The main programs here are Arthur Samuel's, the rote learning method which is a lot like a memory based method, generalization learning which is a lot like backprop and a signature table approach that also gives you a feed-forward type network. One of Samuel's programs did beat a checkers champion and the AI community has often make a fuss over that saying that this AI program played a "championship-level" game however that expert beat the program in the next 6 games. Note too, what Samuels says: "the program is quite capable of beating any amateur player and can give better players a good contest".
This section looks at Berliner's program, two backprop versions by Tesauro and a temporal difference method by Tesauro. This latter program is VERY good and has found strategies that now human backgammon players acknowledge are better than some of the old humanly devised strategies.
This covers a number of game playing techniques, notably checkers and backgammon because so much good research has been done on these problems and because so many different techniques have been tried.
It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in a language. Lexical analysis is dividing the whole chunk of txt into paragraphs, sentences, and words.
During this, what was said is re-interpreted on what it actually meant. It involves deriving those aspects of language which require real world knowledge.
The meaning of any sentence depends upon the meaning of the sentence just before it. In addition, it also brings about the meaning of immediately succeeding sentence.
It involves analysis of words in the sentence for grammar and arranging words in a manner that shows the relationship among the words. The sentence such as "The school goes to boy" is rejected by English syntactic analyzer.
It draws the exact meaning or the dictionary meaning from the text. The text is checked for meaningfulness. It is done by mapping syntactic structures and objects in the task domain. The semantic analyzer disregards sentence such as "hot ice-cream".
It includes the general knowledge about the world.
It deals with how the immediately preceding sentence can affect the interpretation of the next sentence.
It deals with using and understanding sentences in different situations and how the interpretation of the sentence is affected.
It is concerned with the meaning of words and how to combine words into meaningful phrases and sentences.
It refers to arranging words to make a sentence. It also involves determining the structural role of words in the sentence and in phrases.
It is primitive unit of meaning in a language.
It is a study of construction of words from primitive meaningful units.
It is study of organizing sound systematically.
★ World Knowledge
Referring to something using pronouns. For example, Rima went to Gauri. She said, "I am tired." − Exactly who is tired?
A sentence can be parsed in different ways.
For example, "He lifted the beetle with red cap." − Did he use cap to lift the beetle or he lifted a beetle that had red cap?
It is at very primitive level such as word-level.
For example, treating the word "board" as noun or verb?
NL has an extremely rich form and structure.
It is very ambiguous. There can be different levels of ambiguity:
★ Lexical ambiguity
★ Syntax Level ambiguity
★ Referential ambiguity
It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation.
It includes retrieving the relevant content from knowledge base.
It includes choosing required words, forming meaningful phrases, setting tone of the sentence.
It is mapping sentence plan into sentence structure.
Understanding involves the following tasks −
Mapping the given input in natural language into useful representations.
Analyzing different aspects of the language.
There are two components of NLP as given:
★ Natural Language Understanding (NLU)
★ Natural Language Generation (NLG)
Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.
Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc.
Artificial intelligence language processing (AILP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages; it began as a branch of artificial intelligence. In theory, natural language processing is a very attractive method of human-computer interaction. Natural language understanding is sometimes referred to as an AI-complete problem because it seems to require extensive knowledge about the outside world and the ability to manipulate it.