Data and models for metonymy resolution

Markert, Katja; Nissim, Malvina

doi:10.1007/s10579-009-9087-y

Cited by 23 publications

(13 citation statements)

References 19 publications

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“…At the same time, the metonymic usage of capital names has a positive correlation with those predicates which require animate subjects (Anim=yes). This claim recalls the statement by Brdar & Brdar-Szabó (2009, p. 238) that "metonymically used names of capitals seem more natural as subject", as well as the finding by Markert and Nissim (2009) that metonymies occur more often in subject position. ID 2.…”

Section: Interpretation Of the Resultssupporting

confidence: 85%

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Variation in the (non)metonymic capital names in Mainland Chinese and Taiwan Chinese

Zhang

Speelman²,

Geeraerts³

2011

MSW

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This paper examines the (non)metonymic usage of capital names in news articles from Mainland Chinese and Taiwan Chinese and shows that this phenomenon is actually more complex than might have been expected. We annotated capital names extracted from a self-built news corpus with insights from previous studies on place name metonymies in Cognitive Linguistics and identified factors that would influence their (non)metonymic usage. To quantitatively explore the data, logistic regression analysis was employed. The statistical results reveal that the variation in the (non)metonymic capital names is a result of an intricate interplay of a number of conceptual, lectal and discursive factors: (1) more metonymic capital names are found in subject than non-subject position and in political than non-political news topics; types of capital may influence their metonymic usage; (2) differences between Mainland Chinese and Taiwan Chinese cannot be ignored, especially for the interpretation of a specific metonymy, i.e. CAPITAL FOR GOVERNMENT; (3) the (non)metonymic usage of a capital name is also determined by its sequencing and location in discourse. We hope this study may shed some light on the usage-based trend of current Cognitive Linguistics, i.e. investigating metonymy in authentic linguistic data by a range of empirical methodologies.

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Section: Interpretation Of the Resultssupporting

confidence: 85%

“…Their research contributes greatly to the (automatic) recognition of metonymy in real-world texts (cf. Markert & Nissim, 2009;, but still leaves open the question of what other factors may influence the distribution of metonymies.…”

Section: Introductionmentioning

confidence: 99%

Variation in the (non)metonymic capital names in Mainland Chinese and Taiwan Chinese

Zhang

Speelman²,

Geeraerts³

2011

MSW

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“…In our experiments, the above simple rule helps avoiding some false negatives but in future any subsequent improvement with a better metonomy resolver (Markert and Nissim, 2009) should improve the performance of our model.…”

Section: Knowledge Of Semantic Classes Of Nounsmentioning

confidence: 84%

Recognizing Causality in Verb-Noun Pairs via Noun and Verb Semantics

Riaz¹,

Gîrju²

2014

Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL)

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Several supervised approaches have been proposed for causality identification by relying on shallow linguistic features. However, such features do not lead to improved performance. Therefore, novel sources of knowledge are required to achieve progress on this problem. In this paper, we propose a model for the recognition of causality in verb-noun pairs by employing additional types of knowledge along with linguistic features. In particular, we focus on identifying and employing semantic classes of nouns and verbs with high tendency to encode cause or non-cause relations. Our model incorporates the information about these classes to minimize errors in predictions made by a basic supervised classifier relying merely on shallow linguistic features. As compared with this basic classifier our model achieves 14.74% (29.57%) improvement in F-score (accuracy), respectively.

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“…For example, the standard referent of the term ‘Wall Street’ is a street in New York City; however, in its metonymical use it refers to the American financial and banking industry, which is located at Wall Street. This example is an instance of regular polysemy (Markert and Nissim 2009; Pustejovsky 1995). As a second example, consider the metaphorical use of the noun ‘eye’ in the expression ‘eye of a needle’, which doesn't refer to an organ of sight (the first sense of ‘eye’ in WordNet), but rather to a small hole (incidentally, the fifth sense of ‘eye’ in WordNet).…”

Section: Constructing Semantic Representationsmentioning

confidence: 99%

A Survey of Computational Semantics: Representation, Inference and Knowledge in Wide‐Coverage Text Understanding

Bos

2011

Language and Linguist. Compass

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The aim of computational semantics is to capture the meaning of natural language expressions in representations suitable for performing inferences, in the service of understanding human language in written or spoken form. First-order logic is a good starting point, both from the representation and inference point of view. But even if one makes the choice of first-order logic as representation language, this is not enough: the computational semanticist needs to make further decisions on how to model events, tense, modal contexts, anaphora and plural entities. Semantic representations are usually built on top of a syntactic analysis, using unification, techniques from the lambda-calculus or linear logic, to do the book-keeping of variable naming. Inference has many potential applications in computational semantics. One way to implement inference is using algorithms from automated deduction dedicated to first-order logic, such as theorem proving and model building. Theorem proving can help in finding contradictions or checking for new information. Finite model building can be seen as a complementary inference task to theorem proving, and it often makes sense to use both procedures in parallel. The models produced by model generators for texts not only show that the text is contradiction-free; they also can be used for disambiguation tasks and linking interpretation with the real world. To make interesting inferences, often additional background knowledge is required (not expressed in the analysed text or speech parts). This can be derived (and turned into first-order logic) from raw text, semistructured databases or large-scale lexical databases such as WordNet. Promising future research directions of computational semantics are investigating alternative representation and inference methods (using weaker variants of first-order logic, reasoning with defaults), and developing evaluation methods measuring the semantic adequacy of systems and formalisms.

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Data and models for metonymy resolution

Cited by 23 publications

References 19 publications

Variation in the (non)metonymic capital names in Mainland Chinese and Taiwan Chinese

Variation in the (non)metonymic capital names in Mainland Chinese and Taiwan Chinese

Recognizing Causality in Verb-Noun Pairs via Noun and Verb Semantics

A Survey of Computational Semantics: Representation, Inference and Knowledge in Wide‐Coverage Text Understanding

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