The human reality, although seemingly in constant chaos of unplanned changes, has been organized in certain aspects of human life over the centuries to facilitate the process of meeting people's needs. Acquiring food, striving for comfort and safety, and the desire to push the boundaries of knowledge were mechanisms essential for the development and spread of larger social groups and the creation of systems. Population migrations, changes in internal policies, and social phenomena have influenced the emergence from the Indo-European root of many language groups from which modern nations and their unique languages have developed. However, for positive cooperation in this linguistic evolution to be possible between different individuals or distinct groups differing from each other in terms of ethnicity, psychology, or individual adaptive skills, it was necessary to analyze how to create meanings and maintain a record of the developed linguistic system. Using cognitive processes and available tools, attempts were made to acquire knowledge about objects, surrounding phenomena, and the complex processes of the natural and mechanized worlds. To do this, one had to use the tool of speech effectively.

Communication as a social process between people forced the creation of abstract meanings in the mind of primitive humans. The tool at first might not have been perfect - it required effort and time to improve. Language and its inherent progress have changed the economy. It have been resulted in the production of paper and writing instruments, followed by the production of books, and eventually the implementation of advanced communication technologies. Humans gained an advantage in a world full of dangerous animals and changing natural conditions.
Would we ever have been able to create settlements, fortresses, cities, or kingdoms, or finally entire civilizations, without the development of a system necessary for communication? Regardless of the attempt to answer this question, it can be assumed that the removal of the developed systems of communication between people would introduce unprecedented chaos.
It would have catastrophic effects on the destruction of trade, transportation, educational opportunities, and it would even effectively impact the simplest daily activities. However, in order to use and transmit verbal or written messages over long distances, it was necessary to establish standardized rules for the functioning of the language system. It is difficult to closely examine the features of all languages due to the volume of material, although the Industrial Revolution and ubiquitous computerization have significantly facilitated the analysis of language and languages. The computer as a new invention for solving computational problems, initially had difficulty understanding the language system due to its operation in the binary system. Computers had difficulty with prosodic elements that are natural for humans, such as emphasizing irony or intonation in speech. Linguists, in their continuous search, have tried to identify absolute universals for all languages, that is, statements that are true for all languages regardless of their roots (e.g., all languages are characterized by the existence of a structure suitable for the formation of negation).

Did primitive man seek contact because it was a natural instinct embedded in the nature of his functioning, or not necessarily, and he was initially driven by the evolutionary instinct of survival, hence the desire for contact with the stranger?

The process of learning languages as designed systems essential for communication and understanding the world seems undeniably burdened by the compulsion to learn throughout life and compare the past with the present. In the grand search of linguistics, absolute universals as statements true for all languages are not common due to the linguistic diversity in the world. The process of settling territories and acquiring new lands has influenced the emergence of new language families and the mutual interaction of languages with each other (e.g., a huge number of words in the English language are borrowed from French). In Europe, the most significant role was played by the family of Indo-European languages, from which smaller groups of Baltic-Slavic, Celtic, Germanic, etc. languages emerged. The differences between various languages were influenced by political, historical, and social events. Throughout history, from 3500 BC to the present day, the language systems of the Indo-European family have developed so drastically that some nations, despite even close proximity and a shared ancient origin, have adopted such different scripts or phonetics within a few years that conversations between users are completely incomprehensible without prior intensive study (e.g., differences in the form of a different alphabet or different stress on similar words).

If it has been possible to teach a machine the meanings of individual words and sentences in an input-output format for translations from one language to another, what about the bigger picture of conversation? After all, even speakers and users of the same language find it difficult to understand each other. Understanding also depends on group affiliation, psycho-social conditions, and environmental influences. On the path of syntactic and semantic searches, pragmatics of language enters, bringing with it a context adorned with complex metaphors.
Natural language processing was supposed to enable machines to process, interpret, or generate meanings from language systems. Due to the fact that the process of communication is extremely complex and requires taking into account many nuances of human speech, NLP has developed various methods and techniques to teach machines language comprehension. NLP algorithms not only perform semantic analysis, which is the understanding of words or sentences in context, but sometimes they must extract the most important information from the thicket of these meanings, such as specific proper names or dates. Moreover, automatic machine translation, tokenization (breaking down text into smaller units), sentiment analysis (recognition of emotional tone), and the use of recurrent neural networks (RNN) require processor computational power. In the challenge of text generation, a special architecture designed for sequential processing helps.

Chatbots and dialogue systems: how they use semantic analysis to better understand questions and answers.

The tactic of taking small steps towards the goal, the algorithm applied in NLP must first perform a lexical analysis where the text is broken down into smaller elements – tokens. In linguistics, terms such as lemmatization, synonymization, and ontologization can be found, which as semantic tools facilitate the creation of semantic trees. A semantic tree graphically illustrates the relationships between different concepts and meanings in natural language. Tools from the humanities are essential for their creation.
The lemma as the base form of a word facilitates word analysis by setting aside the issue of grammatical inflection (grow -> growed, growing -> wine-growing). This process is called lemmatization in linguistics. However, synonymization facilitates the finding of words with similar meanings by grouping them close to each other in a semantic tree and making it easier for the machine to classify them. Another semantic tool for NLP is ontologization. Ontologies are collections of concepts and the relationships between them that define the hierarchy and structure of knowledge in a given domain. Ontologies provide ready-made structures that can be embedded in semantic trees to reflect the relationships between concepts. For example, crocus – flower – flora. Thanks to ontologization, machines receive not only a ready-made dictionary of thematically related words necessary for processing, but also general knowledge about the organization of smaller concepts into a larger context.

A semantic tree graphically depicts the relationships between different concepts and meanings in natural language.

Thanks to semantic trees, chatbots handle homonyms and polysemy, which are words with multiple meanings, better.
And what is the difference between polysemy and homonymy? In homonymy, words have the same pronunciation but different spelling, and their meanings can be completely different (French mer/mère). In polysemy, the same word has multiple meanings. Semantic analysis helps distinguish the meaning of a word depending on the context. Semantic trees enable machines to navigate such differences, making interactions with users more accurate, especially when it comes to conversations with a doctor or seeking legal assistance.
Importantly, dialogue systems can track previous statements in a conversation to better understand current questions. For example, if the user previously mentioned planning a trip, the chatbot can use this information later in the conversation by suggesting transportation options or places to visit. This increases the fluidity of the conversation and raises the level of satisfaction with the solution to the problem in question.
When it comes to emotions, chatbots can use sentiment analysis, which is also based on semantic analysis, to recognize whether the user is expressing positive, neutral, or negative emotions. Semantic trees help recognize the emotional charge of words, such as "happy" or "angry," and respond accordingly. Thanks to this, dialogue systems are able to adjust their responses to the mood of the interlocutor, for example, by offering support when they detect negative emotions. In the face of the advancing digitization of professions that previously relied solely on human contact, some consulting tasks that do not require complex process mapping can be transferred to chatbots.

For creating content that users care about, machine translations of various languages, business-oriented chatbots, and dialogue systems, language models are also essential, and their combo with semantic trees brings remarkable results. For natural language processing, a bidirectional model like BERT is used, which understands the context of words by analyzing the preceding and following vocabulary in a sentence. It is precisely through such analysis of the structure of entire sentences and text classification that the Chatbot engages in conversations on a variety of topics.

In educational systems, such as intelligent tutors, language models can use semantic trees to create better-tailored responses and explanations. Trees help in presenting students with the relationships between different topics, supporting the teaching process. These systems can explain complex topics by utilizing the relationships between concepts, which helps students understand the material in a broader context. The duet of linguists and programmers creates immense possibilities for developing more complex yet precise tools for natural language processing. Linguists provide knowledge about linguistic structures and analytical tools, while programmers implement this knowledge into the advanced AI models they design.
In the future, intellectual work will be significantly more efficient, profound, and will bring about significant changes in the needs that will drive human action and evolution. If only we could keep this drifting ship in check with mental maturity.