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Natural Language

In the philosophy of language, a natural language (or ordinary language) is a language that is spoken, written, or signed by humans for general-purpose communication, as distinguished from formal languages (such as computer-programming languages or the “languages” used in the study of formal logic, especially mathematical logic) and from constructed languages.

Though the exact definition is debatable, natural language is often contrasted with artificial or constructed languages such as Esperanto, Latino Sexione, and Occidental.

Linguists have an incomplete understanding of all aspects of the rules underlying natural languages, and these rules are therefore objects of study. The understanding of natural languages reveals much about not only how language works (in terms of syntax, semantics, phonetics, phonology, etc), but also about how the human mind and the human brain process language. In linguistic terms, ‘natural language’ only applies to a language that has evolved naturally, and the study of natural language primarily involves native (first language) speakers.

The goal of the Natural Language Processing (NLP) group is to design and build software that will analyze, understand, and generate languages that humans use naturally, so that eventually you will be able to address your computer as though you were addressing another person.

This goal is not easy to reach. “Understanding” language means, among other things, knowing what concepts a word or phrase stands for and knowing how to link those concepts together in a meaningful way. It’s ironic that natural language, the symbol system that is easiest for humans to learn and use, is hardest for a computer to master. Long after machines have proven capable of inverting large matrices with speed and grace, they still fail to master the basics of our spoken and written languages.

There are several major reasons why natural language understanding is a difficult problem. They include:

 

  1. The complexity of the target representation into which the matching is being done. Extracting meaningful information often requires the use of additional knowledge.
  2. The type of mapping: one-to-one, many-to-one, one-to-many, or many-to-many. One-to-many mappings require a great deal of domain knowledge beyond the input to make the correct choice among target representations. So for example, the word tall in the phrase “a tall giraffe” has a different meaning than in “a tall poodle.” English requires many-to-many mappings.
  3. The level of interaction of the components of the source representation. In many natural language sentences, changing a single word can alter the interpretation of the entire structure. As the number of interactions increases, so does the complexity of the mapping.
  4. The presence of noise in the input to the understander. We rarely listen to one another against a silent background. Thus speech recognition is a necessary precursor to speech understanding.
  5. The modifier attachment problem. (This arises because sentences aren’t inherently hierarchical, I’d say — POD.) The sentence Give me all the employees in a division making more than $50,000 doesn’t make it clear whether the speaker wants all employees making more than $50,000, or only those in divisions making more than $50,000.
  6. The quantifier scoping problem. Words such as “the,” “each,” or “what” can have several readings.
  7. Elliptical utterances. The interpretation of a query may depend on previous queries and their interpretations. E.g., asking Who is the manager of the automobile division and then saying, of aircraft?.

Natural Language Processing. Retrieved: 05-05-2008

*http://en.wikipedia.org/wiki/Natural_language

*http://research.microsoft.com/nlp/

*http://www.cs.dartmouth.edu/~brd/Teaching/AI/Lectures/Summaries/natlang.html

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mayo 5, 2008 - Posted by | Human Language Technologies |

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