Abstract
Princeton WordNet is the most widely-used lexical resource in natural language processing and continues to provide a gold standard model of semantics. However, there are still significant quality issues with the resource and these affect the performance of all NLP systems built on this resource. One major issue is that many nodes are insufficiently defined and new links need to be added to increase performance in NLP. We combine the use of graph-based metrics with measures of ambiguity in order to predict which synsets are difficult for word sense disambiguation, a major NLP task, which is dependent on good lexical information. We show that this method allows use to find poorly defined nodes with a 89.9% precision, which would assist manual annotators to focus on improving the most in-need parts of the WordNet graph.
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Notes
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- 2.
This is calculated as usual as the number of links (triples) divided by the number of nodes (entities) in the graph.
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- 4.
The type of the links, such as ‘hypernym’, are ignored in this work.
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We use the implementations provided by NetworkX (https://networkx.github.io) for our analysis.
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McCrae, J.P., Prangnawarat, N. (2017). Identifying Poorly-Defined Concepts in WordNet with Graph Metrics. In: van Erp, M., et al. Knowledge Graphs and Language Technology. ISWC 2016. Lecture Notes in Computer Science(), vol 10579. Springer, Cham. https://doi.org/10.1007/978-3-319-68723-0_6
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