Affect in Metaphor: Developments with WordNet

1
Affect in Metaphor Developments with WordNet

• Tim Rumbell, John Barnden (speaking),
• Mark Lee Alan Wallington
• School of Computer Science
• University of Birmingham

2
Initial Motivating Context

• Online affect detection from text by an automated
  conversational agent, in contexts where
  considerable inaccuracy is tolerable.
• But the intentions are broader/deeper/higher/sharp
  er.
• Relatively shallow techniques used (though
  involving parsing and some semantic analysis)
• but intended to be consistent with our deeper
  theory of metaphor understanding (ATT-Meta).

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Metaphor and Affect

• Affect and metaphor are important to each other
• Affect is often conveyed/described metaphorically
• Metaphor is often affective
• Emotion states are often described metaphorically
• He was boiling inside not discussed here
• Affect of metaphorical source terms typically
  carries over
• My son's room is a bomb site
• this phenomenon underlies aspects of the present
  talk
• Both phenomena are important aspects of ATT-Meta
  approach.

4
Metaphorical Phenomena for this talk

• Someone as an animal
• You piglet
• Someone as a supernatural being
• Youre an angel
• Someone as a special type of human
• Lisa is such a baby
• This case not yet addressed
• Metaphorical use of size adjectives
• You big/little bully, Mike is a little rat

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Examples of Results of Current Implemented System

• You cow
• negative animal metaphor
• She's an absolute angel
• positive supernatural being metaphor
• You are a little rat
• negative animal metaphor with added contempt
• You piglet
• negative animal metaphor meant affectionately
• He is an elephant
• positive-or-negative animal metaphor
• He's a rock
• positive natural object metaphor (NEW)
• Shes a bit of a bag
• negative artefact metaphor (NEW)

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The Recognition Component

• Heuristic metaphoricity signals looked for
• X is/are Y
• You Y
• 'looks like', 'a bit of a ', 'such a'
• Signals detected using the RASP robust parser
  (with some post-processing)
• Information extracted
• X (pro)noun
• Y noun
• Y nouns modifiers

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WordNet-based Analysis Component

• Animal
• Chordate
• Vertebrate
• Mammal
• Ungulate
• Swine

• Person
• Unwelcome person
• Unpleasant person
• Selfish person
• (a person who is unusually selfish)?

• Person
• Unwelcome person
• Unpleasant person
• (a person who is not pleasant or agreeable)?
• Vulgarian
• (a vulgar person)?

• Pig (domestic swine)?

• Pig (a coarse obnoxious person)?

• Pig (a person regarded as greedy and pig-like)?

• Piglet (a young pig)?

You piglet
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Size Adjectives

• Big
• Emphasis added to existing metaphorical evaluation

• Little
• If negative metaphor
• Contempt added to evaluation
• If positive metaphor OR affection already added
  ( through baby animal metaphor)
• (Extra) Affection added to evaluation

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Problems and Ongoing/Future Work

• Only searching for individual words in WN
  glosses no parsing etc. of them yet.
• Positive/negative feature counting is simplistic!
• For non-WN-metaphorical animals etc. affective
  carry-over needs more sophisticated
  affective-feature selection than our current one.
• Go beyond metaphorical animals and supernatural
  beings (and newly natural objects and
  artefacts). In particular, add special types of
  human (baby, freak, lunatic, etc.).
• Improve/generalize the size-adjective processing.
• Integrate processing with ATT-Meta system.