Метонимияны шешу

We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature …...

For the metonymy resolution task SemEval-2007, the use of a memory-basedat learner to train classifiers for the identifica- tion of metonymic location names is inves- tigated. Metonymy is resolved on …...

Though the GYDER system has achieved the highest accuracy scores for the metonymy resolution shared task SemEval-2007 in all six subtasks, we don’tat consider the results (72.80% accuracy for org, …...

We provide an overview of the metonymy resolution shared task organised within SemEval-2007. We describe the problem, the data provided to participants, and the evaluation measures we used to assess …...

This paper presents a supervised method for resolving metonymies. We enhance a commonly used feature set with fea- tures extracted based on collocation in- formation from corpora, generalized us- ing …...

Computational approaches to metonymy res- olution have focused almost exclusively on the local context, especially the constraints placed on a potentially metonymic word by its grammatical collocates. We expand such …...

AbstractNamed entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly …...

AbstractMetonymy is a figure of speech in which an entity is referred to by another related entity. The existing datasets of metonymy are either too small in size or lack …...

AbstractExisting metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources. In this paper, we propose an end-to-end word-level classification approach based only on …...