Image Processing Reference
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people listening it, often producing a funny effect Mihalcea et al. (2006). This module removes
punctuation marks and stopwords (i.e. word that do not carry any meaning) from the
sentence, and then analyzes its phonetic transcription, obtained by using the CMU dictionary
CMU (2010). This technique is aimed at discovering possible repetitions of the beginning
phonemes in subsequent words. In particular the module searches the presence of at least
three words have in common the first one, the first two or the first three phonemes.
As an example the module consider the following humorous sentences:
Veni, Vidi, Visa: I came, I saw, I did a little shopping
Infants don't enjoy infancy like adults do adultery
detecting in the first sentence three words having the first phoneme in common, and in the
second sentence two pairs of words having the first three phonemes in common. The words
infancy and infants have the same following initial phonemes IH1 N F AH0 N while the words
adultery and adults begin with the following phonemes AH0 D AH1 L T .
3.2.2 Antinomy recognition module
This module detects the presence of antinomies in a sentence has been developed exploiting
the lexical dictionary WordNet. In particular the module searches into a sentence for:
• a direct antinomy relation among nouns, verbs, adverbs and adjectives;
• an extended antinomy relation, which is an antinomy relation between a word and a
synonym of its antonym. The relation is restricted to the adjectives;
• an indirect antinomy relation, which is an antinomy relation between a word and an
antonym of its synonym. The relation is restricted to the adjectives.
These humorous sentences contain antinomy relation:
A clean desk is a sign of a cluttered desk drawer
Artificial intelligence usually beats real stupidity
3.2.3 Adult slang recognition module
This module analyzes the presence of adult slang searching in a set of pre-classified words.
As an example the following sentences are reported:
The sex was so good that even the neighbors had a cigarette
Artificial Insemination: procreation without recreation
3.3 Humor evocation area
This area allows the chatbot to evocate funny sentences that are not directly coded as
AIML categories, but that are encoded as vectors in a semantic space, created by means
of Latent Semantic Analysis (LSA) Dumais & Landauer (1997). In fact, if none of the
features characterizing a humorous phrase is recognized in the sentence through the humor
recognition area, the user question is mapped in a semantic space. The humor evocation area
then computes the semantic similarity between what is said by the user and the sentences
encoded in the semantic space; subsequently it tries to answer to the user with a funny
expression which is conceptually close to the user input. This procedure allows to go beyond
the rigid pattern-matching rules, generating the funniest answers which best semantically fit
the user query.
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