CRII: RI: What do you mean? -- Automatic identification of inferences drawn from text

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When dealing with language, readers/hearers understand far more than only the literal meaning of the words they read/hear. For instance, if your son's teacher tells you "I doubt your son will pass his exam", you will infer that your son will probably fail the exam. This project investigates how to automatically derive systematic inferences that people commonly draw. To approximate human understanding, it is essential for natural language processing (NLP) systems to develop broad-coverage models that capture what is conveyed in language without being explicitly said. The project focuses on how events are perceived: in the example above, do people believe that the son will pass? Accurately identifying events that are agreed upon and taken as facts has implications for any NLP task that require an accurate inference process, such as in information extraction. The outcomes of the project consist of a better grasp of how linguistic insights can be used to automatically approximate human-level understanding, and publicly available data that will serve to develop robust, broad-coverage NLP systems as well as to evaluate and sharpen linguistic theories. The goal of automatically deriving common inferences is pursued by developing classifiers that bring in, as features, linguistic insights studied in semantics and pragmatics, and by constructing a dataset of naturally occurring examples, from different genres, annotated with humans' intuitions via crowdsourcing techniques. A large body of work in NLP is recently focusing on the power of "surface" features for NLP tasks. But such features are reaching a limit. This project demonstrates how specialized linguistic features go beyond what can be approximated with surface-level information given available data and leads to fundamental advances in NLP systems. Independently of performance on NLP tasks, semantic and pragmatic features are of interest from a theoretical linguistics perspective. By quantitatively studying the interactions of linguistic features on a large amount of naturally-occurring examples, this project has an impact not only for NLP but also for semantic and pragmatic theories.