例句與造句

1. Approaches range from applying SVM with kernel methods to semantic role labeling ( SRL ) to deep semantic parsing techniques.
2. Many automatic semantic role labeling systems have used PropBank as a training dataset to learn how to annotate new sentences automatically.
3. Due to such differences, semantic role labeling with respect to PropBank is often a somewhat easier task than producing FrameNet-style annotations.
4. From the start, PropBank was developed with the idea of serving as training data for machine learning-based semantic role labeling systems in mind.
5. FrameNet has been used in applications like question answering, paraphrasing, recognizing textual entailment, and information extraction, either directly or by means of Semantic Role Labeling tools.
6. It's difficult to find semantic role labeling in a sentence. 用semantic role labeling造句挺難的
7. "' Semantic role labeling "', sometimes also called shallow semantic parsing, is a task in natural language processing consisting of the detection of the semantic arguments associated with the roles.
8. Daniel Gildea ( University of California, Berkeley / International Computer Science Institute ) and Daniel Jurafsky ( currently teaching at Stanford University, but previously working at University of Colorado and UC Berkeley ) developed the first automatic semantic role labeling system based on FrameNet.
9. They have evolved to investigate the interrelationships among the elements in a sentence ( e . g ., semantic role labeling ), relations between sentences ( e . g ., coreference ), and the nature of what we are saying ( semantic relations and sentiment analysis ).
10. The first automatic system for Semantic Role Labeling ( SRL, sometimes also referred to as " shallow semantic parsing " ) was developed by Daniel Gildea and Daniel Jurafsky based on FrameNet in 2002, and Semantic Role Labelling has since become one of the standard tasks in natural language processing.
11. The first automatic system for Semantic Role Labeling ( SRL, sometimes also referred to as " shallow semantic parsing " ) was developed by Daniel Gildea and Daniel Jurafsky to automate the FrameNet annotation process in 2002, and Semantic Role Labelling has since become one of the standard tasks in natural language processing.
12. The objective of the competition is to organize different lectures, preparing and hand-annotating corpus for testing systems, perform a comparative evaluation of WSD systems in several kinds of tasks, including all-words and lexical sample WSD for different languages, and, more recently, new tasks such as semantic role labeling, gloss WSD, lexical substitution, etc . The systems submitted for evaluation to these competitions usually integrate different techniques and often combine supervised and knowledge-based methods ( especially for avoiding bad performance in lack of training examples ).