being used heavily.
We have the implementation of a chatbot, you might have used the customer chat services provided by various companies and the process behind all of that, is because of the Natural Language Processing.
We have Speech recognition and here we are also talking about personal assistants like Siri, Google Assistant, and Cortana, and the process behind all of this is because of natural language processing.
Machine Translation is also another use case of natural language processing and the most common example for it is the Google Translate which uses NLP to translate data from one language to another and that too in real-time.
One of the coolest applications of natural language processing is advertisement matching. It is basically a recommendation of ads based on your history.
Components of NLP
NLP is divided into two major components that are the natural language understanding (NLU) and natural language generation (NLG). The understanding generally refers to mapping the given input into natural language into useful representation and analyzing those aspects of the language whereas generation is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation.
The natural language understanding is usually harder than the natural language generation because it takes a lot of time and a lot of things to usually understand a particularly especially if you are not a human being.
There are various steps involved in the natural language processing which are tokenization, stemming, lemmatization, the POS tags, named entity recognition, and chunking.
Tokenization is the process of operating strings into tokens which in turn are small structures or units that can be used for tokenization. If we have a look at the example here, taking this sentence into consideration it can be divided into seven tokens. This is very useful in the natural language processing part.
The second process in natural language processing is Stemming. It usually refers to normalizing the words into their base or the root form. If we have a look at the words here, we have Affection, Affects, Affections, Affected, Affections, and Affecting. All of these words originate from a single root word and as you might have guessed it is “Affect”. The stemming algorithm works by cutting off the end or the beginning of the word. Taking into account a list of common prefixes, suffixes that can be found in an infected word. This indiscriminate cutting can be successful on some occasions but not always.
Let’s understand the concept of lemmatization. Lemmatization, on the other hand, takes into consideration the morphological analysis of the word. To do so it is necessary to have a detailed dictionary that the algorithm can look through, to link the form back to its original word or the root word which is also known as the lemma. It groups together different inflected forms of the word called lemma and are somehow similar to stemming, as it mapped several words into one common root but the major difference between stemming and lemmatization is that the output of the lemmatization is a proper word. for example, a lemmatizer should map the word gone, going, and went in to go that will not be the output for stemming now.
4. POS Tags
Once we have the tokens and once we have divided the tokens into its root form next comes to the POS tags. Generally speaking, the grammatical type of the word is referred to as POS tags or the paths of speech. Be it the verb, noun, adjective, or adverb article and many more. It indicates how a word functions in meaning as well as grammatically within the sentence. A word can have more than one part of speech based on the context in which it is used. For example, let’s take the sentence who is the CEO of Google. Here Google is used as a verb although it’s a proper noun. These are some of the limitations or I should say the problems that occur while processing the natural language.
5. Named Entity Recognition
To overcome all of these challenges we have the Named Entity Recognition also known as NER. It is the process of detecting the named entities such as the person name, the company names we have, the quantities, or the location. It has three steps which are the noun phrase identification, the phrase classification, and entity disambiguation. So if you look at this particular example here, Google CEO Sundar Pichai introduced the new pixel 3 at new york central mall. As you can see here Google is identified as an organization, Sundar Pichai as a person, we have new york as a location, and the central mall is also defined as an organization.
Now once we have divided the sentences into tokens, done the stemming, the Lemmatization, added the tags as the Named Entity Recognition, it’s time for us to group it back together and make sense out of it. For that, we have chunking.
Chunking basically means picking up individual pieces of information and grouping them together into the bigger pieces. These bigger pieces are also known as chunks in the context of NLP. Chunking means a grouping of words or tokens into chunks. As you can see here we have pink as an adjective, Panther as a noun, and D as a determiner, and all of these are together chunked into a noun phrase. This helps in getting insights and meaningful information from the given text.
You might be wondering where does one execute or run all of these programs and all of this function on a given text file. For that Python came up with an NLTK.
What is NLTK
NLTK is the natural language toolkit library that is heavily used for all Natural Language Processing and text analysis.
Natural Language Processing plays a critical role in supporting machine-human interactions.
As more research is being carried in this field, we expect to see more breakthroughs that will make machines smarter at recognizing and understanding the human language.
Have you used any NLP technique in enhancing the functionality of your application?
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