It is the Science and Engineering of making intelligent machines, especially intelligent computer programs.
Media can only be downloaded from the desktop version of this website. The work could enable computers chewing through relatively accessible documents to approximate data that might take trained linguists months in the field to collect. But that data could in turn lead to better computational tools.
So we may be able to do much more than just learn linguistic features. About a year ago, Katz proposed to one of his students, Yevgeni Berzak, that he try to write an algorithm that could automatically determine the native language of someone writing in English.
Family resemblance With help from Katz and from Roi Reichart, an engineering professor at the Technion who was a postdoc at MIT, Berzak built a system that combed through more than 1, English-language essays written by native speakers of 14 different languages.
First, it analyzed the parts of speech of the words in every sentence of every essay and the relationships between them. It might conclude, for instance, that a particular essay had a 51 percent chance of having been written by a native Russian speaker, a 33 percent chance of having been written by a native Polish speaker, and only a 16 percent chance of having been written by a native Japanese speaker.
In analyzing the results of their experiments, Berzak, Katz, and Reichart noticed a remarkable thing: When they used that measure to create a family tree of the 14 languages in their data set, it was almost identical to a family tree generated from data amassed by linguists.
Now we can take it one step further and use this tree to predict typological features of a language for which we have no linguistic knowledge. A widely used online linguistic database called the World Atlas of Language Structures WALS identifies nearly such features and includes data on more than 2, languages.
At the time of his study, Berzak points out, only 14 percent of the entries in WALS had been filled in. The new system could help fill in the gaps.
On average, those predictions were about 72 percent accurate. But Katz is confident that given enough training data, the system would perform just as well on other languages. Berzak points out that the African language Tswana, which has only five entries in WALS, nonetheless has 6 million speakers worldwide.
Some researchers think that such modifications take place at a fairly superficial level. But others think they operate over bits of abstract grammar.
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Luca Cardelli has made exceptional contributions to the field of programming languages and beyond. Throughout his career, he has re-invented himself every decade or so, while continuing to make true innovations.
His achievements span many areas: software; language design, including experimental languages; programming language .