In this paper, we present three approaches to automatic ranking of relevant verb phrases extracted from historical text. These approaches are based on conditional probability, log likelihood ratio, and bagof-words classification respectively. The aim of the ranking in our study is to present verb phrases that have a high probability of describing work at the top of the results list, but the methods are likely to be applicable to other information needs as well. The results are evaluated by use of three different evaluation metrics: precision at k, R-precision, and average precision. In the best setting, 91 out of the top-100 instances in the list are true positives.
['Eva Pettersson', 'Beata Megyesi', 'Joakim Nivre']
Ranking Relevant Verb Phrases Extracted from Historical Text