The goal of sehrnett is to provide a nice (and fast) interface to Princeton’s WordNet. Unlike the original wordnet package (Feinerer et al., 2020), you don’t need to install WordNet and / or setup rJava.
The data is not included in the package. The package will download the data (~100M Zipped, ~400M Unzipped) from the Internet, if such data is not available. Please make sure you agree with the WordNet License.
get_lemmasThe most basic function is get_lemmas. It generates basic information about the lemmas [1] you provided.
get_lemmas(c("very", "nice"))
#> # A tibble: 10 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 400032295 very 1 used as intensifiers; real' is som… r adv.all
#> 2 400513282 very 2 precisely so r adv.all
#> 3 301845232 very 1 precisely as stated s adj.all
#> 4 302076350 very 2 being the exact same one; not any … s adj.all
#> 5 301590750 nice 1 pleasant or pleasing or agreeable … a adj.all
#> 6 108957024 nice 1 a city in southeastern France on t… n noun.loca…
#> 7 302000490 nice 2 socially or conventionally correct… s adj.all
#> 8 301844650 nice 3 done with delicacy and skill s adj.all
#> 9 300987524 nice 4 excessively fastidious and easily … s adj.all
#> 10 300644482 nice 5 exhibiting courtesy and politeness s adj.allget_lemmas("nice")
#> # A tibble: 6 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 301590750 nice 1 pleasant or pleasing or agreeable i… a adj.all
#> 2 108957024 nice 1 a city in southeastern France on th… n noun.loca…
#> 3 302000490 nice 2 socially or conventionally correct;… s adj.all
#> 4 301844650 nice 3 done with delicacy and skill s adj.all
#> 5 300987524 nice 4 excessively fastidious and easily d… s adj.all
#> 6 300644482 nice 5 exhibiting courtesy and politeness s adj.allget_lemmas("nice", pos = "n")
#> # A tibble: 1 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 108957024 nice 1 a city in southeastern France on th… n noun.loca…Please note that some definitions in WordNet are considered pejorative or offensive, e.g.
get_lemmas("dog")
#> # A tibble: 8 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 102086723 dog 1 a member of the genus Canis (probab… n noun.anim…
#> 2 110133978 dog 2 a dull unattractive unpleasant girl… n noun.pers…
#> 3 110042764 dog 3 informal term for a man n noun.pers…
#> 4 109905672 dog 4 someone who is morally reprehensible n noun.pers…
#> 5 107692347 dog 5 a smooth-textured sausage of minced… n noun.food
#> 6 103907626 dog 6 a hinged catch that fits into a not… n noun.arti…
#> 7 102712903 dog 7 metal supports for logs in a firepl… n noun.arti…
#> 8 202005890 dog 1 go after with the intent to catch v verb.moti…The dot notation (“lemma.pos.sensenum”) can be used to quick search for a particular word sense. For example, one can search for “king.n.10” to quickly pin down the word sense of “king” as a chess piece.
get_lemmas("king.n.10")
#> # A tibble: 1 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 103623310 king 10 (chess) the weakest but the most i… n noun.artif…The morphological processing of the original Wordnet is partially implemented in sehrnett [2]. As the Wordnet’s database contains only information about lemmas (e.g. eat), you need to convert inflected variants (e.g. ate, eaten, eating) back to their lemmas to query them. The process is otherwise known as lemmatization.
sehrnett provides such lemmatization. But you need to provide exactly one pos and set lemmatize to TRUE (default).
get_lemmas(c("ate", "ducking"), pos = "v")
#> # A tibble: 10 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 201170802 eat 1 take in solid food v verb.consum…
#> 2 201168667 eat 2 eat a meal; take a meal v verb.consum…
#> 3 201182162 eat 3 take in food; used of animals on… v verb.consum…
#> 4 201770125 eat 4 worry or cause anxiety in a pers… v verb.emotion
#> 5 201159815 eat 5 use up (resources or materials) v verb.consum…
#> 6 200275082 eat 6 cause to deteriorate due to the … v verb.change
#> 7 201869189 duck 1 to move (the head or body) quick… v verb.motion
#> 8 201971799 duck 2 submerge or plunge suddenly v verb.motion
#> 9 201980234 duck 3 dip into a liquid v verb.motion
#> 10 200811316 duck 4 avoid or try to avoid fulfilling… v verb.commun…get_lemmas(c("loci", "lemmata", "boxesful"), pos = "n")
#> # A tibble: 7 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 108695366 locus 1 the scene of any event or action… n noun.locati…
#> 2 108641143 locus 2 the specific site of a particula… n noun.locati…
#> 3 108017323 locus 3 the set of all points or lines t… n noun.group
#> 4 106764547 lemma 1 a subsidiary proposition that is… n noun.commun…
#> 5 113176246 lemma 2 the lower and stouter of the two… n noun.plant
#> 6 106356061 lemma 3 the heading that indicates the s… n noun.commun…
#> 7 113787764 boxful 1 the quantity contained in a box n noun.quanti…get_lemmas(c("nicest", "stronger"), pos = "a")
#> # A tibble: 3 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 301590750 nice 1 pleasant or pleasing or agreeable i… a adj.all
#> 2 302328781 strong 1 having strength or power greater th… a adj.all
#> 3 301829730 strong 4 having a strong physiological or ch… a adj.allFor example, you want to know the synonyms of the word “nuance” (very important for academic writing). You can first search using the lemma “nuance” with get_lemmas.
res <- get_lemmas("nuance")
res
#> # A tibble: 1 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 106618544 nuance 1 a subtle difference in meaning … n noun.communi…There could be multiple word senses and you need to choose which word sense you want to convey. But in this case, there is only one. You can then search for the synsetid (cognitive synonym identifier) of that word sense.
# get_synonyms() is a wrapper to get_synsetids
get_synsetids(res$synsetid[1])
#> # A tibble: 5 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 106618544 nuance 1 a subtle difference in meani… n noun.commun…
#> 2 106618544 subtlety 1 a subtle difference in meani… n noun.commun…
#> 3 106618544 nicety 2 a subtle difference in meani… n noun.commun…
#> 4 106618544 refinement 4 a subtle difference in meani… n noun.commun…
#> 5 106618544 shade 4 a subtle difference in meani… n noun.commun…All get_ functions are chainable by using the magrittr pipe operator.
c("switch off") %>% get_lemmas(pos = "v") %>% get_synonyms
#> # A tibble: 4 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 201513208 switch off 1 cause to stop operating by dis… v verb.cont…
#> 2 201513208 turn off 1 cause to stop operating by dis… v verb.cont…
#> 3 201513208 turn out 11 cause to stop operating by dis… v verb.cont…
#> 4 201513208 cut 27 cause to stop operating by dis… v verb.cont…get_outdegreesWordNet is indeed a network. synsetids are connected to each other in a directed graph. An node (a synsetid) is linked to another with different link (edge) types labelling with different linkids. You can list out all available linkids with the function list_linktypes.
list_linktypes()
#> linkid link recurses
#> 1 1 hypernym 1
#> 2 2 hyponym 1
#> 3 3 instance hypernym 1
#> 4 4 instance hyponym 1
#> 5 11 part holonym 1
#> 6 12 part meronym 1
#> 7 13 member holonym 1
#> 8 14 member meronym 1
#> 9 15 substance holonym 1
#> 10 16 substance meronym 1
#> 11 21 entail 1
#> 12 23 cause 1
#> 13 30 antonym 0
#> 14 40 similar 0
#> 15 50 also 0
#> 16 60 attribute 0
#> 17 70 verb group 0
#> 18 71 participle 0
#> 19 80 pertainym 0
#> 20 81 derivation 0
#> 21 91 domain category 0
#> 22 92 domain member category 0
#> 23 93 domain region 0
#> 24 94 domain member region 0
#> 25 95 domain usage 0
#> 26 96 domain member usage 0
#> 27 97 domain 0
#> 28 98 member 0## all hypernyms
get_lemmas("dog", pos = "n", sensenum = 1) %>% get_outdegrees(linkid = 1)
#> # A tibble: 21 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 102085998 canid 1 any of various fissip… n noun.ani…
#> 2 102085998 canine 2 any of various fissip… n noun.ani…
#> 3 101320032 domestic animal 1 any of various animal… n noun.ani…
#> 4 101320032 domesticated animal 1 any of various animal… n noun.ani…
#> 5 110759293 disagreeable woman 1 a woman who is an unp… n noun.per…
#> 6 110759293 unpleasant woman 1 a woman who is an unp… n noun.per…
#> 7 109927483 blighter 2 a boy or man n noun.per…
#> 8 109927483 bloke 1 a boy or man n noun.per…
#> 9 109927483 chap 1 a boy or man n noun.per…
#> 10 109927483 cuss 2 a boy or man n noun.per…
#> # … with 11 more rows## all hyponymes
get_lemmas("dog", pos = "n", sensenum = 1) %>% get_outdegrees(linkid = 2)
#> # A tibble: 35 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 102087384 barker 2 informal terms for dogs n noun.ani…
#> 2 102113458 basenji 1 small smooth-haired bree… n noun.ani…
#> 3 102115149 belgian griffon 1 breed of various very sm… n noun.ani…
#> 4 102087384 bow-wow 2 informal terms for dogs n noun.ani…
#> 5 102115149 brussels griffon 1 breed of various very sm… n noun.ani…
#> 6 102112993 carriage dog 1 a large breed having a s… n noun.ani…
#> 7 102112993 coach dog 1 a large breed having a s… n noun.ani…
#> 8 102115478 corgi 1 either of two Welsh bree… n noun.ani…
#> 9 102087513 cur 1 an inferior dog or one o… n noun.ani…
#> 10 102112993 dalmatian 2 a large breed having a s… n noun.ani…
#> # … with 25 more rows## all antonyms
get_lemmas("nice", pos = "a", sensenum = 1) %>% get_outdegrees(linkid = 30)
#> # A tibble: 1 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 301591485 nasty 1 offensive or even (of persons) malic… a adj.allsehrnett provides several syntactic sugars as get_ functions. For example:
## all hyponymes
get_lemmas("dog", pos = "n", sensenum = 1) %>% get_hyponyms()
#> # A tibble: 35 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 102087384 barker 2 informal terms for dogs n noun.ani…
#> 2 102113458 basenji 1 small smooth-haired bree… n noun.ani…
#> 3 102115149 belgian griffon 1 breed of various very sm… n noun.ani…
#> 4 102087384 bow-wow 2 informal terms for dogs n noun.ani…
#> 5 102115149 brussels griffon 1 breed of various very sm… n noun.ani…
#> 6 102112993 carriage dog 1 a large breed having a s… n noun.ani…
#> 7 102112993 coach dog 1 a large breed having a s… n noun.ani…
#> 8 102115478 corgi 1 either of two Welsh bree… n noun.ani…
#> 9 102087513 cur 1 an inferior dog or one o… n noun.ani…
#> 10 102112993 dalmatian 2 a large breed having a s… n noun.ani…
#> # … with 25 more rowsget_lemmas("nice", pos = "a", sensenum = 1) %>% get_antonyms()
#> # A tibble: 1 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 301591485 nasty 1 offensive or even (of persons) malic… a adj.allget_lemmas("nice", pos = "a", sensenum = 1) %>% get_derivatives()
#> # A tibble: 1 × 6
#> synsetid lemma sensenum definition pos lexdomain
#> <int> <chr> <int> <chr> <chr> <chr>
#> 1 104786760 niceness 2 the quality of nice n noun.attributeYes, the plural of lemma can also be lemmata, you Latin-speaking people.
Like many implementations (e.g. NLTK, Ruby’s rwordnet and node-wordnet-magic), the morpological processing is only partial. Collocations and hyphenation are not supported. Therefore, please don’t expect that lemmatizing asking for it would obtain ask for it (as documented in Wordnet’s website).