Grammatical habits in written English reveal linguistic features of non-native speakers' languages

Jul 22, 2014 by Larry Hardesty

Computer scientists at MIT and Israel's Technion have discovered an unexpected source of information about the world's languages: the habits of native speakers of those languages when writing in English.

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 .

"These [linguistic] features that our system is learning are of course, on one hand, of nice theoretical interest for linguists," says Boris Katz, a principal research scientist at MIT's Computer Science and Artificial Intelligence Laboratory and one of the leaders of the new work. "But on the other, they're beginning to be used more and more often in applications. Everybody's very interested in building computational tools for world languages, but in order to build them, you need these features. So we may be able to do much more than just learn linguistic features. … These features could be extremely valuable for creating better parsers, better speech-recognizers, better natural-language translators, and so forth."

In fact, Katz explains, the researchers' theoretical discovery resulted from their work on a practical application: 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. The hope was to develop grammar-correcting software that could be tailored to a user's specific linguistic background.

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 1,000 English-language essays written by 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. Then it looked for patterns in those relationships that correlated with the writers' native languages.

Like most machine-learning classification algorithms, Berzak's assigned probabilities to its inferences. 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: The algorithm's probability estimates provided a quantitative measure of how closely related any two languages were; Russian speakers' syntactic patterns, for instance, were more similar to those of Polish speakers than to those of Japanese speakers.

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. The nine languages that are in the Indo-European family, for instance, were clearly distinct from the five that aren't, and the Romance languages and the Slavic languages were more similar to each other than they were to the other Indo-European languages.

What's your type?

"The striking thing about this tree is that our system inferred it without having seen a single word in any of these languages," Berzak says. "We essentially get the similarity structure for free. 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."

By "typological features," Berzak means the types of syntactic patterns that linguists use to characterize languages—things like the typical order of subject, object, and verb; how negations are formed; or whether nouns take articles. A widely used online linguistic database called the World Atlas of Language Structures (WALS) identifies nearly 200 such features and includes data on more than 2,000 languages.

But, Berzak says, for some of those languages, WALS includes only a handful of typological features; the others just haven't been determined yet. Even widely studied European languages may have dozens of missing entries in the WALS database. 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. In work presented last month at the Conference on Computational Natural Language Learning, Berzak, Katz, and Reichart ran a series of experiments that examined each of the 14 languages of the essays they'd analyzed, trying to predict its typological features from those of the other 13 languages, based solely on the similarity scores produced by the system. On average, those predictions were about 72 percent accurate.

Branching out

The 14 languages of the researchers' initial experiments were the ones for which an adequate number of essays—an average of 88 each—were publicly available. 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. It shouldn't be too difficult, Berzak argues, to track down more English- essays by native Tswana speakers.

Explore further: Fluency outweighs pronunciation for understanding non-native English speakers

add to favorites email to friend print save as pdf

Related Stories

New language discovery reveals linguistic insights

Jun 18, 2013

A new language has been discovered in a remote Indigenous community in northern Australia that is generated from a unique combination of elements from other languages. Light Warlpiri has been documented by University of Michigan ...

Diversity is good for your English

Oct 21, 2013

New research from experts at The University of Manchester has revealed that as the country's linguistic diversity increases, speakers of other languages are also becoming more proficient in English.

Language by mouth and by hand

Apr 03, 2013

Humans favor speech as the primary means of linguistic communication. Spoken languages are so common many think language and speech are one and the same. But the prevalence of sign languages suggests otherwise. Not only can ...

The structure of language influences learning

Oct 03, 2013

There are words that convey a meaning, like verbs, nouns or adjectives, and others, like articles or conjunctions that sustain them, providing a structure for the sentence. A few years ago some scientists ...

Indo-European languages originate in Anatolia

Aug 28, 2012

(Phys.org)—The Indo-European languages belong to one of the widest spread language families of the world. For the last two millenia, many of these languages have been written, and their history is relatively ...

Recommended for you

Computerized emotion detector

16 hours ago

Face recognition software measures various parameters in a mug shot, such as the distance between the person's eyes, the height from lip to top of their nose and various other metrics and then compares it with photos of people ...

Cutting the cloud computing carbon cost

Sep 12, 2014

Cloud computing involves displacing data storage and processing from the user's computer on to remote servers. It can provide users with more storage space and computing power that they can then access from anywhere in the ...

Teaching computers the nuances of human conversation

Sep 12, 2014

Computer scientists have successfully developed programs to recognize spoken language, as in automated phone systems that respond to voice prompts and voice-activated assistants like Apple's Siri.

Mapping the connections between diverse sets of data

Sep 12, 2014

What is a map? Most often, it's a visual tool used to demonstrate the relationship between multiple places in geographic space. They're useful because you can look at one and very quickly pick up on the general ...

User comments : 0