Sanskrit language analysis and its equivalence with techniques used in applications of AI
Sanskrit is unique and advanced because, unlike other languages, it does not work using a noun-phrase model. In Indian analysis sentence expresses an action that is conveyed by verb and set of auxiliaries. The verbal action is represented by the root of the verbal form, the auxiliary activities by nominal (noun, adjectives, etc.) and their case endings.
Meaning of a verb in Sanskrit is Vyapara (Action) + Phala (Result)
In general, describes an action, state or occurrence However, Sanskrit is architected in such a way that the sentence provides not only the action but other details as well, such as tense, quality of the agent involved (singular, double, plural) and the degree of the agent (first, second, third).
For example, "Gramam Gacchati Chaitra" (Chaitra is going to village). "An act of going taking place in the present of which the agent is no one other than Chaitra qualified by singularity and here the object something not different from village."
"John gave the ball to Mary." This sentence has verbal meaning "to give" but has many auxiliary activities, such as John holding the ball, an act of movement starting from John, an act of giving, an act of receiving, etc. It is important for one to know where to stop the splits. While defining the verb Sanskrit clarifies that the name "action" cannot be applied to a solitary point reached by extreme sub-division. In these types of sentences, auxiliary activities become subordinated to the main sentence meaning. These auxiliary activities will be represented by case endings in Sanskrit. There are seven types of case endings in Sanskrit six of which are definable representations of auxiliary activities (agent, object, instrument, recipient, point of departure, and locality); the seventh is genitive, which is not represented by the other six.
The case endings are explained using the sentences below as examples:
"Out of friendship, Maitra cooks rice for Devadatta in a pot, over a fire."
Here the total process of cooking is rendered by the verb form "cooks" as well as a number of auxiliary actions:
- An agent represented by the person Maitra
- An object by "rice"
- An instrument by "fire"
- A recipient by the person Devadatta
- A point of departure (which includes the causal relationship) by “friendship" (which is between Maitra and Devadatta)
- The locality by “pot"
Rick Briggs gives another example to show how Sanskrit sentence formation is detailed when compared to English. Consider the below sentence
"Because of the wind, a leaf falls from a tree to the ground." Wind is the instrument bringing leaf. Tree is the point of departure. Ground is the locality. Leaf is the agent.
When we consider the same sentence in accordance with English the above sentence can be written as "The wind blows a leaf from the tree." Here, wind becomes the agent and leaf will be considered as object. This sentence is transitive, whereas the earlier one was intransitive.
In the final section, the author tries to establish an equivalence between Sanskrit and techniques used in AI (semantic nets). Both these systems rely to a significant degree on specification, which is crucial in understanding the real meaning of the sentence to the extent that it allows inferences to be made about facts not explicitly stated in the sentence.
For example: "Out of friendship, Maitra cooks rice for Devadatta in a pot over a fire." This sentence, when represented in semantic nets, will have triples as shown below.
- cause, event, friendship
- friendship, objectl, Devadatta
- friendship, object2, Maitra
- cause, result, cook
- cook, agent, Maitra
- cook, recipient, Devadatta
- cook, instrument, fire
- cook, object, rice
- cook, on-lot, pot.
- cook, agent, Maitra
- cook, object, rice
- cook, instrument, fire
- cook, recipient, Devadatta
- cook, because-of, friendship
- friendship, Maitra, Devadatta
- cook, locality, pot
Rick Briggs makes a point that, AI (artificial intelligence) could be improved by adopting Sanskrit's Phala/Vyapara distinction. This helps is elaborating sentence, in the above case we can include the process of "heating" and the process of "making palatable." These comparisons reveal that Sanskrit is more suitable language which can be represented by systems. Below is a simple semantic net for the above sentence.
My view on this paper: This is a quite old, but very interesting, paper in which Rick Briggs tries to establish commonalities between AI techniques and Sanskrit grammar. Of course, the ability to represent a natural language for system processing would be of great benefit to multiple industries as well as the scientific community at large. To enjoy this paper, one should have some idea background in the Sanskrit language—in my case, I studied Sanskrit from elementary school through college. The idea of implementing Sanskrit as a natural language to the systems is very nicely laid out in the paper. Briggs was able to articulate how cumbersome it is to represent semantic nets, and how that task can be made much simpler using Sanskrit. According to Briggs, Sanskrit as a language is very descriptive, and a better candidate for AI than English for this reason. However, very little research has been done in this area. Another hurdle is the fact that, unlike Sanskrit, English today is widely spoken. One suggestion, then, would be to make a two-layer system, into which we can can input any natural language and the system will process it in terms of Sanskrit—this sounds crazy, but it could be wonderful if we succeed.
Briggs also states that Sanskrit has similarities with mathematics, which is true—in Sanskrit, there is a way of analyzing words with “Sandhi." Using Sandhi, we can break down words technically, and group them under predefined categories. Also, there is a scoring system for each letter in a sentence, which enables letters to be grouped. I see this kind of approach as being useful for AI. It requires huge research on the concept of Sanskrit being used as a natural language for systems/AI. This research would be worthwhile, because it will enlighten us on easier ways to design the system representation. Overall—and most critically—the author makes us think in a different direction.
Author: Jayanth Babu MN - Business Analyst
- Rick Briggs (1985) Knowledge Representation In Sanskrit And Artificial Intelligence.
- Bhatta, Nagesha (1963) Vaiyakarana-Siddhanta-Laghu-Manjusa, Benares (Chowkhamba Sanskrit Series Office).
- Nilsson, Nils J. Principles of Artificial Intelligence. Palo Alto: Tioga Publishing Co
- Bhatta, Nagesha (1974) Parama-La&u-Manjusa