Archive

Posts Tagged ‘Natural language processing’

Statistical NLP

September 4th, 2010 No comments

Statistical natural-language processing uses stochastic, probabilistic and statistical methods to resolve some of the difficulties discussed above, especially those, which arise because longer sentences are highly ambiguous when, processed with realistic grammars, yielding thousands or millions of possible analyses. Methods for disambiguation often involve the use of corpora and Markov models. Statistical NLP comprises all quantitative approaches to automated language processing, including probabilistic modeling, information theory, and linear algebra. The technology for statistical NLP comes mainly from machine learning and data mining, both of which are fields of artificial intelligence that involve learning from data.

Ambiguity and Disambiguation in NLP

September 4th, 2010 1 comment

The biggest problem in natural language processing is that most utterances are ambiguous. Following section describes different type of ambiguities

Lexical ambiguity

The lexical ambiguity of a word or phrase consists in its having more than one meaning in the language to which the word belongs. “Meaning” hereby refers to whatever should be captured by a good dictionary. For instance, the word “bank” has several distinct lexical definitions, including “financial institution” and “edge of a river”. Another example is as in apothecary. You could say, “I bought herbs from the apothecary.” This could mean you actually spoke to the apothecary (pharmacist) or went to the apothecary (drug store).

Syntactic ambiguity

Syntactic ambiguity is a property of sentences, which may be reasonably interpreted in more than one way, or reasonably interpreted to mean more than one thing. Ambiguity may or may not involve one word having two parts of speech or homonyms.

Syntactic ambiguity arises not from the range of meanings of single words, but from the relationship between the words and clauses of a sentence, and the sentence structure implied thereby. When a reader can reasonably interpret the same sentence as having more than one possible structure, the text is equivocal and meets the definition of syntactic ambiguity.

Semantic ambiguity

Semantic ambiguity arises when a word or concept has an inherently diffuse meaning based on widespread or informal usage. This is often the case, for example, with idiomatic expressions whose definitions are rarely or never well defined, and are presented in the context of a larger argument that invites a conclusion.

For example, “You could do with a new automobile. How about a test drive?” The clause “You could do with” presents a statement with such wide possible interpretation as to be essentially meaningless. Lexical ambiguity is contrasted with semantic ambiguity. The former represents a choice between a finite number of known and meaningful context-dependent interpretations. The latter represents a choice between any numbers of possible interpretations, none of which may have a standard agreed-upon meaning. This form of ambiguity is closely related to vagueness.

Referential ambiguity

If it is unclear what a referring expression is referring to, then the expression is referentially ambiguous. For example, a pronoun is a referring expression such as “it”, “he”, “they”, etc. You might point to a famous basketball player and say, “he is rich”, and here “he” refers to the player. Nevertheless, if it is not clear whom you are pointing to, then we might not know to whom the pronoun refers, and so might not be able to determine whether you are saying something true. Similarly, without further information, a statement such as “Ally hit Georgia and then she started bleeding” is also referentially ambiguous. This is because it is not clear whether it is Ally or Georgia, or some third person, who started to bleed.

Referential ambiguity can also arise if you are talking about a group using an expression such as “every”. People are often fond of making generalizations, such as “everyone thinks that democracy is a good thing.” However, is it true that absolutely everyone in the world thinks so? Of course not. Therefore, who are we talking about here? There is no ambiguity if the context makes it clear which group of people we are talking about. Otherwise, there is a need to clarify.

Sometimes the context makes it clear which group of people a speaker is referring to. A teacher taking attendance might say, “Everyone is here.” Of course, the teacher is not saying that every human being in the whole world is here. He or she is likely to be talking about the students in the class.

Pragmatic ambiguity

All languages depend on words and sentences in constructing meaning. However, one of the fundamental facts about words and sentences is that many of them in our languages have more one meaning. Therefore, ambiguity may occur when an utterance can be understood in two or more distinct senses. Kess and Hoppe even say in Ambiguity in Psycholinguistics, “Upon careful consideration, one cannot but be amazed at the ubiquity in language. English, as a language is no exception to it. Since Ambiguity is not a new topic, many researches have been made in this field. In the west, ambiguity can be traces back to the sophism of ancient Greek philosophy. However, previous researches are mainly concerned with phonological ambiguity, lexical ambiguity and grammatical ambiguity. However, the word “pragmatics” was first put forward in 1930s by Charles Morris and the category of pragmatic ambiguity was not explored until the 1970s. So researches on pragmatic ambiguity are still insufficiently thorough, for example, its definition, characteristics, category, functions and understanding still need further study.

Natural Language Processing

September 4th, 2010 No comments

Natural Language processing (NLP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages. 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 natural-language recognition seems to require extensive knowledge about the outside world and the ability to manipulate it.

NLP has significant overlap with the field of computational linguistics, and is often considered a sub-field of artificial intelligence.

Replies to the Chinese Room Argument

September 4th, 2010 No comments

Criticisms of the narrow Chinese Room argument against Strong AI have often followed three main lines, which can be distinguished by how much they concede:

  1. Some critics concede that the man in the room does not understand Chinese, but hold that at the same time there is some other thing that does understand. These critics object to the inference from the claim that the man in the room does not understand Chinese to the conclusion that no understanding has been created. There might be understanding by a larger, or different, entity. This is the strategy of The Systems Reply and the Virtual Mind Reply. These replies hold that there could be understanding in the original Chinese Room scenario.
  2. Other critics concede Searle’s claim that just running a natural language processing program as described in the CR scenario does not create any understanding, whether by a human or a computer system. However, these critics hold that a variation on the computer system could understand. The variant might be a computer embedded in a robotic body, having interaction with the physical world via sensors and motors (“The Robot Reply”), or it might be a system that simulated the detailed operation of an entire brain, neuron by neuron (“the Brain Simulator Reply”).
  3. Finally, some critics do not concede even the narrow point against AI. These critics hold that the man in the original Chinese Room scenario might understand Chinese, despite Searle’s denials, or that the scenario is impossible. For example, critics have argued that our intuitions in such cases are unreliable. Other critics have held that it all depends on what one means by “understand” points discussed in the section on the Intuition Reply. Others (e.g. Sprevak 2007) object to the assumption that any system (e.g. Searle in the room) can run any computer program. And finally some have argued that if it is not reasonable to attribute understanding on the basis of the behavior exhibited by the Chinese Room, then it would not be reasonable to attribute understanding to humans on the basis of similar behavioral evidence (Searle calls this last the “Other Minds Reply”).

In addition to these responses, Critics also independently argue against Searle’s larger claim, and hold that one can get semantics (that is, meaning) from syntactic symbol manipulation, including the sort that takes place inside a digital computer, a question discussed in the section on syntax and semantics.

Application Areas

September 4th, 2010 No comments
Agriculture, Natural Resource Management and the Environment, Architecture & Design

Art

Artificial Noses … and Taste

Astronomy & Space Exploration

Assistive Technologies

Automatic Programming

Autonomous Vehicles, Robots, Rovers, Explorers

Marketing, Customer Relations/Service & E-Commerce, Medicine

Military

Music

Networks – including Maintenance, Security & Intrusion Detection

Petroleum Industry

Politics & Foreign Relations

Public Health & Welfare

Scientific Discovery

Banking, Finance & Investing, Bioinformatics

Business & Manufacturing

Drama, Fiction, Poetry, Storytelling & Machine Writing

Earth & Atmospheric Sciences

Engineering

Filtering

Fraud Detection & Prevention

Agents, Expert Systems

Games & Puzzles

Machine Learning

Natural Language Processing

Robots

Vision

Hazards & Disasters, Information Retrieval & Extraction

Intelligent Tutoring Systems

Knowledge Management

Law

Law Enforcement & Public Safety

Libraries

Machine Translation

Smart Rooms, Smart Houses and Household Appliances, Social Science

Sports

Telecommunications

Transportation & Shipping

Video Games, Toys. Robotic Pets & Entertainment

Artificial Intelligence in the form of expert systems and neural networks have applications in every field of human endeavor. They combine precision and computational power with pure logic, to solve problems and reduce error in operation. Already, robot expert systems are taking over many jobs in industries that are dangerous for or beyond human ability. Some of the applications divided by domains are as follows:

Heavy Industries and Space

Robotics and cybernetics have taken a leap combined with artificially intelligent expert systems. An entire manufacturing process is now totally automated, controlled and maintained by a computer system in car manufacture, machine tool production, computer chip production and almost every high-tech process. They carry out dangerous tasks like handling hazardous radioactive materials. Robotic pilots carry out complex maneuvering techniques of unmanned spacecraft sent in space. Japan is the leading country in the world in terms of robotics research and use.

Finance

Banks use intelligent software applications to screen and analyze financial data. Software that can predict trends in the stock market have created which have known to beat humans in predictive power. Credit card providers, telephone companies, mortgage lenders, banks, and the U.S. Government employs AI systems to detect fraud and expedite financial transactions, with daily transaction volumes in the billions. These systems first use-learning algorithms to construct profiles of customer usage patterns, and then use the resulting profiles to detect unusual patterns and take the appropriate action (e.g., disable the credit card). Such automated oversight of financial transactions is an important component in achieving a viable basis for electronic commerce.

Computer Science

Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object-oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence remains a distant dream but people are getting an idea of the ultimate path, which could lead to it.

Aviation

Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object-oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence remains a distant dream but people are getting an idea of the ultimate path, which could lead to it.

Weather Forecast

Neural networks are used for predicting weather conditions. Previous data is fed to a neural network, which learns the pattern and uses that knowledge to predict weather patterns.

Swarm Intelligence

This is an approach to, as well as application of artificial intelligence similar to a neural network. Here, programmers study how intelligence emerges in natural systems like swarms of bees even though on an individual level, a bee just follows simple rules. They study relationships in nature like the prey-predator relationships that give an insight into how intelligence emerges in a swarm or collection from simple rules at an individual level. They develop intelligent systems by creating agent programs that mimic the behavior of these natural systems… etc.