Name: 自然語言處理和哈拉機器人 PART A (Natural Language Processing and Chat Bots PART A)
Uploaded: 2021-01-14T06:00:38.000Z
Duration: 7 min 47 s
Description: 【看影片學英語】數萬部 YouTube 影片，搭配英漢字典即點即查，輕鬆掌握單字發音與用法，長久累積看電影不必再看字幕。

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

情態副詞

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

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

Intelligence is “the 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 the “father” of 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 restricted “blocks worlds” with 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

write “conceptual ontologies” which 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 agreed” but 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 say “Maria’s birthday is on the 1st of

September” or “Maria 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 example “what 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.