Affect in Metaphor: Developments with

WordNet

Tim Rumbell, John Barnden (speaking), Mark Lee & Alan Wallington

School of Computer ScienceUniversity of Birmingham

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”» 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)

“She’s a bit of a bag”» negative artefact 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

“You piglet”

Piglet (a young pig)

Pig (domestic swine)

AnimalChordateVertebrateMammalUngulateSwine

Pig (a coarse obnoxious person)

PersonUnwelcome personUnpleasant person

(a person who is not pleasant or agreeable)

Vulgarian (a vulgar person)

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

PersonUnwelcome personUnpleasant personSelfish person

(a person who is unusually selfish)

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

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.