This is an interesting paper which discusses on, how Sanskrit could be a best natural language for computer processing/Artificial Intelligence (AI). Over the decades scientific community is trying hard to identify and design systems which can represent and process natural language. English is widely spoken language and we intend machines to learn English and process the data. However, one cannot program systems using natural English language, we have to reframe or rephrase in a systematic way so that system can understand. In this paper author explains how Sanskrit is significantly advanced than English and can be made use in the field of artificial intelligence. The paper has three parts where first; knowledge representation scheme is discussed using semantic nets. In second part author outlines methods used by ancient Indian Grammarians to analyze sentence unambiguously. Finally, equivalence is established between Sanskrit language analysis to the techniques used in applications of AI.
When attempts of machine translation failed to teach a computer to understand natural language AI turned to knowledge representation. When we try to teach a machine any natural language, it should not be always a word to word mapping. One has to overcome ambiguity of words in natural language and interference of syntax. To overcome the ambiguity of words, there should be a representation of meaning independent of words used. Author takes three sentences as examples to demonstrate a prototypical semantic net system.
1. “John gave the book to Mary”
The grammatical information can be transformed into an arc and a node. The above sentence can be
stored as triples.
give, agent, John
give, object, ball
give, recipient, mary
give,time, past
This can be schematically represented as below:
Figure 1: Schematic Representation of sentence “John gave the book to Mary” (Rick Briggs, 1985).
2. “John told Mary that the train moved out of the station at 3 o’ clock.”
As the below figure shows there was a change in state in which the train moved to unspecified location from the station. It went to the former at 3:00 and from latter at 3:00. We can now covert this to triples like previous example. Here Verb is given significance and is considered the focus and distinguishing aspect of the sentence.
Figure 2: Schematic Representation of sentence “John told Mary that the train moved out of the station
at 3 o’ clock.” (Rick Briggs, 1985).
There are other sentences when drawn as above nets will represent only a state of a thing or an event.
3. “John, a programmer living at Maple St., gives a book to Mary, who is a lawyer.”
The above statement if read as semantic net it would give an awkward and cumbersome representation. The degree to which a semantic net is cumbersome and odd-sounding in a natural language is the degree to which that language is “natural” and deviates from the precise or “artificial.” Refer to the below image which explains the same.
Figure 3: Schematic Representation of sentence “John, a programmer living at Maple St., gives a book to Mary, who is a lawyer.” (Rick Briggs, 1985).
Author gives brief history of Sanskrit grammarians, Panini who lived during 4th century BCE gave a strong foundation to the Sanskrit grammar. Panini’s successors like Bhartrhari gave algebraic formulation for grammar and tried to improve upon them. During the 16th century Kaundabhatta and Bhattoji Dikshita gave new touch to the existing grammar with their publication of Bhattoji Dikshita’s Vaiyakarana-bhusanasara. Similarly during 17th century Nagesha contributed to the language with his major work on Vaiyakaranasiddhantamanjusa, or Treasury of definitive statements of grammarians. Author sites these grammarians and makes a strong point that the Sanskrit is not only a simple spoken language but has a scientific and mathematical backbone to it.
Part 2: Sanskrit Language Analysis and Its Equivalence with Techniques Used In Applications of AI
Sanskrit is unique and advanced because unlike other linguistic theories, it does not work 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 verb in Sanskrit is Vyapara (Action) + Phala (Result)
In general verb is defined as “to do”. However, Sanskrit language is architected in such a way that the sentence provides not only the action but, also the other details as well such as tense, quality of the agent involved (Singular, Double, Plural) and the degree of the agent (First, Second, Third).
Ex: 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 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, 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 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 out of which six are definable representation of auxiliary activities (Agent, Object, Instrument, Recipient, Point of Departure and Locality), seventh is genitive which is not represented by other six.
The case endings are explained by taking below sentence as example:
“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:
1. An Agent represented by the person Maitra
2. An Object by the “rice”
3. An Instrument by the “fire”
4. A Recipient by the person Devadatta
5. A Point of Departure (which includes the causal relationship) by the “friendship” (which is between Maitra and Devadatta)
6. The Locality by the “pot”
This explanation shows how Sanskrit is advanced and stands out from other languages.
Author gives another example to show how Sanskrit sentence formation is detailed when compared to English. Consider the below sentence in accordance with Sanskrit.
“Because of the wind, a leaf falls from a tree to the ground.” – Here wind is the instrument bringing leaf. Tree is point of departure. Ground is locality and Leaf is 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 agent and leaf will be considered as object. This sentence is transitive whereas the earlier one was intransitive.
In the final section author tries to establish equivalence between Sanskrit language and techniques used in AI (semantic nets). Both these systems stands on extensive degree of specification which is crucial in understanding the real meaning of the sentence to the extent that it will allow inferences to be made about the facts not explicitly stated in the sentence.
“Out of friendship, Maitra cooks rice for Devadatta in a pot over a fire” – This sentence when represented in semantic nets, it will have triples as 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.
The same sentence in Sanskrit can be rendered as
cook, agent, Maitra
cook, object, rice
cook, instrument, fire
cook, recipient, Devadatta
cook, because-of, friendship
friendship, Maitra, Devadatta
cook, locality, pot.
Author makes a point that, to make AI more improved one has to adopt Phala/Vyapara distinction which is in Sanskrit. This helps is elaborating sentence, in the above case we can include the process of “heating” and the process of “making platable”. These comparisons reveal that Sanskrit is closest language which can be represented by systems. Also below is an easy semantic net for the above sentence.
Figure 4: Schematic Representation of sentence “Out of friendship, Maitra cooks rice for Devadatta in a pot over a fire.” (Rick Briggs, 1985).
My Views On This Paper: This is an quite old but very interesting paper where author tries to bring equilibrium between AI techniques and Sanskrit grammar. All the industries and scientific community would have huge advantage, if we will be able to represent a natural language for the system processing. To enjoy the content of the paper one should have idea about Sanskrit language (Being said that, I have studied Sanskrit in my school and college). The idea of implementing Sanskrit as a natural language to the systems is very nicely laid out in the paper. Author was able to signify how cumbersome is to represent the semantic nets and how it can be made much simpler using Sanskrit. According to the paper, it is evident that Sanskrit as a language is very descriptive and beats English in the AI race. However, very minimal research is done in this area. Another hurdle would be how many of us would be willing to adopt for Sanskrit as English is widely spoken. A suggestion would be, if we will make two layered system where we can input any natural language and system will process it in terms of Sanskrit, this would be crazy but could be wonderful if we succeed. Author states that Sanskrit has relativity with Mathematics which is true – in Sanskrit there is a way of analyzing words with “Sandhi”. Using Sandhi we can break any word technically and group them under pre-defined category. Also there is a scoring system for each letter in a sentence and grouping them. I see this kind of approach will be useful in AI area. It requires huge research on the concept of Sanskrit being used as natural language for systems/AI. It would be a worth of a research, as it will enlighten us in making easier way to design the system representation. Overall author makes us think in a different direction with his research and views.
References:
1. Rick Briggs (1985) Knowledge Representation In Sanskrit And Artificial Intelligence.
2. Bhatta, Nagesha (1963) Vaiyakarana-Siddhanta-Laghu-Manjusa, Benares (Chowkhamba Sanskrit Series Office).
3. Nilsson, Nils J. Principles of Artificial Intelligence. Palo Alto: Tioga Publishing Co
4. Bhatta, Nagesha (1974) Parama-La&u-Manjusa
