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Abstract
Advances in statistical and machine learning approaches to natural-language analysis have yielded a wealth of methods and applications in information retrieval, speech recognition, machine translation, and information extraction. Yet, even as we enjoy these advances, we recognize that our successes are to a large extent the result of clever exploitation of redundancy in language structure and use, allowing our algorithms to eke out a few useful bits that we can put to work in applications. By focusing on applications that extract a limited amount of information from the text, finer structures such as word order or syntactic structure could be largely ignored in information retrieval or speech recognition. However, by leaving out those finer details, our language-processing systems have been stuck in an “idiot savant” stage where they can find everything but cannot understand anything. The main language processing challenge of the coming decade is to create robust, accurate, efficient methods that learn to understand the main entities and concepts discussed in any text, and the main claims made. That will enable our systems to answer questions more precisely, to verify and update knowledge bases, and to trace arguments for and against claims throughout the written record. I will argue with examples from our recent research that we need deeper levels of linguistic analysis to do this. But I will also argue that it is possible to do much that is useful even with our very partial understanding of linguistic and computational semantics, by taking (again) advantage of distributional regularities and redundancy in large text collections to learn effective analysis and understanding rules. Thus low-pass semantics: our scientific knowledge is very far from being able to map the full spectrum of meaning, but by combining signals from the whole Web, our systems are learning to read the simplest factual information reliably.
Biography
Fernando Pereira is research director at Google. His previous positions include chair of the Computer and Information Science department of the University of Pennsylvania, head of the Machine Learning and Information Retrieval department at AT&T Labs, and research and management positions at SRI International. He received a Ph.D. in Artificial Intelligence from the University of Edinburgh in 1982, and he has over 120 research publications on computational linguistics, machine learning, bioinformatics, speech recognition, and logic programming, as well as several patents. He was elected AAAI Fellow in 1991 for contributions to computational linguistics and logic programming, and ACM Fellow in 2010 for contributions to machine-learning models of natural language and biological sequences. He was president of the Association for Computational Linguistics in 1993.