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Automatic Metaphor Interpretation as a Paraphrasing Task

Automatic Metaphor Interpretation as a Paraphrasing Task. Ekaterina Shutova Computer Lab, University of Cambridge NAACL 2010. Outline. What is metaphor The idea and overview of the system Experimental data Method Evaluation Conclusion. Metaphor in this paper.

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Automatic Metaphor Interpretation as a Paraphrasing Task

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  1. Automatic Metaphor Interpretation as a Paraphrasing Task Ekaterina Shutova Computer Lab, University of Cambridge NAACL 2010

  2. Outline • What is metaphor • The idea and overview of the system • Experimental data • Method • Evaluation • Conclusion

  3. Metaphor in this paper • Metaphor arise when one concept is viewed in terms of the properties of the other. • News travels fast. • How can I kill the process? • And then my heart with pleasure fills, And dances with the daffodils.

  4. Automatic processing of metaphor • Metaphor recognition • Distinguishing between literal and metaphorical language in text. • Metaphor interpretation • Indentifying the intended literal meaning of a metaphorical expression.

  5. Interpretation as paraphrasing • Phrases • All of this stirred and uncontrollable excitement in her. • A carelessly leaked report. • Their paraphrases • All of this provoked an uncontrollable excitement in her. • A carelessly disclosed report.

  6. System overview • Produces a list of all possible paraphrases for a metaphorical expression. • Ranks the paraphrases according to their likelihood derived from the corpus. • Discriminates between literal and figurative paraphrases by detecting selectional preference violation and output the literal ones. • Disambiguates the sense of the paraphrases using WordNetinventory of senses.

  7. Experimental data • Focused on single-word metaphors expressed by a verb. • Classification according to whether the verbs are used metaphorically or literally. • Some verbs have weak or no potential of being a metaphor are excluded. • Auxiliary verbs • Model verbs • Aspectual verbs • Light verbs

  8. Dataset • The corpus is a subset of the British National Corpus (BNC) that contains 761 sentences and 13642 words. • Annotated based on the principles of the metaphor identification procedure (MIP). • A form of word sense disambiguation with an emphasis on metaphoricity.

  9. Annotation • Discriminate between verbs used metaphorically and literally. • For each verb establish its meaning in context and try to imagine a more basic meaning of this verb on other contexts. • More concrete • Related to bodily action • More precise (as opposed to vague) • Historically older • If you can establish the basic meaning that is distinct from the meaning of the verb in this context, the verb is likely to be used metaphorically.

  10. Example • If he asked her to post a letter or buy some razor blades from the chemist, she was transported while pleasure. • The verb transport in its basic sense is used in the context of “goods being transported/carried by a vehicle”.

  11. Phrase selection • Only the phrases that were tagged as metaphorical by all the annotators were included in the test set. • memories were slipping away. • hold the truth back. • factors shape results • Some phrases are excluded from the test set • Subject or object referent is unknown. • Metaphorical meaning is realized solely in passive constructions. • Subject or object of interest are represented by a named entity. • Multiword metaphors. • The resulting test set contains 62 metaphorical expression.

  12. Paraphrase ranking • The likelihood of a particular paraphrase as a joint probability • Carelessly [w1] leaked [i] report [w2]

  13. Paraphrase ranking • Rank the possible replacements of the term used metaphorically in the fixed context according to the data by the likelihood.

  14. Filter the paraphrases • Filter out the terms whose meaning does not share any common features with that of the metaphorical term. • Kill a process <-> Terminate a process • Kill implies an end or termination of life • Identifying shared meanings using hyponymy relations in the WordNet taxonomy.

  15. Filtering based on selectional preferences • Remove the irrelevant paraphrases and paraphrases where the substitute is used metaphorically again. • Suppress the truth. • The verbs used metaphorically are likely to demonstrate strong semantic preference for the source domain than for the target domain. • Suppress would select for movements (political) rather than ideas or truth (target domain).

  16. Association measure • Resnik (1997) models selectional preference of a verb in probabilistic terms as the difference between the posterior distribution of noun classes in a particular relation with the verb and their prior distribution using the relative entropy (Kullback-Leibler distance):

  17. Evaluation • 7 annotators were asked to mark the ones that have the same meaning as the term used metaphorically and are used literally in the context of the paraphrase expression as correct. • Agreement is 0.62 (k) • Accuracy measure the proportion of correct literal interpretations among the paraphrase in rank 1.

  18. Conclusion • A system that produces literal paraphrases for metaphorical expressions. • Directly transferable to other applications that can benefic from a metaphor processing component. • Does not rely on any hand-crafted knowledge other than WordNet.

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