The Word Net Hierarchy

WordNet synsets correspond to abstract concepts, and they don't always have corresponding words in English. These concepts are linked together in a hierarchy. Some concepts are very general, such as Entity, State, Event; these are called unique beginners or root synsets. Others, such as gas guzzler and hatchback, are much more specific. A small portion of a concept hierarchy is illustrated in Figure 2-8.

Wordnet Hierarchy
Figure 2-8. Fragment of WordNet concept hierarchy: Nodes correspond to synsets; edges indicate the hypernym/hyponym relation, i.e., the relation between superordinate and subordinate concepts.

WordNet makes it easy to navigate between concepts. For example, given a concept like motorcar, we can look at the concepts that are more specific—the (immediate) hyponyms.

>>> motorcar = wn.synset('car.n.Ol') >>> types_of_motorcar = motorcar.hyponyms() >>> types_of_motorcar[26] Synset('ambulance.n.Ol')

>>> sorted([ for synset in types_of_motorcar for lemma in synset.lemmas]) ['Model_T', 'S.U.V.', 'SUV', 'Stanley_Steamer', 'ambulance', 'beach_waggon', 'beach_wagon', 'bus', 'cab', 'compact', 'compact_car', 'convertible', 'coupe', 'cruiser', 'electric', 'electric_automobile', 'electric_car', 'estate_car', 'gas_guzzler', 'hack', 'hardtop', 'hatchback', 'heap', 'horseless_carriage', 'hot-rod', 'hot_rod', 'jalopy', 'jeep', 'landrover', 'limo', 'limousine', 'loaner', 'minicar', 'minivan', 'pace_car', 'patrol_car',

'phaeton', 'police_car', 'police_cruiser', 'prowl_car', 'race_car', 'racer', 'racing_car', 'roadster', 'runabout', 'saloon', 'secondhand_car', 'sedan', 'sport_car', 'sport_utility', 'sport_utility_vehicle', 'sports_car', 'squad_car', 'station_waggon', 'station_wagon', 'stock_car', 'subcompact', 'subcompact_car', 'taxi', 'taxicab', 'tourer', 'touring_car', 'two-seater', 'used-car', 'waggon', 'wagon']

We can also navigate up the hierarchy by visiting hypernyms. Some words have multiple paths, because they can be classified in more than one way. There are two paths between car.n.01 and entity.n.01 because wheeled_vehicle.n.01 can be classified as both a vehicle and a container.

>>> motorcar.hypernyms() [Synset('motor_vehicle.n.01')] >>> paths = motorcar.hypernym_paths() >>> len(paths) 2

['entity.n.01', 'physical_entity.n.01', 'object.n.01', 'whole.n.02', 'artifact.n.01', 'instrumentality.n.03', 'container.n.01', 'wheeled_vehicle.n.01', 'self-propelled_vehicle.n.01', 'motor_vehicle.n.01', 'car.n.01'] >>> [ for synset in paths[1]]

['entity.n.01', 'physical_entity.n.01', 'object.n.01', 'whole.n.02', 'artifact.n.01', 'instrumentality.n.03', 'conveyance.n.03', 'vehicle.n.01', 'wheeled_vehicle.n.01', 'self-propelled_vehicle.n.01', 'motor_vehicle.n.01', 'car.n.01']

We can get the most general hypernyms (or root hypernyms) of a synset as follows:

>>> motorcar.root_hypernyms() [Synset('entity.n.01')]

Your Turn: Try out NLTK's convenient graphical WordNet browser: Explore the WordNet hierarchy by following the hypernym and hyponym links.

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