Again I'll give the Ling 101, computational linguistics for dummies version (as I understand it ...): Selection Preferences assumes that words frequently co-occur with other words that are literally associated with the same semantic domain. For example,
- That ship has sailed the mighty ocean.
- That boat has sailed across lake Erie.
- That captain has sailed many seas.
4. That student sailed through final exams.
It could automatically use the model created from sentences 1-3 above to recognize that the verb sailed occurs with a subject and object not from the SAILING domain, but rather from the STUDENT domain. Then it could use a metaphor mapping component to recognize that HUMANS as MACHINES is an acceptable mapping and thus recognize that #4 might be coherent under a metaphorical interpretation.
Tang et al. rightly point out that matching frequency-based selectional preferences is not the same thing as literal meaning. First, they note that some times, a metaphorical pairing is actually MORE FREQUENT than a litertal pairing. They use some Chinese examples, but I think the English translation makes the point. Take the following two uses of close:
- The plane is close to the tower.
- Opinion are close.
Tang et al.'s solution is a new method they call Semantic Relation Patterns. Their explanation is brief and highly technical, making it a slog to get through, but it hinges on incorporating an existing semantic relations knowledge base, HowNet, and adding a probabalistic model. Note, I had trouble getting the HowNet website to load, but here is a PDF explanation.
How Net is an on-line common-sense knowledge base unveiling inter-conceptual relations and inter-attribute relations of concepts as connoting in Chinese and English bilingual lexicons.
In my quick read the two methods differed only minimally in the crucial ways (namely, they are both lexalist and local). Semantic Relation patterns are still based on lexical semantics and still derived entirely locally. I don't see how SRP would handle this metaphor from my earlier post any better than SP:
Imagine a situation in a biology class where two students, Alger and Miriam, were originally going to be partners for a lab assignment. Then they got into an argument. A third student, Annette, asks Miriam:
- Annette: Are you still going to be lab partners with Alger?
- Miriam: No. That ship has sailed.
I appreciate Tang et al.'s critique of the SP method and their attempt to get beyond it, but I think their methodology fails to make the critical improvements to automatic metaphor recognition that will be crucial to creating a full scale tool that handles real world metaphor.
Xuri Tang, Weiguang Qu, Xiaohe Chen, & Shiwen Yu (2010). Automatic Metaphor Recognition Based on Semantic Relation Patterns International Conference on Asian Language Processing