Sentence examples for kernel sentence from inspiring English sources
The phrase "kernel sentence" is correct and usable in written English
It is typically used in linguistic contexts to refer to a basic, simple sentence that can be expanded with additional elements. Example: "In transformational grammar, a kernel sentence serves as the foundation for more complex sentence structures."
For example, "The workers rejected the ultimatum" is a kernel sentence that may be transformed into the nonkernel sentences "The ultimatum was rejected by the workers" or "Did the workers reject the ultimatum?" Each of these may be described as a transform of the kernel sentence from which it is derived.
Experimental work in the early 1960s appeared to show that nonkernel sentences took longer to process than kernel sentences and, even more interestingly, that the processing time increased proportionately with the number of optional transformations involved.
Harris distinguished within the total set of grammatical sentences in a particular language (for example, English) two complementary subsets: kernel sentences (the set of kernel sentences being described as the kernel of the grammar) and nonkernel sentences.
The shallow linguistic kernel recalled 70.1% of the sentence-level connectivity statements at 50.3% precision.
The rule- and co-occurrence-based methods can operate at the abstract or sentence level, whereas the kernel methods are limited to single sentences.
For any sentence p, a p-kernel is a minimal p-implying set, i.e. a set that implies p but has no proper subset that implies p. A contraction operation ÷ can be based on the simple principle that no p-kernel should be included in A÷p.
Bunescu and Mooney (2005) developed a machine learning approach that explores a sequence kernel measuring the similarity of two sentences by evaluating the common string sequences that they contain.
We have presented and evaluated a supervised machine learning approach using a support vector machine exploiting a linear kernel for two tasks, namely abbreviation and sentence delineation in German-language medical narratives.
For the text genre under scrutiny we showed that support vector machines exploiting a linear kernel produce state of the art results for sentence boundary detection.
(This sentence struck somebody as such a profound kernel of wisdom that it's uttered twice.) And what about Mr. Tykwer's destiny?
Each sentence became an input instance for the kernel methods (including article title).
