WordNet

O'Reilly's textbook (hereafter "Whale book") chapter 2.5:

These two sentences have the same meaning even though the last word (motorcar, automobile) is different.

a. Benz is credited with the invention of the motorcar.
b. Benz is credited with the invention of the automobile.

Using WordNet, to check these words are same meaning (synonym).

>>> from nltk.corpus import wordnet as wn
>>> wn.synsets('motorcar')
[Synset('car.n.01')]
>>> wn.synset('car.n.01').lemma_names
['car', 'auto', 'automobile', 'machine', 'motorcar']
>>> 

A word 'motorcar' is in synset 'car.n.01'. Then searching words which are assigned to the same synset. Then the both words are in 'car.n.01'. We can say they are synonym.

>>> wn.synset('car.n.01').definition
'a motor vehicle with four wheels; usually propelled by an internal combustion engine'
>>> wn.synset('car.n.01').examples
['he needs a car to get to work']
>>> wn.synset('car.n.01').lemmas
[Lemma('car.n.01.car'), Lemma('car.n.01.auto'), Lemma('car.n.01.automobile'), Lemma('car.n.01.machine'), Lemma('car.n.01.motorcar')]
>>> wn.lemma('car.n.01.automobile')
Lemma('car.n.01.automobile')
>>> wn.lemma('car.n.01.automobile').synset
Synset('car.n.01')
>>> wn.lemma('car.n.01.automobile').name
'automobile'
>>> 

>>> wn.synsets('car')
[Synset('car.n.01'), Synset('car.n.02'), Synset('car.n.03'), Synset('car.n.04'), Synset('cable_car.n.01')]
>>> for synset in wn.synsets('car'):
...     print synset.lemma_names
... 
['car', 'auto', 'automobile', 'machine', 'motorcar']
['car', 'railcar', 'railway_car', 'railroad_car']
['car', 'gondola']
['car', 'elevator_car']
['cable_car', 'car']
>>>
>>> wn.lemmas('car')
[Lemma('car.n.01.car'), Lemma('car.n.02.car'), Lemma('car.n.03.car'), Lemma('car.n.04.car'), Lemma('cable_car.n.01.car')] 

Finding synonyms for word 'dish'.

>>> wn.lemmas('dish')
[Lemma('dish.n.01.dish'), Lemma('dish.n.02.dish'), Lemma('dish.n.03.dish'), Lemma('smasher.n.02.dish'), Lemma('dish.n.05.dish'), Lemma('cup_of_tea.n.01.dish'), Lemma('serve.v.06.dish'), Lemma('dish.v.02.dish')]
>>> wn.synsets('dish')
[Synset('dish.n.01'), Synset('dish.n.02'), Synset('dish.n.03'), Synset('smasher.n.02'), Synset('dish.n.05'), Synset('cup_of_tea.n.01'), Synset('serve.v.06'), Synset('dish.v.02')]
>>> 

Maybe this way is smarter:

>>> for synset in wn.synsets('dish'):
...     print synset.lemma_names
... 
['dish']
['dish']
['dish', 'dishful']
['smasher', 'stunner', 'knockout', 'beauty', 'ravisher', 'sweetheart', 'peach', 'lulu', 'looker', 'mantrap', 'dish']
['dish', 'dish_aerial', 'dish_antenna', 'saucer']
['cup_of_tea', 'bag', 'dish']
['serve', 'serve_up', 'dish_out', 'dish_up', 'dish']
['dish']
>>> 

Some meanings are new for me. Let's check them.

>>> wn.synset('dish.n.02').definition
'a particular item of prepared food'
>>> wn.synset('dish.n.03').definition
'the quantity that a dish will hold'
>>> wn.synset('smasher.n.02').definition
'a very attractive or seductive looking woman'
>>> wn.synset('smasher.n.02').examples
[]
>>> wn.synset('dish.n.05').definition
'directional antenna consisting of a parabolic reflector for microwave or radio frequency radiation'
>>> wn.synset('cup_of_tea.n.01').definition
'an activity that you like or at which you are superior'
>>> 

hyponyms() is to get lower level words.

>>> motorcar = wn.synset('car.n.01')
>>> types_of_motorcar = motorcar.hyponyms()
>>> types_of_motorcar[26]
Synset('ambulance.n.01')
>>> sorted([lemma.name 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']
>>> 

hypernyms() is to get higher level words.

>>> motorcar.hypernyms()
[Synset('motor_vehicle.n.01')]
>>> paths = motorcar.hypernym_paths()
>>> paths
[[Synset('entity.n.01'), Synset('physical_entity.n.01'), Synset('object.n.01'), Synset('whole.n.02'), Synset('artifact.n.01'), Synset('instrumentality.n.03'), Synset('container.n.01'), Synset('wheeled_vehicle.n.01'), Synset('self-propelled_vehicle.n.01'), Synset('motor_vehicle.n.01'), Synset('car.n.01')], [Synset('entity.n.01'), Synset('physical_entity.n.01'), Synset('object.n.01'), Synset('whole.n.02'), Synset('artifact.n.01'), Synset('instrumentality.n.03'), Synset('conveyance.n.03'), Synset('vehicle.n.01'), Synset('wheeled_vehicle.n.01'), Synset('self-propelled_vehicle.n.01'), Synset('motor_vehicle.n.01'), Synset('car.n.01')]]

There are 2 paths.

>>> len(paths)
2
>>> paths[0]
[Synset('entity.n.01'), Synset('physical_entity.n.01'), Synset('object.n.01'), Synset('whole.n.02'), Synset('artifact.n.01'), Synset('instrumentality.n.03'), Synset('container.n.01'), Synset('wheeled_vehicle.n.01'), Synset('self-propelled_vehicle.n.01'), Synset('motor_vehicle.n.01'), Synset('car.n.01')]
>>> paths[1]
[Synset('entity.n.01'), Synset('physical_entity.n.01'), Synset('object.n.01'), Synset('whole.n.02'), Synset('artifact.n.01'), Synset('instrumentality.n.03'), Synset('conveyance.n.03'), Synset('vehicle.n.01'), Synset('wheeled_vehicle.n.01'), Synset('self-propelled_vehicle.n.01'), Synset('motor_vehicle.n.01'), Synset('car.n.01')]
>>> [synset.name for synset in paths[0]]
['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']
>>> [synset.name 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']
>>> motorcar.root_hypernyms()
[Synset('entity.n.01')]
>>> 

Try WordNet browser:

>>> nltk.app.wordnet()

Chapter 2.5.3 Other vocabulary relationship:

Meronyms and Holonyms are new concept for me.

>>> wn.synset('tree.n.01').part_meronyms()
[Synset('burl.n.02'), Synset('crown.n.07'), Synset('stump.n.01'), Synset('trunk.n.01'), Synset('limb.n.02')]
>>> wn.synset('tree.n.01').substance_meronyms()
[Synset('heartwood.n.01'), Synset('sapwood.n.01')]
>>> wn.synset('tree.n.01').member_holonyms()
[Synset('forest.n.01')]
>>> 

Other example:

>>> for synset in wn.synsets('mint', wn.NOUN):
...     print synset.name + ':', synset.definition
... 
batch.n.02: (often followed by `of') a large number or amount or extent
mint.n.02: any north temperate plant of the genus Mentha with aromatic leaves and small mauve flowers
mint.n.03: any member of the mint family of plants
mint.n.04: the leaves of a mint plant used fresh or candied
mint.n.05: a candy that is flavored with a mint oil
mint.n.06: a plant where money is coined by authority of the government
>>> 
>>> wn.synset('mint.n.04').part_holonyms()
[Synset('mint.n.02')]
>>> wn.synset('mint.n.04').substance_holonyms()
[Synset('mint.n.05')]
>>> 

Using verbs:

>>> wn.synset('walk.v.01').entailments()
[Synset('step.v.01')]
>>> wn.synset('eat.v.01').entailments()
[Synset('swallow.v.01'), Synset('chew.v.01')]
>>> wn.synset('tease.v.03').entailments()
[Synset('arouse.v.07'), Synset('disappoint.v.01')]
>>> 

Antonyms:

>>> wn.lemma('supply.n.02.supply').antonyms()
[Lemma('demand.n.02.demand')]
>>> wn.lemma('rush.v.01.rush').antonyms()
[Lemma('linger.v.04.linger')]
>>> wn.lemma('horizontal.a.01.horizontal').antonyms()
[Lemma('vertical.a.01.vertical'), Lemma('inclined.a.02.inclined')]
>>> wn.lemma('staccato.r.01.staccato').antonyms()
[Lemma('legato.r.01.legato')]
>>> 

Try to use dir()

>>> dir(wn.synset('harmony.n.02'))
['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_iter_hypernym_lists', '_lemma_pointers', '_needs_root', '_pointers', '_related', '_wordnet_corpus_reader', 'also_sees', 'attributes', 'causes', 'closure', 'common_hypernyms', 'definition', 'entailments', 'examples', 'frame_ids', 'hypernym_distances', 'hypernym_paths', 'hypernyms', 'hyponyms', 'instance_hypernyms', 'instance_hyponyms', 'jcn_similarity', 'lch_similarity', 'lemma_infos', 'lemma_names', 'lemmas', 'lexname', 'lin_similarity', 'lowest_common_hypernyms', 'max_depth', 'member_holonyms', 'member_meronyms', 'min_depth', 'name', 'offset', 'part_holonyms', 'part_meronyms', 'path_similarity', 'pos', 'region_domains', 'res_similarity', 'root_hypernyms', 'shortest_path_distance', 'similar_tos', 'substance_holonyms', 'substance_meronyms', 'topic_domains', 'tree', 'usage_domains', 'verb_groups', 'wup_similarity']
>>> 

Chapter 2.5.4:

I have tried similar examples in other textbook.

>>> right = wn.synset('right_whale.n.01')
>>> orca = wn.synset('orca.n.01')
>>> minke = wn.synset('minke_whale.n.01')
>>> tortoise = wn.synset('tortoise.n.01')
>>> nove = wn.synset('novel.n.01')
>>> novel = wn.synset('novel.n.01')
>>> right.lowest_common_hypernyms(minke)
[Synset('baleen_whale.n.01')]
>>> right.lowest_common_hypernyms(orca)
[Synset('whale.n.02')]
>>> right.lowest_common_hypernyms(tortoise)
[Synset('vertebrate.n.01')]
>>> right.lowest_common_hypernyms(novel)
[Synset('entity.n.01')]
>>> 

Even though I don't know the meaning of some words above, I can check how much the word is abstract using following code.

>>> wn.synset('baleen_whale.n.01').min_depth()
14
>>> wn.synset('whale.n.02').min_depth()
13
>>> wn.synset('vertebrate.n.01').min_depth()
8
>>> wn.synset('entity.n.01').min_depth()
0
>>> 

If number is closer to zero, the word should be more abstract like "entity". On the other hand, if the number is bigger, the word should be more detailed.

To check similarity. I have already tried several ways in my previous post.

>>> right.path_similarity(minke)
0.25
>>> right.path_similarity(orca)
0.16666666666666666
>>> right.path_similarity(tortoise)
0.07692307692307693
>>> right.path_similarity(novel)
0.043478260869565216
>>> 

To get help:

>>> help(wn)