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  • Over the years, since computer science evolutions, Artificial Intelligence principles demanded

  • the Natural Language Processing so that machines or software could communicate efficiently

  • with human. Today we will make an introduction to Natural

  • Language Processing and furthermore we will present chat bots, which are directly connected

  • to Natural Language Processing.

  • So what is Artificial Intelligence exactly? John McCarthy in 1956 stated that Artificial

  • Intelligence isthe science and engineering of making intelligence machines”. In simple

  • words, Artificial Intelligence is the Computer Science sector that has to do with the design

  • and implementation of software that are capable to imitate the human cognitive skills, showing

  • characteristics that are normally attributed to human behavior. Some examples are the problems

  • solution, vision, learning, conclusions and understanding the natural language.

  • Natural Language Processing is a field of computer science and more specifically is

  • a field of artificial intelligence but also is included in linguistics concerned with

  • the interactions between computers and human languages.

  • The main purpose is to create and implement computational models, that are able to extract

  • meaningful information from natural language input and furthermore to produce a natural

  • language output. Some aspects of Natural language processing have to do with communicating with

  • the computer, the machine translation and the browsing and filtering of texts, written

  • in natural language from an agent. Natural Language Processing is actually identical

  • to the field of computational linguistics which is divided to theoretical and applied

  • part. The theoretical computational linguistic, deals with how people acquire and use knowledge,

  • to produce and understand natural language. The applied part focuses on the practical

  • results of modeling the use, of human language. After processing the natural language we can

  • understand more about the world. And if scientists manage to succeed in the creation of a computational

  • language model, we will own an extremely strong communication tool.

  • Natural language is divided to the written and oral language. In order to conquer oral

  • language, it is essential to understand how written word is used and structured.

  • Alan Turing is a famous scientist that is known as thefatherof computer science

  • and artificial intelligence. In 1950 he published his famous article titled: Computing Machinery

  • and Intelligence which introduced a criterion of machine intelligence, what today is called

  • Turing Test. The idea was to test if a human could not tell apart if he/she was communicating

  • with a machine or a human. Practical work began with the Georgetown-IBM

  • experiment in 1954, which was developed by the Georgetown University and IBM. This experiment

  • involved fully automatic translation of more than sixty Russian sentences into English.

  • Although at first, scientists were very optimistic about the results of the experiment and thought

  • that the machine translation would be a solved problem within three of five years, real progress

  • was actually a lot slower than expected. Moving forward in 1972, T. Winograd created

  • the system SHRDLU, using the language Lisp. This is a natural language system working

  • in restrictedblocks worldswith restricted vocabularies. Examples of its aspects are

  • interpretation of questions, states and directions, also ability of entailment and learning new

  • words. During the 70’s many programmers began to

  • writeconceptual ontologieswhich structured real world information into computer understandable

  • data. Up to the 80’s most of Natural Language Processing systems used hand written rules.

  • In the late 80’s though, of machine learning algorithms were introduce so the systems could

  • use decision trees or statistical models which make soft, probabilistic decisions based on

  • attaching real-valued weights to the input data. Nowadays systems focus on unsupervised

  • or semi-supervised learning algorithms that are able to learn from data that has not been

  • hand-annotated. In order to achieve natural language processing

  • it is useful to divide the process to some steps and parts.

  • First, a morphological analysis has to be done, where separate words are analyses to

  • their components and the non verbal symbols. Then, we proceed to syntactic analysis where

  • linear sequences of words are converted into structures that illustrate how words are connected

  • to each other. Some sequences may be rejected by the system, if they violate some of the

  • language rules. Semantic Analysis follows, which gives meaning

  • to the structures that are produced from the syntactic analysis.

  • Then we must do a discourse Integration, which analyzes the meaning of a single sentence

  • compared with the meaning of the previous and the next sentences.

  • And at last we apply a pragmatic analysis in order to define the real meaning of the

  • structure that represents what it has been said.

  • During Natural Language Processing several problems may have to be encountered. These

  • problems have to do with the fact that people often use natural language in a certain way

  • expressing feelings or questions without using the official language and without satisfy

  • every rule of the language. For example, the sentences of natural language

  • contain incomplete description of the information that intends to transfer. We may say, “I

  • called Maria to go to the movies and she agreedbut we actually mean, “Maria was at home

  • when I called her. She answered the phone and I asked her to go to the movies. She said

  • ok.” Another example is that the same sentence

  • may be used for different occasions and meanings and on the other hand there are a lot of ways

  • to say the same thing. For example we may sayMaria’s birthday is on the 1st of

  • SeptemberorMaria was born on the 1st of September”.

  • And also there is the fact that none program of natural language can be ever complete since

  • new words, new phrases or meanings are often created by people.

  • Natural Language processing in our times is widely spread and used. A lot of application,

  • software and machines all over the world use this kind of processing in order to achieve

  • high quality communication. There is machine translation that automatically

  • translates text from one human language to another.

  • Also Natural language generation and understanding systems exist, that have to convert information

  • from computer databases into readable human language and understand several input from

  • users. Optical character recognition has to determine

  • a corresponding text given an image representing printed text.

  • Question answering has to do with answering human language questions. There are specific

  • answers to some questions if we ask for example the capital of a country. But some questions

  • can have different answers for examplewhat is the meaning of life”.

  • Speech recognition where given a sound clip of a person speaking, the system has to determine

  • the textual representation of the speech. As you can see, natural language processing

  • is really important for today’s applications and further evolution of technology. It helps

  • simple and more complex systems to achieve their goals.

Over the years, since computer science evolutions, Artificial Intelligence principles demanded

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Natural Language Processing and Chat Bots PART A

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    shin posted on 2016/03/10
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