Statistical and Machine Learning Approaches for Network Analysis

11.2 Related Work

Hyponym harvesting has attracted a lot of interest. A large number of approaches were developed so far. The approaches can be divided into pattern-based, kernel-based, and document clustering-based [17] methods. A typical text pattern consists of a sequence of words or sentence marks and two placeholder variables labeled with hypernym or hyponym. An example of such a pattern is the Hearst pattern [18], “hyponym and other hypernym.” If matched to the sentence The secretary and other politicians criticized the law, the placeholder variable hyponym would be assigned to secretary, the variable hypernym to politician, and therefore the correct hyponymy relation “secretary is a hyponym of politician” is extracted. Such a surface-based approach is easy to realize and also quite limited. It fails for instance if an additional subclause is inserted, for example, The secretary and, according to our information, a lot of other politicians criticized the law. In this case, the given surface pattern can no longer be used for the extraction of the above-mentioned relation. This problem can be overcome by employing graph-based representations such as dependency trees. The patterns of such an approach are given by dependency subtrees. An approach to learn these patterns automatically was devised by Snow et al. [15]. For that, the path in the dependency tree is extracted, which connects the corresponding nouns with each other. To account for certain keywords indicating a hyponym ...