Natural Language and Computer Programs
Anyone who has tried to explain the workings of a computer, or even a VCR, to an older relative has a very good idea of why natural language operation is a goal of computer science researchers. Simply put, most people have no desire to learn a computer language in order to use their electronic devices. In order to allow people to effectively use computer-based systems, those systems must be programmed to understand natural language – the language a regular person speaks – and respond in kind.
Most natural language-processing systems break that task down into two parts, comprehension and production. Some systems, like the search engine ask.com, where the user types in a whole interrogative sentence instead of a few terms to search for, are programmed to take commands in English and so have comprehension as their goal. Others, particularly those designed to pass the test proposed by Alan Turing in which a computer must pass as a human in conversation with an interrogator, are designed to simply produce realistic responses, sometimes without bothering to break down the input at all.
For the purposes of simplicity, most natural language programs operate through typed input and printed or on-screen output, since speech recognition and production are just complications at this point and can always be integrated later, simply by having the program convert the speech to text and vice-versa. By working only with typed input, a whole host of obstacles to understanding are avoided. People, when speaking, have accents, slur words, change sentence structure mid-thought, stick in “like” anywhere they want, and do many other things that make everyday speech much less straightforward than the slightly more formal process of typing. Even typed, however, an English sentence is not an easy thing to parse.
An example of this difficulty can be seen in the sentence “I left a job for my wife”. Out of context, it is impossible to determine which of two possible meanings is the correct one. Did the speaker leave a job (i.e. quit) because of his wife, or did he leave a job (i.e. let one remain) for his wife? A computer must be able to refer to the context around such a sentence in order to extract the meaning from it.
Human conversations are too complicated for machines to understand and interact properly without flaws. This is what separates humans from animals. Even the dumbest man will be able to form sentences and converse with other human beings, while even the smartest animals will never be able to.... ... middle of paper ... ...
At some point in our lives, we have wondered about the possibility of a computer being able to think. John Searle addresses this issue in his paper, “Can Computers Think?”, where he argues that computers cannot think because they are directed by formal information. This means that the information presented is only syntax with no semantics behind it. In this paper, I will elaborate more on Searle’s position and reasoning whilst critiquing his argument by saying that it is possible to derive semantics from syntax. Finally, I will analyze the significance of my criticism and present a possible response from Searle to defend his argument.
One of the hottest topics that modern science has been focusing on for a long time is the field of artificial intelligence, the study of intelligence in machines or, according to Minsky, “the science of making machines do things that would require intelligence if done by men”.(qtd in Copeland 1). Artificial Intelligence has a lot of applications and is used in many areas. “We often don’t notice it but AI is all around us. It is present in computer games, in the cruise control in our cars and the servers that route our email.” (BBC 1). Different goals have been set for the science of Artificial Intelligence, but according to Whitby the most mentioned idea about the goal of AI is provided by the Turing Test. This test is also called the imitation game, since it is basically a game in which a computer imitates a conversating human. In an analysis of the Turing Test I will focus on its features, its historical background and the evaluation of its validity and importance.
In Turing’s test, an isolated interrogator attempts to distinguish the identities between discreet human and computer subjects based upon their replies to a series of questions asked during the interrogation process. Questions are generally generated through the use of a keyboard and screen, thus communication can only be made through text-only channels. For example, a sample question would contain something along the lines of “What did you think about the weather this morning?” and adequate responses could include, “I do tend to like a nice foggy morning, as it adds a certain mystery” or rather “Not the best, expecting pirates to come out of the fog” or even “The weather is not nice at the moment, unless you like fog”. After a series of tests are performed, if the interrogator fails at identifying the subject more than 70 percent of the time, that subject is deemed intelligent. Simply put, the interrogator’s ability to declare the machine’s capability of intelligence directly correlates to the interrogator’s inability to distinguish between the two subjects.
... and the Union of Soviet Socialist Republics on the Limitation of Anti-Ballistic Missile Systems." U.S. Department of State. U.S. Department of State, n.d. Web. 06 May 2014.
The way in which The Chinese Room example works is that suppose that a person who does not understand or speak Chinese at all is told to sit in a room with an input slot and an output slot. A person that understands Chinese slips Chinese characters through the slot hoping for a response in return. The person sitting in the Chinese room is given a set of formal rules for using the Chinese symbols; however, the rules do not tell him what the words mean, they just simply indicate what they should write back in response to the letters he has received through the input slot. These rules entail grammatically correct information to the receiver even though the person in the room has not idea what they are writing out; a similar—or same— concept that a robot has. The rules only mention the shape and order the characters should be presented—their syntax. This is where Searle argues the idea of semantics as one cannot come to ...
As our world expands through the growing abilities and applications of computers in our everyday lives, it seems that the role of the computer has been reversed. Before we knew that the computer only understood what we programmed it to understand; however, now the majority of our society is learning more from computers than they are able to input into it. Dumm (1986 p.69)
New advancements make it possible to not only program computers to do what people tell them to, but to think for themselves.
Imagine asking your computer to do something in the same way you would ask a friend to do it. Without having to memorize special commands that only it could understand. For computer scientists this has been an ambitious goal; that can further simplify computers. Artificial Intelligence, a system that can mimic human intelligence by performing task that usually only a human can do, usually has to use a form of natural language processing. Natural language processing, a sub-field of computer science and artificial intelligence, concerns the successfully interaction between a computer and a human. Currently one of the best examples of A.I.(Artificial Intelligence) is IBM 's Watson. A machine that gained popularity after appearing on the show
Neuro – Linguistic programming is concerned with how individuals absorb and make sense of information (Young 1983, p.1012). It is referred to as a model of human behaviour and cognition (weaver 2010 p.40). It has been stated (O’Connor, 2001, p1) as the study of brilliance and quality. Neuro-linguistic programming started with John Grinder, who was a linguistic professor and Richard Bandlar who had both a mathematical and computer programming background (Gleeson, 2009, p.6). Both professors had an interest in modelling patterns of behaviour to produce excellence. The traditional focus of neuro-linguisitc programming was with therapeutic techniques however, it has now steered in many other directions (Gleeson, 2009, p.6). Neuro-linguisitc programming cannot be pinned down to one definition (O’Connor, 2001, p1). Although it has tried to be defined on many occasions, each definition focuses on different aspects of it (Dimmick, 1995, pxi). The co-founders have defined neuro-linguisitc programming themselves; however their definitions seem to differ (Dimmick, 1995, pxi). Bandlar defines it as a methodology of modelling which leaves behind a trail of techniques (Dimmick, 1995, pxi). Grinder defines it an epistemology which is the study of self creation or how knowledge is obtained (Dimmick, 1995, pxi). Neuro-linguisitc programming is found within a variety of practices with a range of practitioners utilizing these skills. (McDermott, Jago 2001, p.1). This paper will look at the benefits of neuro-linguisitc programming and will conclude with how this would benefit social work practitioners.
Language is an essential thing needed to communicate and to develop the skills one needs to be a complete, whole, intelligent individual. Language is what separates us from the rest of the animal kingdom. Here we shall define language and lexicon, evaluate the key features of language, describe the four levels of language structure and processing, and analyze the role of language processing in cognitive psychology.
In this essay I intend to investigate how differently one of the closed word classes, determiners, are approached in a series of pre and post corpus-based English grammar reference books, course books and practice books. And the theme of my investigation is how corpus affects the development of English teaching materials. The grammar reference books I intend to analyze and compare are “A Comprehensive Grammar of the English Language” (ACGEL) and “Cambridge Grammar of English” (CGE). The former is an indispensable grammar reference book first published in 1985, which has been widely consulted in researches in relation to English linguistic studies, while the later offers clear explanations of both spoken and written English grammar based on authentic everyday usage.
The Features of Written Language and Speech In English language there are two different ways of actually presenting language. These are written language and speech. These two factors of speech both include many different features between themselves. These features are mostly opposite to each other as they are completely different ways of presenting language. Written language is structured into paragraphs unlike general speech which is hardly thought about before being said and is flowing naturally.
Linguistics and Computer Science are the main components of CL. According to Bolshakov & Gelbukh (2004) CL can be defined as a synonym of NLP. CL aims to construct computer programs which are able to process (recognize and synthesize) texts and speech of natural languages. This process enables scientists to create several applications related to this field such as Machine Translation, Spell and Grammar Checkers, Information Retrieval, Speech Recognition and Speech Synthesis, Topical Summarization, Extraction of factual data from texts, Natural language interface.
If the nineteenth century was an era of the Industrial revolution in Europe, I would say that computers and Information Technology have dominated since the twentieth century. The world today is a void without computers, be it healthcare, commerce or any other field, the industry won’t thrive without Information Technology and Computer Science. This ever-growing field of technology has aroused interest in me since my childhood. After my twelfth grade, the inherent ardor I held for Computer Science motivated me to do a bachelors degree in Information Technology. Programming and Math, a paragon of logic and reasoning, have always been my favorite subjects since childhood.