The WordNet database contains all sorts of interesting relationships between words: it can categorize words into hierarchies, find the parts of an object, and answer many other interesting questions.
What, exactly, is a dog? It's a domestic animal and a carnivore, not to mention a physical entity (as opposed to an abstract entity, such as an idea). WordNet knows all these facts:
How do we generate this image? First, we look up the first entry for "dog" in WordNet. This returns a "synset", or a set of words with equivalent meanings.
dog = wn.synset('dog.n.01')
Next, we compute the transitive closure of the hypernym relationship, or (in English) we look for all the categories to which "dog" belongs, and all the categories to which those categories belong, recursively:
graph = closure_graph(dog, lambda s: s.hypernyms())
After that, we just pass the resulting graph to NetworkX for display:
closure_graph function repeatedly calls
fn on the supplied symset, and uses the result to build a NetworkX graph. This code goes at the top of the file, so you can use
nx in your own code.
from nltk.corpus import wordnet as wn import networkx as nx def closure_graph(synset, fn): seen = set() graph = nx.DiGraph() def recurse(s): if not s in seen: seen.add(s) graph.add_node(s.name) for s1 in fn(s): graph.add_node(s1.name) graph.add_edge(s.name, s1.name) recurse(s1) recurse(synset) return graph
By using a high-quality graph library, we make it much easier to merge, analyze and display our graphs.
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Types of running, generated with