Natural Language Processing in plain English

You might have heard about the large language models and the significant impact they have in our daily lives.

But have you ever wondered how your smartphone voice assistant understands your commands, or how your email platform filters out spam messages?

The magic behind these functionalities is a field of AI called Natural Language Processing (NLP). In this article, we’ll delve into what NLP is, the type of AI it represents, its methods, and the importance of syntax and semantic analysis.

Unveiling the concept: What is NLP in AI?

NLP, or Natural Language Processing, is a component of AI concerned with enabling computers to understand and process human language. Think of it as a bridge between machines and humans, facilitating human-like comprehension of languages for computers. It’s not just about understanding text or spoken words in a superficial way but understanding them in a way similar to how humans do.

By harnessing the power of computational linguistics, machine learning, and deep learning models, NLP allows computers to ‘understand’ the full essence of human language, encompassing the speaker or writer’s intent and sentiment. In other words, NLP empowers computers to translate text, respond to spoken commands, and even summarize large volumes of text rapidly, a task that could be laborious for humans.

While NLP is familiar in consumer products like voice-operated GPS systems and digital assistants, its influence is far-reaching, playing a significant role in business solutions that streamline operations and simplify mission-critical business processes.

Identifying the type of AI: Where does NLP fit in the AI spectrum?

AI is a broad field encompassing several areas, one of which is NLP. To classify, NLP falls under the category of symbolic AI, a type of AI that focuses on duplicating human intelligence through symbols and rules.

However, with the integration of machine learning and deep learning techniques, NLP also aligns with the realm of statistical AI, a type of AI that learns from experience and can improve its understanding over time.

Breaking it down: The 5 key phases of NLP

To understand how NLP works, it’s crucial to know the key phases or methods involved. Each phase contributes to the overall capacity of a system to ‘understand’ language as humans do.

1. Lexical analysis: The first step in NLP is lexical analysis. It involves converting a sequence of characters (like a sentence) into a series of tokens or words. Lexical analysis is key in segmenting text, making it ready for deeper processing.
2. Syntactic analysis: Also known as parsing, this phase deals with analyzing the string of symbols or words according to the rules of formal grammar. Here, grammatical rules are applied to word groups and categories, contributing to the understanding of the grammatical meaning of any sentence.
3. Semantic analysis: As the name suggests, this phase focuses on understanding the meaning of the text. It captures the meaning while considering context, logical structuring of sentences, and grammar roles. It attempts to understand the intended meaning of spoken or written language.
4. Discourse analysis: This phase involves understanding the underlying meaning of a spoken or written text by considering its social and historical contexts. It’s about interpreting the text and understanding social interactions.
5. Pragmatic analysis: The final step of NLP, pragmatic analysis is about understanding the true meaning of the text based on context and interpretation. It’s about understanding communicative and social context in language.

Diving deeper: The 7 levels of NLP

Beyond the 5 key phases, NLP encompasses seven levels of linguistic analysis to achieve its goal.

These are:

1. phonetics & phonology (the study of physical sounds)
2. morphology (word formation)
3. syntax (sentence formation)
4. semantics (meaning)
5. pragmatics (contextual meaning)
6. discourse (text structure)
7. world knowledge (general knowledge).

These levels provide a more detailed breakdown of how NLP systems analyze and understand language.

The crucial role of syntax and semantic analysis in NLP

Syntactic and semantic analyses are critical in NLP, aiding the machine in understanding language in a human-like manner.

Syntactic analysis, also known as parsing, breaks down a string of symbols or words conforming to the rules of formal grammar. It’s like diagramming a sentence, identifying the parts of speech, and understanding how they interact.

Semantic analysis, on the other hand, deals with the meaning of language. It goes beyond recognizing words and reaches into interpreting intent, resolving context and word ambiguity. This helps an NLP system grasp the subtleties of language, such as irony, humor, and metaphors.

Together, syntax and semantic analysis form the heart of NLP, making it possible for machines to understand, process, and generate human language effectively.

The unending possibilities: Interesting uses of NLP

NLP, due to its versatility and broad-reaching implications, has numerous applications across various sectors. Here are some interesting and impactful uses of NLP:

1. Customer service: NLP can supercharge customer service by powering chatbots and virtual assistants. These AI-driven agents can understand customer inquiries, respond effectively, and even predict customer needs based on previous interactions. This results in efficient and personalized customer service, available 24/7.
2. Marketing analytics: In the field of marketing, NLP can be a game-changer. It enables marketers to analyze customer sentiment and feedback across various platforms, identifying trends and consumer needs. This insight can be instrumental in shaping marketing strategies, improving targeted advertising, and increasing customer satisfaction.
3. Healthcare: The healthcare industry is another area where NLP shines. By extracting information from electronic medical records, NLP can assist with diagnosis, improve patient outcomes, and provide insights for research. Moreover, NLP can power AI chatbots to answer patient queries, helping them understand their health conditions and treatment options.
4. Finance and trading: NLP algorithms can analyze financial news, reports, market sentiment, and other data to inform investment decisions. Automated trading systems can use this data to make real-time decisions, taking into account factors that human traders may miss.
5. Human resources: NLP can revolutionize the hiring process by automating resume screening, saving HR personnel significant time. It can analyze the data in resumes and job descriptions to match the best candidates for a job.
6. Legal industry: In the legal world, NLP can streamline processes such as contract review and legal research. It can analyze large volumes of legal documents to extract relevant information, identify patterns, and even predict legal outcomes based on historical data.
7. Education and eLearning: NLP can personalize learning by understanding a student’s strengths and weaknesses. It can also power tutoring systems, offering personalized feedback to students, and grading open-ended assignments.
8. Language translation and localization: NLP, combined with machine learning, can provide real-time language translation, breaking down language barriers and facilitating global communication.

Conclusion: The future of NLP and its impact on our lives

The applications of NLP are vast, from enhancing customer service through chatbots to extracting valuable insights from vast volumes of textual data. With the growing investment in AI and the rapid advancements in machine learning, we can expect NLP’s capabilities to expand even further.

The evolution of NLP towards Natural Language Understanding (NLU), a field focusing on understanding the nuances and context of language, hints at a future where our interactions with machines become more seamless and intuitive.

In this journey of continuous evolution and refinement, NLP holds immense promise, signifying a future where human-machine communication becomes as natural as human-to-human conversations. With its profound impacts on various sectors, NLP is indeed a transformative aspect of AI that is reshaping our world.