Day 2 Keynoter: Natural Language Processing Has Huge Potential

NEW YORK (SpeechTEK 2010) --- University of Rochester Professor James Allen yesterday said there is a “huge amount of promise” in using natural language processing (NLP) techniques in expanding the range of speech.

Among the examples cited by Allen, author of Natural Language Understanding and a the John H. Dessauer Chair of Computer Science at the University of Rochester, N.Y., are interactive planning, human-robot interaction, medical advising (as in managing patient medications), and interactive data exploration. “This is the future. It’s what we really want systems to do.”

Today’s systems, and what the technology will be able to do in the future, represent a “dramatic change” from the old days when speech application developers “built everything by hand—slowly and rather painfully,” he said.

“There was a huge revolution in the 1980s” in NLP with the introduction of statistical modeling, Allen said, noting that NLP has gained popularity since then and has achieved up to 97 percent accuracy for parsing parts of speech and speech tagging.

One of the earliest applications for NLP was machine translation, which when it first came out in the 1980s used very complex models and a lot of computational power, according to Allen. Since then, NLP has been used for information extraction, message understanding, sentiment analysis, question answering, summarization, and more.  “There are a wide range of applications, and the field has exploded,” he said.

NLP, he explained, “has a fairly robust ability to abstract meaning from sentiments,” with success rates in the range of 85 percent.

But even today, NLP has its challenges, least of which is customer sentiment, according to Allen. “From a consumer perspective, people find these systems frustrating most of the time,” he said, “and from a developers’ framework, the tasks that can be accomplished are fairly limited.”

He added that one of the problems with current NLP technology is “there’s a lot of off-the-shelf NLP capabilities, but often shallow processing,” something that can be overcome.

NLP can improve modern speech systems by combining them with other technologies, creating opportunities for speech-to-speech translation, spoken dialogue systems, and “more complex systems handling more complex tasks,” Allen stated.

To show what’s possible with NLP systems, Allen demonstrated a medical and a mapping application that he built, both of which received high performance rates.

But in the final analysis, “significant progress has been made in natural language in the last few decades, significant opportunities are ready for adding speech to applications, and robust, mixed initiative dialogue systems are more robust,” he concluded.

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