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NLP: The Subtle Orchestra of Language

· 15 min read
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Series of Articles on AI
This is the second article in a series of four:

  1. LLMs: understanding what they are and how they work.
  2. NLP: a deep dive into the fundamental building blocks of natural language processing (this article).
  3. AI Agents: discovering autonomous artificial intelligences.
  4. Comparison and AI Smarttalk’s positioning: synthesis and perspective.

If language were a symphony, its score would be infinitely complex—sometimes grand, sometimes intimate—driven by the diversity of languages, contexts, and cultural nuances. At the heart of this symphony lies a subtle yet crucial orchestra: NLP (Natural Language Processing), which orchestrates words and meaning in the world of AI.

In the first article, we likened LLMs (Large Language Models) to enormous swarms of bees producing textual honey. Here, we’re returning to fundamental—often more discreet—building blocks that underpin how text is understood and generated in AI. This exploration will help you grasp:

  • The historical roots of NLP
  • The main methods and techniques (statistical, symbolic, neural)
  • The key stages of an NLP pipeline (tokenization, stemming, lemmatization, etc.)
  • The varied applications (semantic analysis, translation, automatic summarization...)
  • The ethical, cultural, and technological challenges
  • How classical NLP coexists with LLMs and what differentiates one from the other

We’ll see that NLP can be viewed as a set of musicians each playing a part: tokenization is the subtle flute, morphological analysis the thoughtful clarinet, syntax dependency the cello grounding the melody, and so on. From this harmony emerges a comprehension (or at least a manipulation) of natural language.

Ready to tune your instruments? Let’s dive into NLP, that subtle orchestra conductor of language.

1. Definition and History: When Language Became (Also) a Matter for Machines​

1.1. Early Steps: Computational Linguistics and Symbolic Approaches​

NLP dates back several decades, long before the advent of powerful LLMs. As early as the 1950s and ’60s, researchers wondered how to make machines process language. The first approaches were mostly symbolic: people tried to manually code grammatical rules, word lists, and ontologies (representing world concepts), among others.

These so-called “knowledge-based” methods rely on the assumption that if you provide enough linguistic rules, the system can analyze and generate text accurately. Unfortunately, human language is so complex that it’s nearly impossible to codify every linguistic nuance in fixed rules.

caution

Example of Linguistic Complexity
In French, the rules of gender for nouns have countless exceptions (e.g., “le poêle” vs. “la poêle,” “le mousse” vs. “la mousse,” etc.). Every rule can spawn new counterexamples, and the list of special cases keeps growing.

1.2. The Statistical Era: When Numbers Were Allowed to Speak​

As computing power progressed, statistical approaches to NLP arose: instead of manually coding rules, the machine infers patterns from annotated data.

For example, you can assemble a corpus of translated texts and learn a probabilistic model that calculates the likelihood that a word in the source language corresponds to a word (or group of words) in the target language. This is how, in the early 2000s, statistical machine translation (such as Google Translate) took off, primarily relying on methods like Hidden Markov Models or aligned phrases.

Gradually, simple count-based methods (word occurrences) and analytic approaches (n-grams, TF-IDF, etc.) proved highly effective for classification or keyword-detection tasks. Researchers discovered that language largely follows statistical patterns, although these are far from explaining everything.

1.3. The Age of Neural Networks: RNN, LSTM, and Transformers​

The 2010s brought large-scale neural models, starting with RNNs (Recurrent Neural Networks), LSTMs (Long Short-Term Memory), and GRUs (Gated Recurrent Units). These architectures enabled better handling of word order and context in a sentence compared to purely statistical approaches.

Then in 2017, the paper “Attention is all you need” introduced Transformers, sparking the wave that led to LLMs (GPT, BERT, etc.). Yet even with this spectacular advance, the fundamental building blocks of NLP still matter: we still talk about tokenization, lemmatization, syntactic analysis, and so on, even if they’re sometimes integrated implicitly into these large models.

2. Key Stages of an NLP Pipeline: The Orchestra in Action​

To better understand the richness of NLP, let’s imagine a classic pipeline where text passes through different stages (different “musicians”):

2.1. Tokenization: The Flute That Provides the Basic Notes​

Tokenization breaks down text into elementary units known as tokens. In languages like French, this often aligns with words separated by spaces or punctuation, though it’s not always straightforward (contractions, embedded punctuation, etc.).

It’s the indispensable first step of any NLP pipeline, because the machine doesn’t “understand” raw character strings. Proper tokenization makes it easier to work with these units of meaning.

2.2. Normalization and Noise Removal​

Once you’ve split the text, you can normalize it (e.g., convert to lowercase), remove unnecessary punctuation or stop words (function words like “the,” “and,” “of,” which don’t always carry meaning).

It’s also at this stage that you address linguistic specifics: handling accents in French, character segmentation in Chinese, and so on. This phase is somewhat like a clarinet clarifying the melody by filtering out extra noise.

2.3. Stemming vs. Lemmatization: The Viola and Violin of Morphological Analysis​

  • Stemming: It trims words down to a “radical” form by removing suffixes. For example, “manger,” “manges,” “mangeons” might become “mang.” It’s fast but imprecise since the radical isn’t always a valid word.
  • Lemmatization: It identifies the canonical form of the word (its lemma), such as “manger” (to eat). It’s more accurate but requires a more elaborate lexicon or linguistic rules.

Both methods help reduce lexical variability and group words sharing the same semantic root. It’s akin to the viola and violin tuning their notes to create a harmonious ensemble.

2.4. Syntactic Analysis (Parsing), Part-of-Speech Tagging (POS Tagging)​

Syntactic analysis identifies a sentence’s structure—for instance, which is the subject, the verb, the object, which are the adverbial clauses, etc. Often referred to as “parsing,” it can be done using dependency systems or constituency trees.

POS tagging assigns each token a grammatical category (noun, verb, adjective, etc.). It’s crucial for deeper understanding: knowing whether “bank” is a noun (a place to sit, in French “banc”) or a verb, for instance, changes how the phrase is interpreted.

2.5. Semantic Analysis, Named Entity Recognition​

Semantic analysis aims to grasp the meaning of words and sentences. This can include sentiment analysis (“Is the text positive, negative, or neutral?”), named entity recognition (people, places, organizations), coreference resolution (knowing which pronoun refers to which noun), and more.

Here the orchestra truly starts to play in harmony: each instrument (step) offers clues about what the text “means” and how its elements connect.

2.6. Final Output: Classification, Summarization, Translation, Generation​

Finally, depending on the task, there can be a variety of final outputs: a label (spam/not spam), a translation, a summary, etc. Each context corresponds to a different “piece,” performed by the NLP orchestra.

Of course, in modern LLMs, many of these steps are integrated or implicitly “learned.” But in practice, for targeted applications, we often still use these modules in a more modular fashion.

3. Main NLP Methods: Symbolic, Statistical, and Neural Scores​

3.1. Symbolic Approaches​

Based on explicit rules, these approaches attempt to model grammar, semantics, and vocabulary. The upside: they can be highly accurate in a narrow domain (e.g., legal contexts with specific coded rules). The downside: they require heavy human effort (linguists and IT experts) and do not generalize well.

3.2. Statistical Approaches​

Here, we estimate probabilities from annotated corpora. For example, the probability that one word follows another or that a string of words belongs to a certain category. Classic examples include n-gram models, HMM (Hidden Markov Models), and CRF (Conditional Random Fields).

These approaches dominated NLP from the 1990s through the 2010s, enabling systems like statistical machine translation and large-scale named entity recognition. They can require substantial amounts of data, but generally are less resource-intensive than the most recent neural methods.

3.3. Neural Approaches​

Thanks to modern computing power, it’s possible to train neural networks on very large corpora. RNNs and especially Transformers (BERT, GPT, etc.) have become the leading edge of current NLP.

These models learn vector representations (embeddings) and capture complex contextual relationships. They automate much of what the “instruments” in the pipeline did: tokenization, syntactic and semantic analysis, and so forth. In practice, we often use a hybrid approach: a pre-trained neural model fine-tuned on a specific task, with possibly symbolic rules on top to avoid certain pitfalls.

4. Key NLP Applications: The Orchestra Serving Humanity​

4.1. Sentiment Analysis and Opinion Monitoring​

Want to know what people think of a product on social media? NLP techniques can classify tweets, posts, and reviews as “positive,” “negative,” or “neutral.” It’s a valuable tool for businesses (marketing, customer relations) and institutions (media monitoring, public opinion surveys).

4.2. Chatbots and Virtual Assistants​

Even before LLMs (like ChatGPT), NLP modules were used to develop chatbots able to answer simple questions using FAQs or predefined scripts. Nowadays, these chatbots can be combined with bigger models for a more fluid conversation feel.

4.3. Automatic Translation and Summarization​

Machine translation has been one of NLP’s major challenges from the start. Today, it mainly relies on neural approaches (NMT – Neural Machine Translation), though statistical methods remain influential.

Likewise, automatic summarization (producing a concise summary of an article, book, etc.) is highly sought after. There are two main types:

  • Extractive Summaries: extracting key sentences
  • Abstractive Summaries: reformulating text in a concise way

4.4. Information Extraction​

In areas like finance, law, or medicine, there’s a need to leverage large volumes of documents to extract key data (numbers, references, diagnoses, etc.). NLP offers tools for named entity recognition, relationship extraction (who is connected to what?), and more.

4.5. Spelling and Grammar Checks​

Whether you’re using a word processor or an online tool, chances are you benefit from NLP modules to detect spelling, grammar, or style errors. This task was once largely symbolic (lists of rules), but it now includes statistical and neural models for greater flexibility.

5. Linguistic, Cultural, and Ethical Challenges: A More Complex Score​

5.1. Multilingualism and Cultural Diversity​

NLP is not limited to English or French. Many languages have very different structures (agglutinative, tonal, or non-alphabetic scripts). Annotated datasets are often scarcer for “rare” or under-resourced languages.

This raises the question of inclusivity: how can we ensure the linguistic richness of the world is represented in models? How do we avoid systematically favoring “dominant” languages?

5.2. Bias and Discrimination​

NLP algorithms, like all algorithms, can inherit biases from their training data. Discriminatory statements, deeply-rooted stereotypes, or representation imbalances may be amplified by such systems.

caution

Example of Bias
A résumé-screening model trained on a company’s historical data might learn a sexist bias if, in the past, the company predominantly hired men for certain positions.

5.3. Privacy and GDPR​

Since NLP deals with language, it potentially applies to emails, private messages, and other personal communications. Privacy is crucial, especially given regulations like GDPR (General Data Protection Regulation) in Europe that impose strict requirements on handling and storing personal data.

5.4. Disinformation and Manipulation​

Advances in NLP, especially coupled with generative models, make it possible to fabricate increasingly credible text. This paves the way for fake news campaigns, propaganda, and more. Thus, there’s a need for detection and verification methods, along with public awareness initiatives.

6. Coexistence and Complementarity with LLMs: A Stellar Duo?​

You might ask, “Now that LLMs are here, why bother with traditional NLP techniques?” The answer is simple: the NLP orchestra remains highly relevant:

  1. Size and Resources: LLMs are huge and computationally heavy. For small local or embedded applications (e.g., on smartphones), lighter models or traditional NLP tools are often preferred.
  2. Interpretability: Classical methods (symbolic parsing, linguistic rules) can sometimes offer better transparency. We can trace why a decision was made, whereas LLMs are more opaque.
  3. Limited Data: In niche fields (e.g., specialized medicine, or a country’s specific legal system), there may not be a massive corpus to train an LLM. Classical approaches can excel here.
  4. Preprocessing, Postprocessing: Even with an LLM, we often need to preprocess or clean data, or post-process the output (for formatting, consistency checks, etc.).

In practice, many companies combine a pre-trained neural model (BERT, GPT, etc.) with more traditional NLP modules. It’s like having a virtuoso soloist for complex passages while keeping the rest of the orchestra for accompaniment and cohesion.

7. Backbone of the Future: Why NLP Will Only Expand​

7.1. Growing Use Cases​

Natural language processing is everywhere: information retrieval, automated responses, content generation, writing assistance, knowledge base management... As text-based data (emails, chats, documents) grows exponentially, NLP is becoming increasingly strategic across industries.

7.2. Multimodality​

We’re moving toward multimodal models that handle text, images, videos, and audio. But text remains a core foundation: the ability to understand and generate language paves the way for interoperability with other modalities (describing an image, subtitling a video, etc.).

Businesses and researchers are increasingly interested in semantic search, i.e., querying a corpus by concepts rather than just keywords. This relies on vectorization and semantic encoding (embeddings), coupled with algorithms for contextual similarity.

7.4. Remaining Challenges​

Even with significant breakthroughs, major challenges remain:

  • Understanding sarcasm, humor, irony
  • Handling high-level logical reasoning and complex inferences
  • Resolving ambiguous meanings tied to context and culture

NLP will therefore continue to evolve, leveraging both algorithmic advances and the richness of linguistic research.

8. How AI Smarttalk Fits In and the Future of AI Agents​

In the next article, we’ll discuss AI Agents—autonomous entities capable of reasoning, planning, and acting in a given environment. You’ll see that they heavily rely on NLP components to understand instructions, formulate responses, and even generate actions.

AI Smarttalk, for its part, aims to position itself as an intelligent yet controlled conversational service, able to draw on LLMs when needed and revert to lighter NLP techniques for specific tasks (classification, question routing, intent detection, etc.).

The idea is to combine the best of both worlds: the raw power of a large model and the precision or reliability of dedicated NLP modules. Essentially, have a complete orchestra (traditional NLP) capable of playing multiple pieces, plus a virtuoso soloist (an LLM) for a lyrical flourish when needed.

9. Practical Tips for Building an NLP Pipeline​

Before concluding, here are some recommendations for those looking to dive into NLP or improve its implementation in their organization.

9.1. Define the Task and the Data​

  • What is your end goal? Sentiment classification, information extraction, translation?
  • What data do you have? Annotated corpora, unannotated data, multilingual data?
  • Which performance criteria matter? Accuracy, recall, response time, interpretability?

9.2. Choose the Right Tools​

There are numerous open-source libraries (spaCy, NLTK, Stanford CoreNLP, etc.) and cloud platforms (turnkey NLP services). LLMs (GPT-like) are often accessible via APIs. Think carefully about constraints (cost, confidentiality, hardware resources needed).

9.3. Focus on Annotation and Evaluation​

Both statistical and neural models need quality data. Investing in precise annotations is vital to achieving good results. You should also set up a proper evaluation protocol (a test set, metrics like F-measure, BLEU score for translation, etc.).

9.4. Monitor and Iterate​

Language evolves, and so do usage patterns. It’s critical to regularly reassess your NLP pipeline, update it with new data, and spot possible drifts or biases that might arise. An NLP system is never truly “done” once deployed.

10. Conclusion: NLP, The Discreet Maestro Preparing AI’s Future​

We’ve just surveyed NLP (Natural Language Processing) in broad strokes. Like an orchestral ensemble, the field unites many instruments (symbolic, statistical, neural) and several types of scores (tokenization, syntactic and semantic analysis). Together, they create the music of machine language, where each note may be a word, a morpheme, or a concept.

Although LLMs have lately dominated headlines with their astonishing performance, NLP remains the foundational infrastructure that enables those large models to exist and perform daily tasks. Without the legacy of parsing, POS tagging, lemmatization, and more, we wouldn’t see today’s accuracy and fluency.

And this is only the beginning: with multimodality, semantic search, and a deeper grasp of humor, cultural contexts, and real-world logic, NLP still has plenty to refine. Ethical considerations, privacy, and regulation will also add complexity, reminding us that this technology can be as potent as it is risky if misused.

tip

Reminder: What’s Next?

  • Article #3: AI Agents, or how NLP and cognitive planning unite to create autonomous systems.
  • Article #4: A global comparison and presentation of AI Smarttalk’s approach, merging the power of LLMs with modular NLP.

All in all, NLP is the discreet conductor—often in the background—tuning the violins and setting the tempo while soloists (LLMs) gather the applause. Without that groundwork, the symphony would never be the same. In the next article, we’ll see how language, once interpreted, can be used by agents to make decisions and act on the world, taking one more step toward ever more autonomous AI.

Until then, take a moment to listen to the “music of language” around you: every word, every sentence, every nuance is the product of a rich construction, and NLP is there to reveal its hidden structure.

Thank you for reading, and see you soon in the third article of this series on AI Agents!