Image Processing Reference
In-Depth Information
3.3.1 Semantic space creation
A semantic representation of funny sentences has been obtained mapping them in a semantic
space. The semantic space has been built according to a Latent Semantic Analysis (LSA)
based approach described in Agostaro (2005)Agostaro (2006). According to this approach, we
have created a semantic space applying the truncated singular value decomposition (TSVD)
on a m × n co-occurrences matrix obtained analyzing a specific texts corpus, composed of
humorous texts, where each (i, j)-th entry of the matrix represents square root of the number
of times the i-th word appears in the j-th document.
After the decomposition we obtain a representation of words and documents in the reduced
semantic space. Moreover we can automatically encode in the space new items, such as
sentences inserted into AIML categories, humorous sentences and user utterances. In fact,
a vectorial representation can be obtained evaluating the sum of the vectors associated to
words composing each sentence.
To evaluate the similarity between two vectors v i and v j belonging to this space according to
Agostaro et al. we use the following similarity measure Agostaro (2006):
sim v i , v j = ( cos 2 v i , v j if cos v i , v j 0
0
(1)
otherwise
The closer this value is to 1, the higher is the similarity grade. The geometric similarity
measure between two items establishes a semantic relation between them. In particular
given a vector S , associated to a user sentence s, the set CR(s) of vectors sub-symbolically
conceptually related to the sentence s is given by the q vectors of the space whose similarity
measure with respect to S is higher than an experimentally fixed threshold T.
CR(s) = v i |sim(s , v i ) > T with i = 1 . . . q (2)
To each of these vectors will correspond a funny sentence used to build the space. Specific
AIML tags called relatedSentence and randomRelatedSentence allow the chatbot to query the
semantic space to retrieve respectively the semantically closer riddle to the user query or one
of the most conceptually related riddles. Tha chatbot can also improve its own AIML KB
mapping in the evocative area new items like jokes, riddles and so on introduced by the user
during the dialogue.
3.4 Emotional area
This area is suited to the generation of emotional expressions in the Talking Head. Many
possible models of emotions have been proposed in literature. We can distinguish three
different categories of models. The first one includes models describing emotions through
collections of different dimensions (intensity, arousal, valence, unpredictability, potency, ...).
The second one includes models based on the hypothesis that a human being is able to express
only a limited set of primary emotions. All the range of the human emotions should be the
result of the combination of the primary ones. The last category includes mixed models,
according to which an emotion is generated by a mixture of basic emotions parametrized by
a set of dimensions. One of the earlier model of the second category is the model of Plutchik
Ekman (1999). He listed the following primary emotions: acceptance, anger, anticipation,
disgust, joy, fear, sadness, surprise. Thee emotions can be combined to produce secondary
emotions, and in their turn those can be combined to produce ternary emotions. Each emotion
can be characterized by an intensity level. After this pioneering model, many other similar
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