17th-18th Century

The 18th century saw several notable advancements in linguistics, philosophy, and the conceptualization of computational thinking that indirectly contributed to the eventual development of Natural Language Processing (NLP). These advancements built on earlier ideas, such as Leibniz’s vision of a universal language, and laid the groundwork for computational linguistics and the formal study of semantics.

1. Mechanical Language Devices and Leibniz’s Universal Language

Leibniz’s Universal Language (17th Century, Influential in the 18th Century)

Gottfried Wilhelm Leibniz proposed the idea of a lingua characteristica universalis, a universal symbolic language capable of representing all human knowledge.

Key Elements:

• Symbolic Representation: Leibniz imagined using symbols to encode ideas, much like programming languages and formal grammars do today.

• Logical Calculus: His dream was to create a computational system (calculus ratiocinator) to process this universal language, allowing reasoning and the resolution of disputes.

• Influence on NLP: The idea of encoding knowledge symbolically foreshadowed semantic representation in NLP, where information is structured for machine interpretation.

18th-Century Legacy of Leibniz’s Ideas

Leibniz’s vision inspired later thinkers like George Boole (Boolean logic) and Charles Babbage (mechanical computation), whose work directly influenced computational linguistics and symbolic reasoning in NLP.

2. Advances in Linguistics and Philology

Philosophical Grammar

18th-century linguists, particularly in Europe, shifted focus toward identifying universal principles underlying all human languages.

Key Figures:

• James Harris (1709–1780): In Hermes: A Philosophical Inquiry Concerning Universal Grammar, Harris explored how languages share common grammatical structures. This idea influenced Noam Chomsky’s transformational grammar, a cornerstone of NLP.

• Johann Gottfried Herder (1744–1803): Herder’s work emphasized the role of language in shaping thought, foreshadowing the Sapir-Whorf hypothesis and the importance of context in NLP.

Standardization of Dictionaries

The 18th century witnessed significant efforts to codify and standardize languages, providing essential resources for later computational linguistics:

• Samuel Johnson’s Dictionary of the English Language (1755): A monumental achievement in lexicography, it cataloged English words with definitions and usage examples.

• Example in NLP:

  Early dictionaries provided the groundwork for word embedding models, which rely on lexical resources to map words to meanings.

Historical and Comparative Linguistics

Scholars began systematically comparing languages to understand their origins and relationships:

• Sir William Jones (1746–1794): Discovered the Indo-European language family, demonstrating systematic relationships between languages. His work laid the foundation for understanding syntactic and morphological structures, essential in NLP for multilingual systems.

3. Emergence of Logical Systems

The Port-Royal Grammar

Developed by Antoine Arnauld and Claude Lancelot in the late 17th century but widely influential into the 18th century, the Grammaire générale et raisonnée (General and Rational Grammar) presented language as a system of logic and reasoning.

• Key Concepts:

  • Universal structures underpin human languages.

  • Language is a reflection of thought.

• Example in NLP:

  The emphasis on universal structures and logical relations prefigures formal language theories used in computational syntax and semantics.

David Hume’s Philosophical Contributions

Scottish philosopher David Hume’s work on associationism — how ideas are linked in the mind—provided insights into how words and meanings are connected:

• Influence on NLP:

  Associationism relates to modern vector space models, where words are represented by their contextual associations (e.g., Word2Vec).

4. Automata and Computational Devices

Jacques de Vaucanson’s Automata

French inventor Jacques de Vaucanson created intricate mechanical devices, including a duck that mimicked real-life actions (e.g., eating, digesting).

• Though not language-related, these automata represented early attempts to emulate complex human behaviors mechanically, a precursor to machine interaction with natural language.

Wolfgang von Kempelen’s Speaking Machine

Von Kempelen (1734–1804) created one of the earliest speech synthesis devices, capable of producing basic vowel and consonant sounds.

• Significance for NLP:

  This device marked the beginning of machine interaction with spoken language, paving the way for modern speech synthesis and voice-based NLP systems.

5. Development of Cryptography and Statistical Analysis

Advancements in Cryptography

18th-century cryptographers refined methods for encoding and decoding messages, including frequency analysis.

• Example in NLP:

  The use of statistical techniques in cryptography influenced probabilistic language models used in NLP, such as n-grams.

Statistical Foundations

Mathematicians like Pierre-Simon Laplace developed probability theory, which became essential for statistical NLP.

• Example:

  Bayesian inference, derived from this period, is widely used in spam filtering and sentiment analysis.

6. Exploration of Machine Learning Precursors

Tabula Rasa and Machine Learning

Philosophers like John Locke popularized the tabula rasa (blank slate) theory, which suggested that knowledge comes from experience.

• Influence on NLP:

  This idea parallels machine learning, where systems "learn" from data rather than being pre-programmed with rules.

7. Early Ideas on Universal Communication

Esperanto Precursors

Although Esperanto itself was developed in the 19th century, the 18th century saw growing interest in creating a universal language to bridge communication gaps.

• Influence on NLP:

  The aspiration for universal languages foreshadowed efforts in machine translation and cross-lingual NLP.

Conclusion

The 18th century set the stage for NLP by advancing linguistic theory, logical reasoning, and the mechanical emulation of human behavior. Key influences include:

• The idea of universal languages and symbolic representation (Leibniz).

• Philosophical grammar and the search for universal linguistic principles.

• Early speech synthesis devices and mechanical automata.

• Foundational work in statistics and probability.

These developments provided the intellectual and technical foundations for the computational approaches that would emerge in the 19th and 20th centuries.