Basic Usage¶
See also
This document covers the basics of querying wordnets, filtering results, and performing secondary queries on the results. For adding, removing, or inspecting lexicons, see Working with Lexicons. For more information about interlingual queries, see Interlingual Queries.
For the most basic queries, Wn provides several module functions for retrieving words, senses, and synsets:
>>> import wn
>>> wn.words('pike')
[Word('ewn-pike-n')]
>>> wn.senses('pike')
[Sense('ewn-pike-n-03311555-04'), Sense('ewn-pike-n-07795351-01'), Sense('ewn-pike-n-03941974-01'), Sense('ewn-pike-n-03941726-01'), Sense('ewn-pike-n-02563739-01')]
>>> wn.synsets('pike')
[Synset('ewn-03311555-n'), Synset('ewn-07795351-n'), Synset('ewn-03941974-n'), Synset('ewn-03941726-n'), Synset('ewn-02563739-n')]
Once you start working with multiple wordnets, these simple queries may return more than desired:
>>> wn.words('pike')
[Word('ewn-pike-n'), Word('wnja-n-66614')]
>>> wn.words('chat')
[Word('ewn-chat-n'), Word('ewn-chat-v'), Word('frawn-lex14803'), Word('frawn-lex21897')]
You can specify which language or lexicon you wish to query:
>>> wn.words('pike', lang='ja')
[Word('wnja-n-66614')]
>>> wn.words('chat', lexicon='frawn')
[Word('frawn-lex14803'), Word('frawn-lex21897')]
But it might be easier to create a Wordnet
object and use
it for queries:
>>> wnja = wn.Wordnet(lang='ja')
>>> wnja.words('pike')
[Word('wnja-n-66614')]
>>> frawn = wn.Wordnet(lexicon='frawn')
>>> frawn.words('chat')
[Word('frawn-lex14803'), Word('frawn-lex21897')]
In fact, the simple queries above implicitly create such a
Wordnet
object, but one that includes all installed
lexicons.
Primary Queries¶
The queries shown above are "primary" queries, meaning they are the first step in a user's interaction with a wordnet. Operations performed on the resulting objects are then secondary queries. Primary queries optionally take several fields for filtering the results, namely the word form and part of speech. Synsets may also be filtered by an interlingual index (ILI).
Searching for Words¶
The wn.words()
function returns a list of Word
objects that match the given word form or part of speech:
>>> wn.words('pencil')
[Word('ewn-pencil-n'), Word('ewn-pencil-v')]
>>> wn.words('pencil', pos='v')
[Word('ewn-pencil-v')]
Calling the function without a word form will return all words in the database:
>>> len(wn.words())
311711
>>> len(wn.words(pos='v'))
29419
>>> len(wn.words(pos='v', lexicon='ewn'))
11595
If you know the word identifier used by a lexicon, you can retrieve
the word directly with the wn.word()
function. Identifiers are
guaranteed to be unique within a single lexicon, but not across
lexicons, so it's best to call this function from an instantiated
Wordnet
object or with the lexicon
parameter
specified. If multiple words are found when querying multiple
lexicons, only the first is returned.
>>> wn.word('ewn-pencil-n', lexicon='ewn')
Word('ewn-pencil-n')
Searching for Senses¶
The wn.senses()
and wn.sense()
functions behave
similarly to wn.words()
and wn.word()
, except that
they return matching Sense
objects.
>>> wn.senses('plow', pos='n')
[Sense('ewn-plow-n-03973894-01')]
>>> wn.sense('ewn-plow-v-01745745-01')
Sense('ewn-plow-v-01745745-01')
Senses represent a relationship between a Word
and a
Synset
. Seen as an edge between nodes, senses are often
given less prominence than words or synsets, but they are the natural
locus of several interesting features such as sense relations (e.g.,
for derived words) and the natural level of representation for
translations to other languages.
Searching for Synsets¶
The wn.synsets()
and wn.synset()
functions are like
those above but allow the ili
parameter for filtering by
interlingual index, which is useful in interlingual queries:
>>> wn.synsets('scepter')
[Synset('ewn-14467142-n'), Synset('ewn-07282278-n')]
>>> wn.synset('ewn-07282278-n').ili
'i74874'
>>> wn.synsets(ili='i74874')
[Synset('ewn-07282278-n'), Synset('wnja-07267573-n'), Synset('frawn-07267573-n')]
Secondary Queries¶
Once you have gotten some results from a primary query, you can
perform operations on the Word
, Sense
, or
Synset
objects to get at further information in the
wordnet.
Exploring Words¶
Here are some of the things you can do with Word
objects:
>>> w = wn.words('goose')[0]
>>> w.pos # part of speech
'n'
>>> w.forms() # other word forms (e.g., irregular inflections)
['goose', 'geese']
>>> w.lemma() # canonical form
'goose'
>>> w.derived_words()
[Word('ewn-gosling-n'), Word('ewn-goosy-s'), Word('ewn-goosey-s')]
>>> w.senses()
[Sense('ewn-goose-n-01858313-01'), Sense('ewn-goose-n-10177319-06'), Sense('ewn-goose-n-07662430-01')]
>>> w.synsets()
[Synset('ewn-01858313-n'), Synset('ewn-10177319-n'), Synset('ewn-07662430-n')]
Since translations of a word into another language depend on the sense
used, Word.translate
returns a dictionary
mapping each sense to words in the target language:
>>> for sense, ja_words in w.translate(lang='ja').items():
... print(sense, ja_words)
...
Sense('ewn-goose-n-01858313-01') [Word('wnja-n-1254'), Word('wnja-n-33090'), Word('wnja-n-38995')]
Sense('ewn-goose-n-10177319-06') []
Sense('ewn-goose-n-07662430-01') [Word('wnja-n-1254')]
Exploring Senses¶
Compared to Word
and Synset
objects, there
are relatively few operations available on Sense
objects. Sense relations and translations, however, are important
operations on senses.
>>> s = wn.senses('dark', pos='n')[0]
>>> s.word() # each sense links to a single word
Word('ewn-dark-n')
>>> s.synset() # each sense links to a single synset
Synset('ewn-14007000-n')
>>> s.get_related('antonym')
[Sense('ewn-light-n-14006789-01')]
>>> s.get_related('derivation')
[Sense('ewn-dark-a-00273948-01')]
>>> s.translate(lang='fr') # translation returns a list of senses
[Sense('frawn-lex52992--13983515-n')]
>>> s.translate(lang='fr')[0].word().lemma()
'obscurité'
Exploring Synsets¶
Many of the operations people care about happen on synsets, such as hierarchical relations and metrics.
>>> ss = wn.synsets('hound', pos='n')[0]
>>> ss.senses()
[Sense('ewn-hound-n-02090203-01'), Sense('ewn-hound_dog-n-02090203-02')]
>>> ss.words()
[Word('ewn-hound-n'), Word('ewn-hound_dog-n')]
>>> ss.lemmas()
['hound', 'hound dog']
>>> ss.definition()
'any of several breeds of dog used for hunting typically having large drooping ears'
>>> ss.hypernyms()
[Synset('ewn-02089774-n')]
>>> ss.hypernyms()[0].lemmas()
['hunting dog']
>>> len(ss.hyponyms())
20
>>> ss.hyponyms()[0].lemmas()
['Afghan', 'Afghan hound']
>>> ss.max_depth()
15
>>> ss.shortest_path(wn.synsets('dog', pos='n')[0])
[Synset('ewn-02090203-n'), Synset('ewn-02089774-n'), Synset('ewn-02086723-n')]
>>> ss.translate(lang='fr') # translation returns a list of synsets
[Synset('frawn-02087551-n')]
>>> ss.translate(lang='fr')[0].lemmas()
['chien', 'chien de chasse']
Filtering by Language¶
The lang
parameter of wn.words()
, wn.senses()
,
wn.synsets()
, and Wordnet
allows a single BCP 47 language
code. When this parameter is used, only entries in the specified
language will be returned.
>>> import wn
>>> wn.words('chat')
[Word('ewn-chat-n'), Word('ewn-chat-v'), Word('frawn-lex14803'), Word('frawn-lex21897')]
>>> wn.words('chat', lang='fr')
[Word('frawn-lex14803'), Word('frawn-lex21897')]
If a language code not used by any lexicon is specified, a
wn.Error
is raised.
Filtering by Lexicon¶
The lexicon
parameter of wn.words()
, wn.senses()
,
wn.synsets()
, and Wordnet
take a string of
space-delimited lexicon specifiers. Entries in a lexicon whose ID matches one of
the lexicon specifiers will be returned. For these, the following
rules are used:
A full
id:version
string (e.g.,ewn:2020
) selects a specific lexiconOnly a lexicon
id
(e.g.,ewn
) selects the most recently added lexicon with that IDA star
*
may be used to match any lexicon; a star may not include a version
>>> wn.words('chat', lexicon='ewn:2020')
[Word('ewn-chat-n'), Word('ewn-chat-v')]
>>> wn.words('chat', lexicon='wnja')
[]
>>> wn.words('chat', lexicon='wnja frawn')
[Word('frawn-lex14803'), Word('frawn-lex21897')]