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Natural language interfaces have in the past led users to anthropomorphize the computer, or at least to attribute more intelligence to machines than is warranted.On the part of the user, this has led to unrealistic expectations of the capabilities of the system.Such expectations will make it difficult to learn the restrictions of the system if users attribute too much capability to it, and will ultimately lead to disappointment when the system fails to perform as expected as was the case in the AI winter of the 1970s and 80s.A 1995 paper titled 'Natural Language Interfaces to Databases – An Introduction', describes some challenges: Other goals to consider more generally are the speed and efficiency of the interface, in all algorithms these two points are the main point that will determine if some methods are better than others and therefore have greater success in the market.In addition, localisation across multiple language sites requires extra consideration - this is based on differing sentence structure and language syntax variations between most languages.Finally, regarding the methods used, the main problem to be solved is creating a general algorithm that can recognize the entire spectrum of different voices, while disregarding nationality, gender or age.The significant differences between the extracted features - even from speakers who says the same word or phrase - must be successfully overcome.
Natural language interfaces are an active area of study in the field of natural language processing and computational linguistics.
An intuitive general natural language interface is one of the active goals of the Semantic Web. Many formal (un-natural) programming languages incorporate idioms of natural human language.
Likewise, a traditional keyword search engine could be described as a "shallow" natural language user interface.
A natural language search engine would in theory find targeted answers to user questions (as opposed to keyword search). ', conventional search engines ignore the question and instead search on the keywords 'state', 'income' and 'tax'.
For example, when confronted with a question of the form 'which U. Natural language search, on the other hand, attempts to use natural language processing to understand the nature of the question and then to search and return a subset of the web that contains the answer to the question.