Natural language processing (NLP), a subset of artificial intelligence (AI), focuses on equipping computers with the capacity to comprehend text and spoken language.
NLP unites computational languages, incorporating rule-based human language modelling, with statistical machine learning and deep learning approaches. These methodologies empower computers to handle human language in both written and spoken forms. That is, comprehending not just the words but also the underlying purpose and emotional tone of the speaker or writer.
This article delves into the inner workings of NLP, exploring how it uncovers the relationships within language, real-world examples, techniques and the power of NLP in this digital age.
Why does NLP matter?
In retrospect, NLP has become a vital aspect of our daily lives. Its applications have extended into diverse fields such as retail (like customer service chatbots) and medicine (interpreting or summarising electronic health records).
Popular conversational agents like Amazon’s Alexa and Apple’s Siri leverage NLP to comprehend user queries and respond. The most advanced agents, exemplified by GPT-3, are now available for commercial use and can produce sophisticated text across a wide range of topics while empowering chatbots to engage in conversations. Leading companies like Google utilise NLP to enhance search engine results, and social platforms like Facebook use it to identify and filter out hate speech.
How does NLP work?
NLP models operate by uncovering connections among the fundamental elements of language. This includes letters, words, and sentences within a text dataset. NLP architectures employ diverse techniques for various things and they include:
1. Data preprocessing
Data preprocessing is a crucial step in preparing text for model processing, enhancing model performance and converting words and characters into a format understandable by the model.
Several techniques are involved in this data preprocessing, and they include:
- Stemming and Lemmatisation: Stemming simplifies words to their base forms using heuristic rules. For instance, “university,” “universities,” and “university” could all be reduced to the base form “universe” (though it may not distinguish between “universe”). Lemmatisation is a more formal method that finds word roots by analyzing word morphology using a dictionary.
- Sentence Segmentation: This divides a lengthy text into linguistically meaningful sentence units. In English, this is often evident as sentences are separated by periods, but it can be complex when dealing with abbreviations or languages like ancient Chinese without clear sentence markers.
- Stop Word Removal: This process targets the removal of commonly occurring, low-information words like “the,” “a,” “an,” and others.
- Tokenisation: Tokenisation splits text into individual words or word fragments, producing a word index and tokenized text. Words are often represented as numerical tokens for use in various deep-learning methods. Using methods that instruct language models to disregard unimportant tokens can enhance efficiency.
2. Feature Extraction
Feature extraction is a fundamental step in many traditional machine-learning techniques, it involves converting text into numerical features that describe a document within its corpus. Some of these features are:
- Bag-of-Words: Bag-of-Words tallies the occurrences of each word or n-gram (a combination of n words) in a document.
- TF-IDF (Term Frequency-Inverse Document Frequency): In contrast, TF-IDF assigns weights to words based on their importance, considering two key factors; Term Frequency (TF): this measures how significant a word is within the document and is calculated as follows: TF (word in a document) = Number of occurrences of that word in the document / Number of words in the document. Secondly, Inverse Document Frequency (IDF): assesses how important a term is across the entire corpus and is calculated as IDF (word in a corpus) = log (number of documents in the corpus/number of documents that include the word).
3. Modelling
Following data preprocessing, the next step involves using NLP architecture to model the data for various tasks. The numerical features obtained through techniques like TF-IDF or Bag-of-Words can be fed into different models, depending on the specific task.
Deep neural networks, in particular, often work directly with raw text data and do not require extracted features. However, it’s still possible to use TF-IDF or Bag-of-Words features as inputs if needed.
Techniques of Natural Language Processing (NLP)
1. Sentiment Analysis
Sentiment analysis involves the analysis of data, which can be in the form of text or voice. This is done to determine whether it expresses a positive, neutral, or negative sentiment.
This process is valuable as it can convert extensive collections of customer feedback, reviews, or social media responses into actionable, quantifiable insights. These results can subsequently be scrutinised to gain customer insights and formulate strategic decisions.
2. Named Entity Recognition
Named Entity Recognition (NER) is an important Natural Language Processing technique that identifies and tags “named entities” within the text, allowing for their extraction and further analysis.
NER essentially labels these entities, which can be organization names, people, proper nouns, locations, and more, while also tracking how frequently they appear in a dataset. The frequency with which specific entities appear in customer feedback can signal areas that need improvement, hence, helping to address vain points.
In reviews and searches, it can reveal preferences for particular product types, enabling you to personalise each customer’s journey and enhance their overall experience. The potential applications of NER are limited only by the creativity and innovation of your feedback and content team, making it a versatile tool for understanding and responding to customer needs.
Some of its uses are:
Text Summary:
Text summarization is the process of simplifying complex jargon, whether it’s scientific, medical, technical, or any other specialized field, using natural language processing techniques. The goal is to make the content more accessible and understandable.
While human languages can be intricate, text summarisation software can effectively distil complex language into a concise and comprehensible output by employing fundamental noun-verb linking algorithms.
Topic modelling:
Topic modelling is an unsupervised Natural Language Processing technique that leverages artificial intelligence algorithms to identify and categorize clusters of text that revolve around common subjects. It’s akin to keyword tagging but focuses on extracting and categorising essential words related to specific topics and the associated information clusters.
Text classification:
Text classification involves the structuring of extensive unstructured text data. This refers to the raw textual information you receive from customers. It encompasses various subtasks like topic modelling, sentiment analysis, and keyword extraction.
Text classification plays a pivotal role in organising your text dataset for subsequent analysis. It is frequently applied to extract valuable insights from customer reviews and customer service logs, aiding in the mining of useful data from these sources.
Keyword Extraction:
Keyword extraction is the final key to the text analysis puzzle that encompasses the techniques we’ve discussed so far. It involves the automated extraction of the most pertinent information from text using AI and machine learning algorithms. You can configure your software to identify keywords that are specifically relevant to your particular requirements, allowing you to adapt the process to your needs. Feel free to explore this with our sample keyword extractor to see its practical applications.
Real-World Examples of Natural Language Processing
1. Email Filters
Once an algorithm is effectively trained on email text data, it becomes capable of accurately identifying, categorizing, and labelling emails as regular, spam, or malicious. Malicious emails are typically intercepted and removed from your account before reaching your inbox.
Additionally, email providers often implement other filters, such as those for social or promotional emails, to help users manage their email content more efficiently.
2. Language Translation
Traditionally, translating sentences from one language to another often yielded confusing or even offensive results. Leading to doubts about the possibility of accurate translations. However, thanks to advancements in AI and NLP, algorithms can now be trained on text in various languages, thereby, allowing for the generation of equivalent meanings in different languages. This technology has even extended its capabilities to languages like Russian and Chinese, which present translation challenges due to their unique alphabet structures and the use of characters instead of letters.
3. Smart assistance
Smart assistants, such as Siri, Alexa, and Cortana, are among the most widely recognized applications of NLP. Most importantly, they have become increasingly integrated into our daily lives. These applications are designed to respond to commands issued through both voice and text. They can answer questions using their connected knowledge bases and, in some cases, carry out tasks on connected “smart” devices.
To function effectively, these applications employ a range of AI technologies. NLP dissects language into parts of speech, word stems, and other linguistic features.
Natural language understanding (NLU) enables machines to comprehend human language, while natural language generation (NLG) empowers machines to communicate effectively. These components work together to deliver the desired responses and interactions with users.
4. Chatbots
The primary objective of a chatbot is to supply users with the information they require, precisely when they need it, all while reducing the necessity for human intervention.
A chatbot system leverages AI technology to interact with users in natural language. This is similar to how a person would communicate when speaking or writing. This interaction typically occurs through messaging applications, websites, or mobile apps.
Artificial intelligence and NLP technologies enable these systems to comprehend a user’s request using their own words and engaging in a conversational dialogue. NLP-powered chatbots, which can be considered a variation of smart assistants, respond to textual input from users. How the system interprets the user’s request relies on the specific AI approach being used.
For most businesses, chatbots serve as a primary communication channel on their websites or apps, offering continuous customer support, particularly for FAQs. This ensures a convenient and always-available means of assisting users.
5. Personalized CX
In today’s business landscape, the abundance of data, advanced analytics, and increasingly sophisticated technologies provide companies with an unprecedented wealth of information to establish meaningful connections with customers.
Moreover, this level of personalisation has become the standard for customer engagement, aligning with the expectations of individuals when they interact with the companies they do business with.
Conclusion
In conclusion, Natural Language Processing (NLP) plays a key role in the power of language and text to advance various applications. From sentiment analysis to chatbots, NLP technologies transform the way we interact with data and systems. By making accurate language translation, chatbots, and smart assistants a reality, there are new possibilities for communication and customer support.
Researched By Olukotun Joan
