Reading WordNet synsets

Distinguish a word’s spelling from its meanings

similarity
WordNet
word senses
Learn how WordNet groups words into synsets and records multiple senses. Examine the functions and limits of a curated lexical resource.

The label bank could describe river photographs, money records, or a toy building with a coin slot. The spelling alone does not show the intended meaning.

A synset is a set of words that share one sense. WordNet is a manually compiled lexical database of words and meanings. It records synsets and links them to broader meanings called hypernyms. A hypernym names a broader category in an “is-a” hierarchy.

TipWhat you will learn

This lesson shows how to:

  • explain a synset in plain language
  • count senses for a word string
  • compare a sense count with a simple chance baseline
  • list synonyms inside one synset
  • follow hypernym links to broader categories
  • name the functions and limits of WordNet.

Load the WordNet extract

The lesson reads four local lexical CSV files made from Open English WordNet 2024. No code downloads WordNet while the page renders. The metadata file records the source, licence, and SHA-256 fingerprints, which identify file contents.

library(readr)
library(dplyr)
library(tibble)
library(stringr)

source("R/permutation-null.R")

senses <- read_csv(
  "data/wordnet/wordnet-senses.csv",
  na = character(),
  col_types = cols(
    lemma = col_character(),
    part_of_speech = col_character(),
    sense_id = col_character(),
    synset_id = col_character(),
    sense_order = col_integer()
  )
)

synsets <- read_csv(
  "data/wordnet/wordnet-synsets.csv",
  na = character(),
  col_types = cols(
    synset_id = col_character(),
    part_of_speech = col_character(),
    definition = col_character(),
    example = col_character()
  )
)

members <- read_csv(
  "data/wordnet/wordnet-members.csv",
  na = character(),
  col_types = cols(
    synset_id = col_character(),
    lemma = col_character()
  )
)

relations <- read_csv(
  "data/wordnet/wordnet-relations.csv",
  na = character(),
  col_types = cols(
    synset_id = col_character(),
    relation = col_character(),
    target_synset_id = col_character(),
    target_definition = col_character(),
    target_members = col_character()
  )
)

metadata <- read_csv(
  "data/wordnet/wordnet-metadata.csv",
  na = character(),
  col_types = cols(
    field = col_character(),
    value = col_character()
  )
)

metadata_summary <- tibble(
  item = c("lemmas", "senses", "synsets", "synset membership rows", "relations"),
  value = c(
    n_distinct(senses$lemma),
    nrow(senses),
    nrow(synsets),
    nrow(members),
    nrow(relations)
  )
)
Size of the local WordNet extract used in this lesson
Item Count
lemmas 38
senses 419
synsets 417
synset membership rows 1050
relations 2432

This extract covers 38 lemmas, not all of English. A lemma is the dictionary form of a word. The extract shows how a lexical database separates spelling from meaning.

Read the senses of one word

A sense is one listed meaning of a word. The string bank has 18 senses in this extract: 10 noun senses and 8 verb senses. The first noun senses split river land, finance, piles, rows, reserves, and gambling funds.

bank_senses <- senses |>
  filter(lemma == "bank") |>
  count(part_of_speech, name = "senses") |>
  arrange(part_of_speech)

bank_noun_senses <- senses |>
  filter(lemma == "bank", part_of_speech == "noun") |>
  arrange(sense_order) |>
  left_join(synsets, by = c("synset_id", "part_of_speech")) |>
  slice_head(n = 6) |>
  transmute(
    sense = sense_order,
    definition
  )

lemma_sense_counts <- senses |>
  count(lemma, name = "sense_count")

bank_sense_null <- permutation_null(
  observed = sum(senses$lemma == "bank"),
  replicate_fn = function() {
    lemma_sense_counts$sense_count[
      match(sample(lemma_sense_counts$lemma, 1L), lemma_sense_counts$lemma)
    ]
  },
  replicates = 1000L,
  seed = 5801L,
  alternative = "greater"
)
The first six noun senses of bank in the local WordNet extract
Noun sense Definition
1 sloping land (especially the slope beside a body of water)
2 a financial institution that accepts deposits and channels the money into lending activities
3 a long ridge or pile
4 an arrangement of similar objects in a row or in tiers
5 a supply or stock held in reserve for future use (especially in emergencies)
6 the funds held by a gambling house or the dealer in some gambling games
Sense count for bank compared with random lemmas in the extract
Observed Null mean Null 5th pct Null 95th pct p-value Replicates Alternative
18 11.352 2 48 0.138 1000 greater

A word string is not a unit of meaning. Treating every bank occurrence as the same meaning simplifies a search, model, or count. The null table compares the sense count with counts for randomly selected lemmas. The extract contains ambiguous teaching examples, so 18 senses for bank is not unusual within this small file.

Any lexical method will return an answer for a known word. Compare the target word with other words from the same source before interpreting the answer.

Look inside a synset

For the financial-institution sense, the synset contains four member lemmas. These are four names WordNet links to the same sense.

financial_bank_synset <- senses |>
  filter(lemma == "bank", part_of_speech == "noun", sense_order == 2L) |>
  pull(synset_id)

financial_bank_members <- members |>
  filter(synset_id == financial_bank_synset) |>
  arrange(lemma) |>
  mutate(role = if_else(lemma == "bank", "queried word", "other member"))
Members of the financial-institution synset for bank
Synset ID Lemma Role
oewn-08437235-n bank queried word
oewn-08437235-n banking company other member
oewn-08437235-n banking concern other member
oewn-08437235-n depository financial institution other member

Synonymy here is local to one sense. bank and banking company share the finance sense. river bank has a different sense.

Follow an is-a hierarchy

A hypernym names a broader category for a meaning. In this extract, the professor-position sense of chair links to a position synset, then a work synset, then activity.

first_hypernym <- function(synset) {
  next_row <- relations |>
    filter(synset_id == synset, relation == "hypernym") |>
    arrange(target_synset_id) |>
    slice_head(n = 1)

  if (nrow(next_row) != 1L) {
    stop("No hypernym recorded for synset: ", synset, call. = FALSE)
  }

  next_row
}

select_chair_synset <- function() {
  candidates <- senses |>
    filter(lemma == "chair", part_of_speech == "noun") |>
    left_join(synsets, by = c("synset_id", "part_of_speech")) |>
    filter(str_detect(definition, regex("professorship|professor", ignore_case = TRUE))) |>
    arrange(sense_order)

  if (nrow(candidates) != 1L) {
    stop("Expected one professor-position chair sense in this extract.", call. = FALSE)
  }

  candidates$synset_id[[1]]
}

walk_hypernyms <- function(start_synset, steps) {
  path <- start_synset

  for (step in seq_len(steps)) {
    path <- c(path, first_hypernym(path[length(path)])$target_synset_id)
  }

  path
}

lookup_definition <- function(id) {
  local_definition <- synsets$definition[synsets$synset_id == id]
  if (length(local_definition) > 0L) {
    return(local_definition[[1]])
  }

  external_definition <- relations$target_definition[match(id, relations$target_synset_id)]
  if (is.na(external_definition)) {
    stop("No definition available for synset: ", id, call. = FALSE)
  }

  external_definition
}

lookup_members <- function(id) {
  local_members <- members |>
    filter(synset_id == id) |>
    arrange(lemma) |>
    pull(lemma)

  if (length(local_members) > 0L) {
    return(paste(local_members, collapse = ", "))
  }

  external_members <- relations$target_members[match(id, relations$target_synset_id)]
  if (is.na(external_members)) {
    stop("No members available for synset: ", id, call. = FALSE)
  }

  external_members
}

chair_synset <- select_chair_synset()
path_ids <- walk_hypernyms(chair_synset, steps = 3L)

hypernym_path <- tibble(
  level = seq_along(path_ids) - 1L,
  synset_id = path_ids,
  members = vapply(path_ids, lookup_members, character(1)),
  definition = vapply(path_ids, lookup_definition, character(1))
)
A four-level hypernym path from chair to activity
Level Synset ID Members Definition
0 oewn-00599171-n chair, professorship the position of professor
1 oewn-00587299-n berth, office, place, position, post, situation a job in an organization
2 oewn-00583425-n business, job, line, line of work, occupation the principal activity in your life that you do to earn money
3 oewn-00408356-n activity any specific behavior

The lookup assumes this extract contains the chosen chair sense and three successive hypernym links. The functions stop when that assumption is false. A missing lemma therefore cannot produce an incomplete path that appears valid.

The hierarchy is useful for broadening a search. A search for occupations might group chair, office, and job under a broader category. The hierarchy can be too general when a task requires academic rank, the furniture sense, or a named committee role.

State the limits

WordNet is a curated resource with a version and a licence. It is not a direct measurement of English. It covers much common vocabulary, while domain terms, slang, new coinages, and local meanings can be missing.

Sense inventories distinguish meanings at a chosen level of detail. A distinction that helps one annotation task can be too detailed for another. Treat a WordNet sense as an inventory entry, not a definitive account of meaning.

WordNet can help teach the difference between words and meanings or expand a controlled search. It can find labels that share a listed sense. Users can also build small rule-based features from WordNet when they can examine vocabulary coverage.

limit_check <- tibble(
  check = c("version recorded", "licence recorded", "target lemmas recorded"),
  value = c(
    metadata$value[metadata$field == "source_version"],
    metadata$value[metadata$field == "source_licence"],
    metadata$value[metadata$field == "n_lemmas_found"]
  )
)
Provenance fields stored with the WordNet extract
Check Value
version recorded Open English Wordnet 2024
licence recorded https://creativecommons.org/licenses/by/4.0
target lemmas recorded 38

What to remember

  • A synset is a set of words sharing one sense.
  • bank has 18 senses in this extract, so one spelling is not one meaning.
  • A null check compares the sense count with counts for other lemmas in this extract.
  • Hypernym links make an is-a hierarchy that can broaden a search.
  • WordNet is useful when a curated inventory is acceptable and users can examine its limited coverage.

Sources