How Large Are Lions? Inducing Distributions over Quantitative Attributes
This addresses the need for better quantitative attribute knowledge in NLP, offering a novel resource for tasks like relative comparisons, though it is incremental as it builds on prior work in this area.
The authors tackled the problem of NLP systems lacking knowledge about quantitative attributes by proposing an unsupervised method to collect quantitative information from web data, creating a large resource called Distributions over Quantitative (DoQ), which achieved favorable results compared to state-of-the-art methods on existing and new datasets.
Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as "Is a lion bigger than a wolf?". Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.