Hypernyms Through Intra-Article Organization in Wikipedia
This addresses hypernym detection for natural language processing applications, but it is incremental as it builds on existing unsupervised approaches.
The authors tackled unsupervised hypernym detection by proposing a computationally light method based on word locations and section titles in Wikipedia articles, achieving results comparable to the best unsupervised measures in terms of average precision.
We introduce a new measure for unsupervised hypernym detection and directionality. The motivation is to keep the measure computationally light and portatable across languages. We show that the relative physical location of words in explanatory articles captures the directionality property. Further, the phrases in section titles of articles about the word, capture the semantic similarity needed for hypernym detection task. We experimentally show that the combination of features coming from these two simple measures suffices to produce results comparable with the best unsupervised measures in terms of the average precision.