Understanding Metonymies in Discourse (2002)

1. Understanding Metonymies in Discourse (2002) Presented by Mike O’Leary Linguistics 580 I: Lexical Ambiguity May 24, 2006

2. Outline • Metonymy definitions, examples and characteristics • Earlier computational approaches to detecting and interpreting metonymy • Markert & Hahn’s criticism of the earlier computational approaches and proposed solutions to these problems • Markert & Hahn’s computational model • Conclusions

3. Metonymy: Definitions A non-literal figure of speech in which the name of one thing is substituted for that of another related to it. (Lakoff & Johnson, 1980) The use of a word or expression as a substitute for something with which it is closely associated. From Greek metonumia ‘change of name’. (Oxford English Dictionary) The use of a single characteristic to identify a more complex entity. It is also known as denominatio or pars pro toto (part for the whole). One of the basic characteristics of cognition. It is extremely common for people to take one well-understood or easy-to-perceive aspect of something and use that aspect to stand either for the thing as a whole or for some other aspect or part of it. (http://en.wikipedia.org/wiki/Metonymy)

4. Metonymy: Examples The ham sandwich is waiting for his check. Nixon bombed Hanoi. Which airlines fly from Boston to Denver? The Boston office called. Denise drank the bottle. The kettle is boiling. You’ll find better ideas than that in the library. John began a novel. Scotland beat Ireland. America did once try to ban alcohol. Ask seat 19 whether he wants to swap. Press-men hoisted their Kodaks. I liked the laser; its printouts were excellent. He read Shakespeare. I’m parked out back. The White House announced a preparedness plan that could include travel limitations should an outbreak of avian flu threaten the United States.

5. Metonymy and Metaphor • Similarities • Relationship is made between two concepts • Non-literal meaning is intended • Disambiguation is required for interpretation • Make use of shared context between speaker and listener • Differences • In metaphor, characteristics of one concept are attributed to the other concept (“the internet is a gold mine”) • In metonymy, no attribution of characteristics is intended; the relationship is made for convenience of reference (“the ham sandwich is waiting for his check”)

6. What is metonymy good for?(Observations and speculations) • Using a metonym saves time (“The ham sandwich….”) • It can be cumbersome to give the full name of what you are referring to (“Which airlines fly from Boston to Denver?“) • You may not know enough to give the full name of what you are referring to (“My computer beeps when I hit the delete key.”) • You can avoid sounding wordy or pedantic (“The kettle is boiling.”) • The metonym may refer to the most salient attribute of an entity in the speaker and listener’s context (“The ham sandwich….”) We use pronouns and acronyms for similar reasons

7. Computational Metonymy Resolution ― Statistics Markert and Hahn cite the following: • “Stallard reports on a 27% performance improvement when metonymy resolution is incorporated into a question-answering system about airline reservations.” • “We found metonymic expressions in 15% of the utterances in a German language corpus of information technology test reports.” • “In a small-scale experiment [using the BNC] we found, e.g., that approximately 50% of 100 randomly drawn occurrences of ‘BMW’ referred metonymically to cars or motorcycles, while the other half referred literally to the company.” • The interdependence of anaphora and metonymy resolution is obvious in everyday language use, e.g., “The treaty has to be signed by the American government. The United States announced….”.

8. Earlier Approaches to Computational Metonymy Resolution • Recognize syntactic and semantic irregularities that are associated with metonyms • Syntactic: feature agreement (“The french fries is getting impatient.”) • Syntactic: phrase structure (“He read the Shakespeare.”) • Semantic: selection restriction violations (“Denise drank the bottle.”)

9. Earlier Approaches (contd.) • Recognize two types of metonymy • Referential: The metonymic noun phrase has an intended referent related to what the metonym literally refers to. (Ex. “The ham sandwich is waiting to pay.” “He (*it) is impatient.” “ham sandwich” <=> “customer”) • Predicative: The intended referent of the metonymic noun phrase is the same as what the metonym literally refers to. (Ex. “Nixon bombed Hanoi.” “He (*they) wanted to force the Communists to negotiate.” “Nixon” <=> “Nixon”)

10. Earlier Approaches (contd.) • Recognize patterns in the relationships between metonyms and the intended referents. • Producer-for-product (“He bought a Ford.”) • Part-for-whole (“I liked the laser….”) • Place-for-institution (“The White House said….”) • Container-for-contents (“….drank the bottle”) • Artist-for-artworks (“He bought a Picasso.”)

11. Earlier Approaches: Metonymy Resolution Algorithm • Prefer literal interpretation first • If a selection restriction violation is detected, consider possible metonymy and attempt resolution as follows: • Determine what types the selection restriction permits • Identify concepts related to the supposed metonym that are of the permitted types • Constrain set of candidate concepts (and the related words and phrases) when possible using known relationship patterns and syntactic and referential/predicative metonymy information • Coerce the metonym to a type permitted by the selection restriction by making it refer to the chosen related concept.

12. Markert and Hahn’s Critique of Earlier Approaches • Preferring literal interpretation first leads to inadequate results under incremental parsing conditions unless expensive backtracking is granted. • Some cases of metonymy do not violate selection restrictions (“He doesn’t like Shakespeare.”) • Some selection restriction violations have nothing to do with metonymy (“The shirt was waiting for him...”) • Some metonyms can only be resolved by taking a broader, multi-sentence context into account (“I saw this butterfly fall. I said to myself: Similar is my destiny. [….] Like this caterpillar I have crawled around in the mud.”)

13. Markert and Hahn’s Approach to Computational Metonymy Resolution • Treat literal and figurative interpretations on an equal basis • Constrain interpretations according to consistency with world knowledge and intrasentential context, e.g., selection restrictions • Prefer interpretations that preserve referential cohesion • Prefer interpretations that conform to known relationship patterns • Prefer interpretations that fulfill aptness conditions

14. Equality of literal and figurative interpretations • Markert and Hahn dispute idea that figurative language represents a violation of communicative norms • They claim that considering literal and figurative interpretations simultaneously is more in line with current research on human parsing behavior • They point to ways in which metonymy resolution can aid in the resolution of literal language phenomena, especially in nominal anaphora resolution

15. Consistency with world knowledge and intrasentential context • Knowledge of relationships between concepts • Selection restriction violations (and related syntactic violations) are taken into account, but literal interpretations are not necessarily favored over metonymic ones when no selection restrictions are violated

16. Referential cohesion • More significant criterion than resolution of selection restriction violations is choosing interpretations that maximize the (intra-sentential and inter-sentential) cohesion among referents (includes anaphora)

17. Known relationship patterns • Make use of producer-for-product, etc. patterns where applicable. Prefer interpretations that fit a known pattern to ones that would require on-the-fly creation of a new pattern

18. Aptness conditions • Which characteristics of an entity will a speaker most likely choose as metonyms? Visible over unseen, context relevant over context irrelevant, short over long, etc.

19. Components of Markert and Hahn’s metonymy resolution system • KL-ONE knowledge representation system with information about the hierarchical and lateral relations between entities in an information technology domain. Defines relationship duals such as “has-part”, “part-of”, etc. • “Path Finder” component and set of relationship path rules for identifying non-cyclic paths between related concepts • “Path Classifier” component and set of interpretation conditions for obtaining and ranking literal and metonymic interpretations

20. Path Finder • Finds all well-formed paths between concepts that correspond to conceptual interpretations of the syntactic relationship between the referents of the concepts. Uses these definitions: • conceptual relatedness (of two concepts) • connectivity (of a series of relations) • cyclicity (of a connective path) • A path is cyclic iff it contains two non-identical relations that are inverses of each other or have an inverse super-relation. Ex. (Printer, Printer-of, Computer-system, Has-Central-Unit Central-Unit) is cyclic because Printer-of is a sub-relation of Part-of and Has-central-unit is a sub-relation of Has-part • Does not distinguish between literal and metonymic paths

21. Path Classifier • Uses predefined path patterns to separate paths into three groups: literal paths, metonymic paths and unclassified paths (not currently able to construct new patterns such as food-for-customer). • Performs metonymic and nominal anaphora resolution in tandem. • Identifies anaphora and prefers interpretations that resolve anaphoric reference. • Ranks interpretations according to degree of “referential cohesion”, in which literal, metonymic and anaphoric references are resolved most satisfactorily.

22. Example “We also tested the printer Epson EPL-5600. I liked the laser, as its printouts were excellent.” Path Finder identifies paths such as (Laser Part-of Laser Printer Has-model Epson EPS-5600),(Epson Has-product Epson EPL-5600), (Epson EPS-5600 Producer-for-product Epson), etc. and forms paths that include ones that resolve metonymic relation between “Epson EPS-5600” and “laser” and anaphoric relation between “its” and “laser” (and ultimately between “its” and “Epson EPS-5600”)

23. Conclusions • Markert and Hahn’s approach resolves metonyms that systems relying solely on repair of intrasentential selection restriction violations do not address • Their approach focuses on treating literal and figurative language on an equal basis and maximizing: • Consistency with world knowledge and intra-sentential context • Referential cohesion • Conformity to known relationship schema • Aptness of referents • Does not handle “logical metonymy” (where a noun stands for the activity described by an absent noun: “John began the book”) or metonymy that depends crucially on certain kinds of world knowledge (e.g., Christopher Marlowe saying “I don’t like Shakespeare”) • Planned future work includes obtaining new metonymic relationship patterns from corpora