Exploring Affect and Metaphor in WordNet for Conversational AI
This study investigates affect and metaphor in WordNet for conversational AI applications. The research delves into metaphorical phenomena and the recognition and analysis components using WordNet-based techniques for understanding metaphoricity signals.
Exploring Affect and Metaphor in WordNet for Conversational AI
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Presentation Transcript
Tim Rumbell, John Barnden (speaking), Mark Lee & Alan Wallington School of Computer Science University of Birmingham Affect in Metaphor: Developments with WordNet
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/sharper. • 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).
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.
Metaphorical Phenomena for this talk • Someone as an animal • “You piglet” • Someone as a supernatural being • “You’re 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”
Examples of Results of Current Implemented System • “You cow” • negativeanimal metaphor • “She's an absolute angel” • positivesupernatural being metaphor • “You are a little rat” • negativeanimal metaphor with added contempt • “You piglet” • negativeanimal metaphor meant affectionately • “He is an elephant” • positive-or-negativeanimal metaphor • “He's a rock” • positivenatural object metaphor (NEW) • “She’s a bit of a bag” • negativeartefact metaphor (NEW)
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 noun’s modifiers
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”
Little: If negative metaphor: Contempt added to evaluation If positive metaphor ORaffection already added (= through baby animal metaphor): (Extra) Affection added to evaluation Size Adjectives • Big: • Emphasis added to existing metaphorical evaluation
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.
