A two-stage approach for context-dependent hypernym extraction

Abstract

In this paper we present an unsupervised method to deal with the classification of out-of-vocabulary words in open-domain spoken dialog systems. This classification is vital to ameliorate the human-computer interaction and to be able to extract additional information, which can be presented to the user. We propose a two-stage approach for interpreting named entities in a document corpus: to cluster documents dealing with a particular named entity and to classify it with the help of structural and contextual information in these documents. The idea is to take the resulting websites from a search engine queried for a named entity as documents and to cluster those which are semantically similar. Named entities can then be classified with the information contained in the clusters. Our evaluation showed that the precision of the classification task was as high as 64.47%.