When Will My Computer Understand Me?

Linguists, computer scientists use TACC supercomputers to improve natural language processing

It’s not hard to tell the difference between the “charge” of a battery and criminal “charges.” But for computers, distinguishing between the various meanings of a word is difficult.

For more than 50 years, linguists and computer scientists have tried to get computers to understand human language by programming semantics as software. Driven initially by efforts to translate Russian scientific texts during the Cold War (and more recently by the value of information retrieval and data analysis tools), these efforts have met with mixed success. IBM’s Jeopardy-winning Watson system and Google Translate are high profile, successful applications of language technologies, but the humorous answers and mistranslations they sometimes produce are evidence of the continuing difficulty of the problem.

Our ability to easily distinguish between multiple word meanings is rooted in a lifetime of experience. Using the context in which a word is used, an intrinsic understanding of syntax and logic, and a sense of the speaker’s intention, we intuit what another person is telling us.

“In the past, people have tried to hand-code all of this knowledge,” explained Katrin Erk, a professor of linguistics at The University of Texas at Austin focusing on lexical semantics. “I think it’s fair to say that this hasn’t been successful. There are just too many little things that humans know.”

Other efforts have tried to use dictionary meanings to train computers to better understand language, but these attempts have also faced obstacles. Dictionaries have their own sense distinctions, which are crystal clear to the dictionary-maker but murky to the dictionary reader. Moreover, no two dictionaries provide the same set of meanings — frustrating, right?

Watching annotators struggle to make sense of conflicting definitions led Erk to try a different tactic. Instead of hard-coding human logic or deciphering dictionaries, why not mine a vast body of texts (which are a reflection of human knowledge) and use the implicit connections between the words to create a weighted map of relationships — a dictionary without a dictionary?

“An intuition for me was that you could visualize the different meanings of a word as points in space,” she said. “You could think of them as sometimes far apart, like a battery charge and criminal charges, and sometimes close together, like criminal charges and accusations (“the newspaper published charges…”). The meaning of a word in a particular context is a point in this space. Then we don’t have to say how many senses a word has. Instead we say: ‘This use of the word is close to this usage in another sentence, but far away from the third use.'”

To create a model that can accurately recreate the intuitive ability to distinguish word meaning requires a lot of text and a lot of analytical horsepower.

“The lower end for this kind of a research is a text collection of 100 million words,” she explained. “If you can give me a few billion words, I’d be much happier. But how can we process all of that information? That’s where supercomputers and Hadoop come in.”

Applying Computational Horsepower

Erk initially conducted her research on desktop computers, but around 2009, she began using the parallel computing systems at the Texas Advanced Computing Center (TACC). Access to a special Hadoop-optimized subsystem on TACC’s Longhorn supercomputer allowed Erk and her collaborators to expand the scope of their research. Hadoop is a software architecture well suited to text analysis and the data mining of unstructured data that can also take advantage of large computer clusters. Computational models that take weeks to run on a desktop computer can run in hours on Longhorn. This opened up new possibilities.

“In a simple case we count how often a word occurs in close proximity to other words. If you’re doing this with one billion words, do you have a couple of days to wait to do the computation? It’s no fun,” Erk said. “With Hadoop on Longhorn, we could get the kind of data that we need to do language processing much faster. That enabled us to use larger amounts of data and develop better models.”

Treating words in a relational, non-fixed way corresponds to emerging psychological notions of how the mind deals with language and concepts in general, according to Erk. Instead of rigid definitions, concepts have “fuzzy boundaries” where the meaning, value and limits of the idea can vary considerably according to the context or conditions. Erk takes this idea of language and recreates a model of it from hundreds of thousands of documents.

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