Many techniques in modern computational linguistics and natural language processing (NLP) make the assumption that approaches that work well on English and other widely used European (and sometimes Asian) languages are “language agnostic” – that is that they will also work across the typologically diverse languages of the world. In high-resource languages, especially those that are analytic rather than synthetic, a common approach is to treat morphologically-distinct variants of a common root (such as dog and dogs) as completely independent word types. Doing so relies on two main assumptions: that there exist a limited number of morphological inflections for anymore »
Learning general event schemas with episodic logic
We present a system for learning generalized, stereotypical patterns of events—or
“schemas”—from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a “head start” set of protoschemas—schemas that a 1- or 2-year-old child would likely know—we can obtain useful, general world knowledge with very few story examples—often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available.
- Award ID(s):
- 1940981
- Publication Date:
- NSF-PAR ID:
- 10299990
- Journal Name:
- Workshop at NASSLLI 2020, Brandeis University, Waltham MA, July 11-17, 2020.
- Sponsoring Org:
- National Science Foundation
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