Introduction
Intelligent solutions offered by Alexa, Siri, Google Assistant, and several other virtual assistants are now indispensable parts of our lives, enabling us to get through everyday manual tasks. These simplest forms of artificial intelligence use Natural Language Processing (NLP) and Machine Learning (ML) algorithms to understand, process, and provide relevant outputs.
Advanced research in NLP has led us to Generative AI that can generate images and texts from input prompts. These ground-breaking advancements find their application is improving the efficiencies of enterprises by streamlining and automating business operations while also boosting agent productivity. Â
However, the challenges of NLP are a significant area for consideration, as human languages are complex and inconsistent. Let us understand them in a little more detail in this article.
What is Natural Language Processing (NLP)?
Natural Language Processing is a sub-field of computer science and artificial intelligence that enables computers to comprehend and interact with human language through text or voice using statistical analysis of machine learning.
The development of NLP has allowed computers to interact in a natural human tone filled with empathy and emotions. It opens up new possibilities for communication and applications.
How does Natural Language Processing work?
NLP combines a large number of techniques to interpret human language. It uses computational statistics, machine learning, and deep learning for processing language. It uses these techniques to overcome any challenges of NLP that it may encounter while processing any human language. Computational linguistics, a branch of linguistics in computer science, is applied to the analysis and understanding of spoken and written language. Â
Computational linguistics comprises semantical analysis and syntactical analysis as its two primary categories of analysis. By analyzing word syntax and using preprogrammed grammar rules syntactical analysis ascertains the meaning of a word, phrase, or sentence. Semantical analysis interprets word meaning inside the sentence structure using the syntactic output.
Word parsing done in computational linguistics can be of two kinds. A constituency parsing creates a parse tree (or syntax tree), a rooted and ordered representation of the syntactic structure of the phrase or string of words. On the other hand, dependency parsing examines the links between words, such as recognizing nouns and verbs.
Speech recognition and language translators use the parse trees that are produced. This analysis should ideally make the output as text or speech, comprehensible to humans and NLP algorithms.
Modern artificial intelligence (AI) models train using vast volumes of labeled data, so self-supervised learning (SSL), in particular, helps support NLP. As data labeling requires hours of time-consuming annotations, consolidating enough datasets to train AI can be impractical. In such cases, self-supervised learning techniques are much more economical and time-efficient.
Major 3 methods of NLP
Rules-based Natural Language Processing
The first NLP applications were highly straightforward with their if-then decision approach that needed rules to preprogram. Such an approach allowed responses to only specific prompts. Rule-based NLP has been unscalable and limited in application due to a lack of machine-learning approaches.
Statistical Natural Language Processing (NLP)
It automatically extracts, categorizes, and labels text and voice input elements and then gives potential interpretation of those elements a statistical likelihood. This technique uses machine learning for complex linguistics breakdown, such as part-of-speech tagging.
Statistical NLP established the critical approach of mapping language elements—such as words and grammar rules—to a vector representation, allowing languages to model using mathematical (statistical) methods such as regression or Markov models.
Deep Learning NLP
Deep learning models have recently emerged as the primary form of NLP, utilizing massive amounts of raw unstructured data as text and voice—to achieve ever greater accuracy. Deep learning is an extension of statistical NLP, except that it employs neural network models.
NLP techniques
There might be several challenges of NLP that may slow down its evolution. However, for human language processing, NLP uses several techniques. Some of them are as follows;
Part-of-speech tagging : It is also known as grammatical tagging. It involves identifying and marking words in a sentence to a corresponding part of speech, such as nouns, verbs, adjectives, adverbs, etc.
Word sense ambiguity: It involves identifying the meaning of a word among multiple possibilities as per the context. For example, word sense disambiguation helps to interpret the meaning of “plant” in the following two phrases: “I water my plants every day” and “Every worker is at huge personal risk while working in a power plant”.
Coreference resolution : An NLP application figures if and when two words refer to the same entity. The best for this technique is to identify if the pronoun refers to a person, object, or animal.
Speech recognition : This technique involves converting a spoken language into text. Speech recognition can pose a challenge for NLP programs as people tend to have varied forms of pronunciation and speed of their speech, with different emphases on words.
Sentiment analysis: Sentiment analysis seeks to extract subjective traits such as attitudes, emotions, sarcasm, bewilderment, or mistrust from written text.
Summarization: It involves summarizing long text that generally includes reports and articles into meaningful content for time-sensitive users.
Tokenization: In tokenization, words or subwords are categorized into “tokens” that a program may analyze. Tokenization is the foundation for many NLP activities, including word modeling, vocabulary construction, and frequent word occurrence.
Must read article: What is Generative AI? How It May Help To Grow Your Business (kenyt.ai)
Benefits of Natural Language Processing
Despite facing several challenges in NLP, this subset of artificial intelligence has several benefits. Leading reports indicate that the global market for the NLP is to grow at a CAGR of 29.3%, leading to a value of $18.9 billion in 2023 to $68.1 billion in 2028. Whether to summarize a long text or to translate a language into another, NLP benefits businesses and individual users. Some of the benefits of NLP are;
Which of the following are the major challenges of natural language processing?
Although NLP provides us with innumerable benefits and applications, it is not without some significant challenges of NLP. These challenges can be a hindrance to its further development. All of the challenges mentioned in this section majorly affect its progress toward natural conversation; Â
Understanding synonyms
For an NLP application, understanding and identifying synonyms might present problems as humans are very natural at conveying the same idea with several words. Moreover, while several of these terms may have the same meaning, others may have varying complexities. Various people employ synonyms to indicate somewhat different meanings within their lexicon as per their understanding and learning.
Therefore, it is crucial to include all conceivable meanings and synonyms of a word while developing NLP systems. While text analysis algorithms are not infallible, their comprehension of synonyms will improve as the volume of training data increases.
Vocabulary
Human language is constantly evolving, with the invention and consistent addition of new terms in the spoken vocabulary. With evolving societies, slang words come into existence apart from formal terms. It may cause the NLP to generate inaccurate text responses, posing a challenge for the NLP application and its further development. Â
Tone of speech
While speaking, humans tend to pronounce words differently or stress differently on various words, which changes the meaning of a word or sentence under specific contexts. Under such conditions, an NLP system might fail to understand the context or miss the sarcasm, making it unreliable in various applications.
Ambiguity
Human communication is with sentences that may have two or more meanings, depending upon the context. There can be lexical, semantic, or syntactic ambiguity. Without the ability to understand in what context a phrase has been spoken, there are challenges in NLP systems to correctly and coherently generate human conversation in a natural tone.
Languages with limited speakers
A big focus of AI machine learning NLP applications has been on the most popular languages. However, several regional languages globally may vary in dialects, which don’t have much documented records to train the NLP system. We risk losing the efficiency of an NLP system when it comes to languages with fewer users.
Nevertheless, new methods, such as multilingual sentence embeddings and multilingual transformers, seek to recognize and capitalize on the universal similarities that exist between languages.
Industry-specific vocabularies
Different industries and businesses frequently use specific words to indicate relevant contexts. For example, an NLP processing model required for processing financial documents can be substantially unique from one needed for education. Many tools train for particular fields, allowing specialized industries to develop or train their models.
Understanding colloquialisms
It is common for humans to use informal words specific to regions and idioms, which may pose a challenge in NLP application development meant for widespread use. Since colloquialisms are informal language, they might not even have a “dictionary definition”, and their meanings might vary depending on where you live. Furthermore, new terms appear daily as cultural slang is evolving and growing.   Â
Textual errors
Misused or misspelled words can be a problem for text analysis. While there are systems to autocorrect and do grammar checks, it doesn’t always capture the exact context behind the text.
Mispronunciations, accents, stutters, and other spoken language can be challenging for a machine to comprehend. These problems can be reduced with language database expansion and personal users educating intelligent assistants. Â
Overcoming the challenges in NLP
There may be challenges in the NLP that hinder its widespread usage in various applications. However, there are ways to overcome those challenges. One of the methods that can improve NLP is training the application in large amounts of data. It will train the NLP application to remove bias and improve its accuracy in interpreting human language.
Additionally, there are efforts to enhance the NLP systems with the application of deep learning and neural networks. It will expand the abilities of the NLP for a better understanding of the context and improve its accuracy in human languages.
Furthermore, there is a need for training these NLP systems on more contextual knowledge and data to overcome the challenges of NLP. It ensures that the NLP program has a better grasp on the disambiguate of human languages and can improve its understanding.
For better outcomes with NLP applications, developing them with advanced algorithms is yet another way to enable the NLP programs to understand complex sentence formations and grammar specific to a language.
Use cases of Natural Language Processing (NLP)
As NLP is the driving force behind AI, it finds many applications in enterprises for their communication operations, customer interactions, data analysis, etc. Here are the few use cases of NLP;
Customer engagement
NLP applications in enterprise AI chatbots and virtual assistants are game-changers in improving customer communications. They are significant in responding to customer queries instantly and engaging in conversations with a natural human tone, building stronger customer relationships.Â
This advanced automation is useful in reducing operational costs, manual repetitive work, and improved productivity with accuracy.
Similarly, there are new-age voice bots that are capable of speech recognition. They recognize the pattern in speech and process the input commands using advanced NLP to generate a relevant output in natural human language. Kenyt is one of the leaders in conversational AI chatbots and voice bots that are taking the market by storm with their sophisticated applications. It is a trendsetter to showcase the efficient ways to overcome the challenges for NLP for maximum impact. Â
Frequently Asked Questions
Most customers prefer to interact with someone to find a resolution for their questions. For such cases, a specifically designed chatbot to help customers with their FAQs is highly relevant. The virtual agent can interact with the customer naturally as a human to understand their query.
Most common customer queries are of simple types like the who/what/when/where form. A virtual assistant capable of using an NLP program can provide relevant answers. It allows human agents to be free of repetitive tasks and to focus more on complex scenarios.
Content generation
With the help of natural language processing (NLP), computers can now produce text and speech that sounds authentic and natural enough to pass for human speech. One could use the generated language to create first drafts of tweets, memos, blogs, computer code, and letters. The resulting language quality from an enterprise-grade system may be good enough for real-time use in chatbots, virtual assistants, and autocomplete features.
Advancements in NLP are leading us towards improved generative AI programs that are significant for overcoming the challenges in NLP, opening up further intricate possibilities. Generative AI tools can streamline and improve everyday tasks for enterprise operations or regular personal activity.
Sentiment analysis
An NLP model can rapidly analyze incoming text for keywords and phrases to determine a customer’s mood in real-time as positive, neutral, or negative after being trained on language relevant to a business or industry. How an incoming communication is received can influence how it is handled.
Additionally, NLP can find use to examine call center records or consumer comments, so the incoming communication need not be real-time.
Data translation
As globalization allows us to interact with diverse languages, voice, and text translation is becoming integral in everyday enterprise or professional operations. A translation is not only about rephrasing or replacing the words in one language with another.
A meaningful translation involves understanding the tone, context, and emotions of one language that is then translated into another without missing the original meaning.
With the advancements in NLP programs, data translation tools are becoming increasingly accurate and proficient. It is also relevant to showcase how we can overcome the challenges of NLP.
Kenyt’s AI chatbots are resourceful in assisting customers and employees in their productivity. They can help agents translate large volumes of data and summarize the context for time-sensitive consumption.
Grammar checks
Grammar rules can be applied in word processing or other applications where the natural language processing (NLP) function is trained to identify improper grammar and recommend better wordings.
Conclusion
NLP has emerged as a significant branch of artificial intelligence that is revolutionizing how we communicate and interact with each other. It has presented us with its widespread applications in various scenarios, improving accuracy and efficiency.
Even though there are a few challenges in NLP, there are numerous ways to overcome them. We can realize the full potential of natural language processing by adding more data, improving NLP systems with deep learning and neural networks, and tackling the problems of ambiguity, context, and rare cases.
Frequently Asked Questions
Machine translation, named entity recognition, sentiment analysis, and text categorization are few applications of NLP.
Natural Language Processing (NLP) Challenges
1. Understanding synonyms
2.Vocabulary
3. Tone of speech
4. Ambiguity
5. Languages with limited speakers
6. Industry-specific vocabularies
7. Understanding colloquialisms
8. Textual errors
All of the challenges mentioned above majorly affect the progress of NLP toward achieving complete fluency and independent understanding of a language. However, some methods may allow NLP to overcome these challenges, such as training on large volumes of data, contextual information, and advanced algorithms.   Â
The four types of NLP are;
1. Natural Language Understanding (NLU): This branch of NLP focuses on machines interpreting and understanding the meaning of human language. The functions of NLU involve machine translation, speech recognition, sentiment analysis, and answering questions.
2. Natural Language Generation (NLG): This branch of NLP deals with computers producing human-like text. Its applications are chatbots, text summarization, and machine writing.
3. Natural Language Processing (NLP): This refers to the entire field of computer science concerned with the interaction between computers and human language. NLP encompasses both NLU and NLG.
4. Natural Language Interaction (NLI): This type of NLP focuses on how computers and humans interact with each other through natural language. It includes virtual assistants and interactive voice response (IVR) systems.
Data translation involves understanding the tone of the phrase, context, and emotions for meaningfully conveying the original information from one language to another. One of the biggest challenges that NLP faces while machine translation is the lack of contextual understanding of text.
NLP has a bright future thanks to developments in deep learning and neural networks, which will increase efficiency and accuracy even more. Further, NLP’s interaction with other technologies will open up intriguing new applications.
About the Author
Nisha Sneha
Nisha Sneha is a passionate content writer with 5 years of experience creating impactful content for SAAS products, new-age technologies, and software applications. Currently, she is contributing to Kenyt.AI by crafting engaging content for its readers. Creating captivating content that provides accurate information about the latest advancements in science and technology has been at the core of her creativity.
In addition to writing, she enjoys gardening, reading, and swimming as hobbies.
