Linguistics (ACL) Conference in Bulgaria entitled “Unsupervised Consonant-Vowel Prediction Over Hundreds Of Languages,” Snyder describes how his program is able to tell—with 99% accuracy—which letters of a long-dead language are consonants and which are vowels. In addition, the paper describes how the program can determine—with 89% accuracy—some of the qualities of the consonants; for example, which are probably nasal sounds and which are not.

“What I wanted to develop was a pro-
gram based on machine-learning tech-
niques that would examine hundreds
of languages and, by doing so, build a
universal model of linguistic plausibil-
ity,” he explains. “What I’ve done so far
is just the starting point; I hypothesize
that, in time, we’ll be able to determine
much more with the software.”
Unlike the reconstruction program
created by Dan Klein’s team, which
acts to supplement the manual work
of linguistics by making it simpler and
more efficient, Snyder says what his de-
cipherment software “goes way beyond
what humans are able to do through
manual analysis.”
While Snyder does not see software
replacing more-traditional manual
linguistic methods completely, he
suspects the field will undergo a shift
toward greater use of computational
methods, given the amount of data
that is being accumulated.

“This is a really nice example of
analyzing large amounts of data using
novel algorithmic techniques,” he says,
“and a great example of computer sci-
ence being the new handmaiden of the
sciences, the way math once was.”
Why would any linguistic analysis
continue to be done by hand, when
computational techniques seem to be
so much faster and easier?

Kevin Knight believes that humans
are simply better at finding new data
changing in similar ways, explains
Klein, and so patterns are left. The trick
is to identify those patterns of change
and then to “reverse them,” basically
tracking the evolution of the language
backward in time.

“Linguists have known this for a
good 100 years or more, but it’s a hard
and time-consuming process to do by
hand,” he says. “However, that is where
computers shine.”
Yet the use of computers and lin-
guistic software is not limited to recon-
structing ancient languages.

Ben Snyder, an assistant professor in the Department of Computer Sciences at the University of Wisconsin– Madison, employs his own software to do decipherment of some sort of text— perhaps a tablet—written in a long-dead language that may or may not be related to a living language. He then tries to reconstruct the dead language, making a prediction about that language for which he has no direct evidence.

In his most recent work, he developed a software program into which he is able to feed an unknown language not necessarily connected to any other language. The program is then able—in about 30 seconds—to “say something useful” about the language, says Snyder.

For instance, in a paper for this year’s Association of Computational

 

newly incorporated acm Europe recently aired its support for a report that identifies software as a Key Enabling Technology (KET), and urged its implementation as part of the horizon 2020 strategy to secure Europe’s global competitiveness.

The report, “Software
Technologies: The missing
Key Enabling Technology,”
was issued by the European
commission’s Information
Society Technology advisory
Group (ISTaG) in 2012 in
response to two Ec studies that
purported to identify KE Ts “that
strengthen the EU’s industrial
and innovation capacity to
address the societal challenges
ahead and proposes a set of
measures to improve the related
framework conditions.”
The 2009 Ec communication
“Preparing for Our future:
Developing a common strategy
for key enabling technologies in
the EU,” and the Ec’s June 2011
final report of the high-Level
Expert Group on Key Enabling
Technologies, both identify
six enabling technologies that
“underpin innovation in many
strategic sectors and play a key
role in making new products
and services affordable for the
population at large:”

˲ nanotechnology

˲ micro and nanotechnology

˲ Industrial biotechnology

˲ Photonics

˲ advanced materials

˲ advanced manufacturing sys-
tems
The 2012 ISTaG report says
software “is a key driver for
the European economy,” and
suggests a “Strategic agenda for
Software Technologies in Europe
should be created in cooperation
with industry, academia and
public sector.”
acm Europe chair fabrizio
Gagliardi says, “all six KETs
identified in the previous studies
rely on software. The creation of
the KETs, and the engineering,
production, and distribution of
new products based on them, all
require software.”
a letter to Ec chief scientific
officer anne Glover by Gagliardi,
acm Europe vice-chair and
former acm president Wendy
hall, and acm Europe council
member and acm vice president
alexander Wolf, notes, “It would
be a serious missed opportunity
if the considerable funding that
horizon 2020 will provide to
European researchers ended up
most directed at supporting only
applied computing science…
we believe that support for
fundamental research and
education in computing science
should become a priority in
horizon 2020. This is essential
to support the innovation life
cycle in computing, which will
eventually allow major society
challenges of Europe to be
addressed in the years to come.”
With an €80-billion
($106-billion) budget (of which
€ 24. 5 billion/$32.5 billion is
dedicated to strengthening the
EU’s position in science), the
horizon 2020 program will run
2014–2020 as part of a drive to
create growth and jobs in Europe.
—Lawrence Fisher

EU Policy
ACM Europe Backs Software as Enabling Tech

“What i wanted
to develop was a
program ... that would
examine hundreds
of languages and,
by doing so, build a
universal model of
linguistic plausibility.”