Hypernyms Through Intra-Article Organization in Wikipedia
Shrivastava, Disha, Kenkre, Sreyash, Penubothula, Santosh
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Sep-2-2018
- Country:
- North America
- United States
- Michigan (0.04)
- Colorado > Denver County
- Denver (0.04)
- Canada
- Quebec > Montreal (0.05)
- British Columbia > Metro Vancouver Regional District
- Vancouver (0.04)
- United States
- Europe
- Slovenia (0.04)
- Germany > Berlin (0.04)
- United Kingdom > Scotland
- City of Edinburgh > Edinburgh (0.04)
- Switzerland > Geneva
- Geneva (0.04)
- Sweden > Vaestra Goetaland
- Gothenburg (0.04)
- Spain > Valencian Community
- Valencia Province > Valencia (0.04)
- Ireland > Leinster
- County Dublin > Dublin (0.05)
- France > Pays de la Loire
- Loire-Atlantique > Nantes (0.04)
- Asia > India
- North America
- Genre:
- Research Report (0.40)
- Technology: