Machine Understanding of Natural Language

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Prismata is a research initiative aimed at developing a conceptual framework for Artificial General Intelligence (AGI) based on principles of self-organization, proprioceptive feedback, and emergent sentience. It utilizes a novel "Promethean Language" — a self-referential, dynamically evolving communication system — to facilitate internal and external interaction, which allows AI to adapt, learn, and potentially achieve a form of autonomous self-improvement. This framework integrates principles of self-organization with the Prometheus language to advance artificial intelligence by enhancing adaptability, communication, and decision-making. Traditional AI models are constrained by static programming, while this framework introduces a self-referential intelligence architecture that continuously restructures in response to internal and external stimuli. Prometheus, as an emergent cognitive system, enables AI to refine its interaction models, develop autonomous reasoning, and engage in iterative self-enhancement.

Gujarat is one of the states in the western part of India, and the Gujarati language is the official language of Gujarat. The Gujarati language is more than 700 years old and is spoken by more than 55 million people around the world. A machine translation system (MTS) is needed for the communication between people knowing different languages. Idioms are used in almost all the languages. An idiom is a phrase or an expression whose meaning does not necessarily relate to the literal meaning of its individual words. The idiom generally means something different than what is directly conveyed by its individual words. Translation of idioms for any language, as with Gujarati idioms, into any other language is a challenging task. All existing MTS including Google Translate and Microsoft Bing fail to translate Gujarati idioms suitably. In the current paper, a particular category of Gujarati idioms, called the personage idioms, has been treated for detection from the input text and translation into the English language. We have deployed a dictionary-based algorithm and context-based search, respectively, for idioms having one and multiple meanings. This is a first of its kind work in the world. From a broad technical view point, this is an application of Gujarati to interlingual translation for the MTS sub-domain of natural language processing (NLP). The interlingual language is considered to be English for the present research work.

The aim of this work is to explore new methodologies on Semantic Parsing for unrestricted texts. Our approach follows the current trends in Information Extraction (IE) and is based on the application of a verbal subcategorization lexicon (LEXPIR) by means of complex pattern recognition techniques. LEXPIR is framed on the theoretical model of the verbal subcategorization developed in the Pirapides project.

This article considers the remaining hindrances for natural language processing technologies in achieving open and natural (human-like) interaction between humans and computers. Although artificially intelligent (AI) systems have been making great strides in this field, particularly with the development of deep learning architectures that carry surface-level statistical methods to greater levels of sophistication, these systems are yet incapable of deep semantic analysis, reliable translation, and generating rich answers to open-ended questions. I consider how the process may be facilitated from our side, first, by altering some of our existing language conventions (which may occur naturally) if we are to proceed with statistical approaches, and secondly, by considering possibilities in using a formalised artificial language as an auxiliary medium, as it may avoid many of the inherent ambiguities and irregularities that make natural language difficult to process using rule-based methods. As current systems have been predominantly English-based, I argue that a formal auxiliary language would not only be a simpler and more reliable medium for computer processing, but may also offer a more neutral, easy-to-learn lingua franca for uniting people from different linguistic backgrounds with none necessarily having the upper hand.

This study will introduce a tool - termed "NLPytaly" - for the automatic treatment of naturally-occurring texts in Italian. The tool distinguishes constructs with an ordinary verb from those with a support verb. Meaning is rendered by using cognate (i.e. etymologically related) semantic roles (CSR), which differ from other roles because they are expressed with the content morpheme of the predicate licensing arguments. CSRs offer a number of advantages: they can be semi-automatically derived and use the who-does-what model. Besides, they facilitate the detection of anaphoric chains and produce a foreground/ background opposition of the named entities. Finally, they permit the construction of a chronology of narrated events.
Keywords: computational linguistics, Natural Language Understanding, semantic roles, non-verbal predication, automatic text summarization

Construction of machine understanding is definitely the challenge. There are several technologies used widely. Currently mainstream applications uses machine operatable knowledge bases, for example Wolfram Alpha[?] to support simple dialogue and operate devices. Newer the less those approaches do not create machine understanding of even primitive incidents. We tried new approach based on assumption that human understanding is tightly coupled with human thinking itself. We used thinking model described in Marvin Minsky book "The emotion machine" [?].