Natural language-enabled AI is fast becoming a necessity rather than a luxury in today’s world. Gartner believes that by 2024, up to 80% of digital brand experiences will be delivered to consumers through virtual personas. That’s what makes embracing the contemporary natural language processing techniques so imperative.
Conversational AI (another flashy term for natural language processing!) extends beyond chatbots. The latest AI systems process natural language to engage in human-like dialogue, understand context, and deliver intelligent responses within milliseconds.
In simple terms, natural language processing is a computer program that understands human language the way it is spoken. Natural language processing changes all the time. New developments in technology and ever-evolving strategies refine the way AI processes language.
What is NLP and How Does It Work?
Natural Language Processing (NLP) is a branch of Artificial Intelligence focused on the relationship between human language and machines, based on the use of natural language.
Thanks to NLP, it is possible to equip computer systems with capabilities to identify the use of natural language. For example, programming a computer to understand, process, and generate language like a person. This is how computational linguistics arises.
Artificial Intelligence includes various technologies that make it easy for machines to develop their intelligence. Combining algorithms makes it possible to create machines with the same capabilities as humans, such as decision-making.
Machine Learning allows the development of algorithms capable of learning and making predictions for machines’ progression and evolution.
Both NLP and Machine Learning are part of AI, and both share techniques, algorithms, and applications. In fact, Chatbots with Artificial Intelligence involve a combination of natural language processing and Machine Learning. What is expressed in writing or orally generates a large amount of data and information.
Humans can communicate and interpret this information through the tone, the structure of a sentence, idioms, and other linguistic elements, and the choice of certain words, expressions, and punctuations.
And machines can understand that complex set of data and information through natural language processing.
How do they do that? Well, by decoding unstructured data! Contemplating not only the language as a succession of symbols but also the hierarchical structure of natural language, that is, sentences or phrases as coherent ideas.
At this point, it’s also essential to understand the concept of NLU, Natural Language Understanding, a subcategory of NLP, which enables machines to understand incoming audio or text. Its counterpart is natural language generation, which allows the computer to react with an answer.
In short, the NLP is not only responsible for finding keywords. It also decodes the meanings and intentions of the words. Technically, it breaks incoming language into small parts that can be processed and analyzed through text and speech vectorization. This vectorization allows the machine to transform language into something it understands and lends meaning to.
Then, the AI provides algorithms to the machine to identify and process the norms of the language. It also uses semantic and syntactic analysis to understand grammatical rules and find the real meaning of communications.
Natural Language Processing Techniques
We have compiled a list of the top 14 natural language processing techniques contributing significantly to the continual betterment of the NLP industry in particular and AI in general. (While you’re at it, you may like to explore the Top 12 Natural Language Processing Applications for Businesses).
1. Tokenization
Tokenization is one of the most basic and straightforward NLP techniques in the natural language process. Tokenization is a vital step in word processing for any NLP application. It requires a long string of text and is divided into smaller units called tokens, which form words, symbols, numbers, etc.
These tokens are building blocks and help understand the context in developing NLP models. Most tokenizers use “empty space” as a stop to create tokens. Depending upon the language and purpose of the model, different tokenization techniques are used in NLP.
The most commonly used are:
- Rule-Based Tokenization
- Spacy Tokenizer
- White Space Tokenization
- Dictionary Based Tokenization
- Subword Tokenization
- Penn Tree Tokenization
Though most of the industry prefers the word tokenization technique in NLP, it basically depends on the goal you set to achieve.
2. Stemming and Lemmatization
Stemming and Lemmatization is another essential NLP technique in the preprocessing pipeline that comes after tokenization. For example, when we search for products on Amazon, we want to see products for the exact word we entered in the search box and other possible word forms around our primary keyword/key phrase. We would likely prefer to see results for products containing the form “sneakers” if we enter “sneaker” in the search field.
In English, similar words are rendered differently depending on the tense they have been used in and their placement in the sentence. For example, words like go, went, and gone have similar meanings, but they are interpreted in the context of the sentence.
The natural language processing techniques like stemming or lemmatization aim to generate the root words from these word variants.
Stemming is very much of a basic heuristic process that strives to accomplish the above-stated objective by chopping off the end of words. (Heuristics is a problem-solving approach aiming to produce a working solution within a reasonable time frame.) The outcome may or may not be a meaningful word.
On the other hand, Lemmatization is a more advanced technique that aims to do things right using vocabulary and morphological word analysis. Removing inflectional endings yields the basic or lexical form of the word, referred to as a lemma.
3. Stop Words Removal
It is the preprocessing step that rightfully follows stemming or lemmatization. In any language, many words are just fillers and have no meaning. These are usually words used to connect sentences (conjunctions – “why,” “and,” “from”) or are used to denote the relationship of words to other words (prepositions – “below,” “above,” “and”).
These words make up most of the human language and are not really useful for the development of NLP models. But the removal of stop words is not the definitive NLP technique applicable for all models. Its implementation varies from task to task.
Let’s assume you want to run text classification on some data (in order to classify text into different categories, i.e., genre classification, auto tag generation, spam filtration).
Under such circumstances, removing stop words from the text comes with benefits, as it helps the model focus on words defining the meaning of the text in that particular dataset.
There might not be any imminent need or benefit of removing stop words for some other tasks like text summarization and machine translation. And experts resort to different methods for eliminating stop words by using libraries like SpaCy, NLTK, and Genism.
4. TF-IDF
TF-IDF is basically a statistical technique that shows how important a word is to a document in a collection of documents. This calculation is taken by multiplying two different values, i.e., term frequency and inverse document frequency.
Term Frequency: It is used to calculate how often a word appears in a document. It’s calculated using the formula given below:
TF (t, d) = the number of t in d/ the total number of words in d
Words that are usually found in documents, such as stop words – “it,” “is,” and “will” are likely to reflect a high term frequency.
Inverse Document Frequency: Before calculating the inverse document frequency, we must first understand the document frequency. In a multi-document corpus, document frequency measures the occurrence of words throughout the document body (N).
DF(t) = the number of t in N documents
This calculation will typically yield a higher number of words commonly used in the English language that we discussed earlier. To conclude, document frequency and inverse document frequency are just the opposite of each other.
IDF (t) = N / Total number of t in N documents
Its primary purpose is to measure the usefulness of the expression in our corpus. Terms such as – biomedicine, genomics, etc. appear only in biology-related documents and have a high IDF.
TF-IDF = Term frequency*Inverse document frequency
The entire idea of TF-IDF is to find the crucial words in a document by finding words having a higher frequency in that particular document but nowhere else in the corpus.
For a computer science-related document, these words can be: computer, data, processor, computational, etc. But for an astronomical document, it would be an alien, galactic, black hole, etc.
5. Text Classification
Text classification serves to organize a large amount of unstructured text (i.e., the raw text data you receive from your customers). Topic modeling, keyword extraction, and sentiment analysis can be considered the subgroups of text classification.
Text classification takes your textual data set and structure for further analysis. It is often used to obtain useful data from customer reviews and customer service slogs.
6. Keyword Extraction
When reading a piece of text, whether on the phone, in a newspaper, or in a book, you do the inadvertent activity of scrolling. It usually results in ignoring complementary words and finding essential words from the text, and everything else fits into the context well enough.
Extracting keywords does exactly the same thing as finding critical keywords in a document. Keyword extraction is an NLP text analysis technique to gain meaningful insight into a topic in no time.
Instead of verifying the document, you can use the keyword extraction technique to condense the text and highlight relevant keywords.
The keyword extraction technique is instrumental in NLP applications, especially when a business wants to identify customer issues based on reviews or if they want to identify topics of interest from a recent report.
Experts resort to several different ways for this purpose. One of them is TF-IDF, as we saw above. You can choose the top 10 words with the highest TF-IDF, and these will be your keywords.
Another method for extracting keywords is to use Gensim, Python’s open-source library.
Keyword extraction can also be implemented with SpaCy, YAKE (Yet Another Keyword Extractor), and Rake-NLTK. It would be best to experiment with these libraries to implement this NLP technique and see which one is best for your application.
7. Word Embeddings
Since machine learning and deep learning algorithms require only numeric input, it is also vital to know how we can convert a block of text into numbers that can be inserted into these models?
When forming a model based on textual data, whether classification or regression, a necessary condition is its conversion into a numerical representation. The answer is simple, follow the word entry method to view the text data. This NLP technique allows you to compile a list of words with similar meanings to get a similar view.
The inclusion of words, also known as vectors, are numerical representations of words in a language. These representations are taught so that words of similar meaning have vectors that are very close to each other. Individual words are represented as vectors or coordinates of an actual value in a predefined vector space with n dimensions.
You can use predefined word input (learn about a vast corpus like Wikipedia) or learn how to insert words from scratch for your own dataset. There are many different types of word insertion, such as GloVe, Word2Vec, TF-IDF, CountVectorizer, BERT, ELMO, and so on.
8. Sentiment Analysis
Also referred to as emotion AI or opinion mining, sentiment analysis is becoming one of the best NLP techniques used for text classification. Its purpose is to categorize a text, such as a tweet, a news article, a book review, or any other type of text on the web, into one of 3 categories: positive/negative/neutral.
The sentiment analysis technique is commonly used to diminish hate speech from social media platforms and identify grieved clients by analyzing negative reviews.
9. Aspect Mining
This NLP technique helps identify various aspects of the text. When applied alongside sentiment analysis, it plays a vital role in extracting thorough information from the text. Part-of-speech tagging is considered one of the easiest ways of aspect mining.
The combination of aspect mining and sentiment analysis on a sample text yields the full intent of the text.
10. Topic Modelling
The subject model is a statistical NLP technique that analyzes a corpus of text documents to find topics stored in them. Best of all, the topic modeling is an unsupervised machine learning algorithm, which means there is no need to label these documents.
This technique allows us to organize and summarize electronic archives on a scale that would not be possible with human annotation.
Latent Dirichlet Allocation is one of the most effective techniques used to model objects. The underlying intuition is that each document has different topics, each distributed over a fixed vocabulary of words.
11. Text Summarization
This NLP technique summarizes text seamlessly, concisely, and accurately. Summarization helps extract useful information from documents without reading them word for word.
When performed by humans, this process takes too much time. The automatic text summarization significantly shortens the time.
Two different types of text summarization techniques currently being used are:
Extraction Based Summarization: In this technique, several key phrases and keywords are pulled out of the document to create a summary (without significantly changing the original text).
Abstraction Based Summarization: This text summarization technique creates new sentences and phrases containing the most useful information from the original document.
The language and structure of the abstract sentence are not the same as the original document since this technique involves paraphrasing. We can also overcome grammatical inconsistencies found in extraction-based methods.
12. Named Entity Recognition
NER is a subfield for obtaining information that searches for and classifies named entities into predefined categories, such as personal names, organization, location, event, date, etc., from an unstructured document.
NER resembles Keyword Extraction, except that the extracted keywords are placed in already defined categories. It is undoubtedly a step ahead of what we do with conventional keyword extraction.
13. Bags of Words (BoW)
You may consider this NLP technique as words in a phrase or text. It first constructs the occurrence matrix for the document, followed by appropriate placement and sequencing of the word or grammar.
This technique compiles the occurrences and frequencies into a classifier specifically designed for the analysis. However, like everything, there are certain advantages and disadvantages to using this technique. In this case, those include the lack of semantic context and meaning.
14. Machine Translation
Machine translation is an important NLP tool. The techniques that fall under the umbrella of machine translation are used to analyze and generate language. Major companies use complex machine translation systems. They play a vital role in modern business.
These tools have been able to break down language barriers globally and allow people worldwide to access foreign sites and communicate with users who speak foreign languages. The machine translation industry generated USD 40 billion last year.
Let’s have a look at some examples of how MT helps businesses:
- Google Translate handles translations of more than 100 billion words every day
- Facebook (now Meta) uses MT to enable the automatic translation of posts/comments
- MT allows eBay to process cross-border transactions and connect buyers and sellers worldwide
- Microsoft pioneers state-of-the-art machine translation mechanisms powered by artificial intelligence aimed at helping Android and iOS users access convenient translations
Conclusion
In general, NLP is still in its primitive phase. There are thousands of critical linguistic details and complications that need to be addressed. But with significant investments in areas like human feature engineering, experts have high hopes of coping with independent machine learning issues at substantial rates.
And before we sign off, we thought going through these 4 Key Benefits of Natural Language Processing (NLP) for Businesses would give you a better idea of how you might take advantage of this emerging technology for your business gains.
