Metonymy Resolution as a Classification Task

@inproceedings{Markert2002MetonymyRA,
  title={Metonymy Resolution as a Classification Task},
  author={Katja Markert and Malvina Nissim},
  booktitle={EMNLP},
  year={2002}
}
We reformulate metonymy resolution as a classification task. This is motivated by the regularity of metonymic readings and makes general classification and word sense disambiguation methods available for metonymy resolution. We then present a case study for location names, presenting both a corpus of location names annotated for metonymy as well as experiments with a supervised classification algorithm on this corpus. We especially explore the contribution of features used in word sense… 
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52 Citations
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7
Background Citations
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Methods Citations
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Results Citations
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52 Citations

Syntactic Features and Word Similarity for Supervised Metonymy Resolution
TLDR
It is shown that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparseness, which is partially overcome by integrating a thesaurus and introducing simpler grammatical features, thereby preserving precision and increasing recall.
Data and models for metonymy resolution
TLDR
The paper motivates the linguistic principles of data sampling and annotation and shows the task’s feasibility via human agreement and the first shared task for figurative language resolution, which was organised within SemEval-2007 and focused on metonymy.
XRCE-M: A Hybrid System for Named Entity Metonymy Resolution
TLDR
A hybrid system based on the use of a robust parser that extracts deep syntactic relations combined with a non supervised distributional approach, also relying on the relations extracted by the parser.
Combining Collocations, Lexical and Encyclopedic Knowledge for Metonymy Resolution
TLDR
A commonly used feature set with features extracted based on collocation information from corpora is enhanced using lexical and encyclopedic knowledge to determine the preferred sense of the potentially metonymic word using methods from unsupervised word sense disambiguation.
A Large Harvested Corpus of Location Metonymy
TLDR
This work proposes a new, labelled, high-quality corpus of location metonymy called WiMCor, which is large in size and has high coverage, and uses different labels of varying granularity to annotate the corpus.
Whats in a Name? The Automatic Recognition of Metonymical Location Names
TLDR
This paper investigates two approaches to metonymy recognition that dispense with this complexity, albeit in different ways, by replacing the complexity of current algorithms by a ‘lazy’ learning phase.
Metonymic Proper Names : A Corpus-based Account
TLDR
A general framework for annotating metonymies in domainindependent text that considers the regularity, productivity and underspecification of metonymic usage is described and a gold standard corpus consisting of 4000 annotated occurrences of location and organisation names in the British National Corpus is presented.
Unsupervised approaches to metonymy recognition
TLDR
Although the investigated technique, Schütze’s (1998) algorithm, enjoys considerable popularity in Word Sense Disambiguation, it is shown that it is not yet robust enough to tackle the specific case of metonymy recognition.
On metonymy recognition for geographic IR
TLDR
Evaluation results indicate that, using a large annotated corpus of location names, a classifier based on shallow features achieves adequate performance, and removing metonymic senses from a database index yields a higher performance for GIR.
Impact of Target Word and Context on End-to-End Metonymy Detection
TLDR
It is shown that the target word is less useful for detecting metonymy in the authors' dataset and the entity types that are associated with domain-specific words in their context are easier to solve, which shows that the context words are much more relevant for detecting meetonymy.
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