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Biography creation requires the identification of important events in the
life of the individual in question. While there are events such as birth
and death that apply to everyone, most of the other activities tend to be
occupation-specific. Hence, occupation gives important clues as to which
activities should be included in the biography. We present techniques for
automatically identifying which important events apply to the general
population, which ones are occupation-specific, and which ones are
person-specific. We use the extracted information as features for a
multi-class SVM classifier, which is then used to automatically identify
the occupation of a previously unseen individual. We present experiments
involving 189 individuals from ten occupations, and we show that our
approach accurately identifies general and occupation-specific activities
and assigns unseen individuals to the correct occupations. Finally, we
present evidence that our technique can lead to efficient and effective
biography generation relying only on statistical techniques.
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