pythainlp.corpus

The pythainlp.corpus provides access to corpus that comes with PyThaiNLP.

Modules

pythainlp.corpus.countries() → FrozenSet[str][source]

Return a frozenset of country names in Thai such as “แคนาดา”, “โรมาเนีย”, “แอลจีเรีย”, and “ลาว”.

(See: dev/pythainlp/corpus/countries_th.txt)

return:

frozenset containing countries names in Thai

rtype:

frozenset

pythainlp.corpus.get_corpus(filename: str, as_is: bool = False) → frozenset | list[source]

Read corpus data from file and return a frozenset or a list.

Each line in the file will be a member of the set or the list.

By default, a frozenset will be return, with whitespaces stripped, and empty values and duplicates removed.

If as_is is True, a list will be return, with no modifications in member values and their orders.

Parameters:: filename (str) – filename of the corpus to be read
Returns:: frozenset or list consists of lines in the file
Return type:: frozenset or list
Example:

from pythainlp.corpus import get_corpus

get_corpus('negations_th.txt')
# output:
# frozenset({'แต่', 'ไม่'})

get_corpus('ttc_freq.txt')
# output:
# frozenset({'โดยนัยนี้\t1',
#    'ตัวบท\t10',
#    'หยิบยื่น\t3',
#     ...})

pythainlp.corpus.get_corpus_db(url: str)[source]

Get corpus catalog from server.

Parameters:: url (str) – URL corpus catalog

pythainlp.corpus.get_corpus_db_detail(name: str, version: str | None = None) → dict[source]

Get details about a corpus, using information from local catalog.

Parameters:: name (str) – name corpus
Returns:: details about a corpus
Return type:: dict

pythainlp.corpus.get_corpus_default_db(name: str, version: str | None = None) → str | None[source]

Get model path from default_db.json

Parameters:: name (str) – corpus name
Returns:: path to the corpus or None of the corpus doesn’t exist in the device
Return type:: str

If you want edit default_db.json, you can edit in pythainlp/corpus/default_db.json

pythainlp.corpus.get_corpus_path(name: str, version: str | None = None, force: bool = False) → str | None[source]

Get corpus path.

Parameters:

name (str) – corpus name
version (str) – version
force (bool) – force download

Returns:

path to the corpus or None of the corpus doesn’t exist in the device

Return type:

str

Example:

(Please see the filename from this file

If the corpus already exists:

from pythainlp.corpus import get_corpus_path

print(get_corpus_path('ttc'))
# output: /root/pythainlp-data/ttc_freq.txt

If the corpus has not been downloaded yet:

from pythainlp.corpus import download, get_corpus_path

print(get_corpus_path('wiki_lm_lstm'))
# output: None

download('wiki_lm_lstm')
# output:
# Download: wiki_lm_lstm
# wiki_lm_lstm 0.32
# thwiki_lm.pth?dl=1: 1.05GB [00:25, 41.5MB/s]
# /root/pythainlp-data/thwiki_model_lstm.pth

print(get_corpus_path('wiki_lm_lstm'))
# output: /root/pythainlp-data/thwiki_model_lstm.pth

pythainlp.corpus.download(name: str, force: bool = False, url: str | None = None, version: str | None = None) → bool[source]

Download corpus.

The available corpus names can be seen in this file: https://pythainlp.github.io/pythainlp-corpus/db.json

Parameters:

name (str) – corpus name
force (bool) – force download
url (str) – URL of the corpus catalog
version (str) – Version of the corpus

Returns:

True if the corpus is found and succesfully downloaded. Otherwise, it returns False.

Return type:

bool

Example:

from pythainlp.corpus import download

download('wiki_lm_lstm', force=True)
# output:
# Corpus: wiki_lm_lstm
# - Downloading: wiki_lm_lstm 0.1
# thwiki_lm.pth:  26%|██▌       | 114k/434k [00:00<00:00, 690kB/s]

By default, downloaded corpus and model will be saved in $HOME/pythainlp-data/ (e.g. /Users/bact/pythainlp-data/wiki_lm_lstm.pth).

pythainlp.corpus.remove(name: str) → bool[source]

Remove corpus

Parameters:: name (str) – corpus name
Returns:: True if the corpus is found and succesfully removed. Otherwise, it returns False.
Return type:: bool
Example:

from pythainlp.corpus import remove, get_corpus_path, get_corpus

print(remove('ttc'))
# output: True

print(get_corpus_path('ttc'))
# output: None

get_corpus('ttc')
# output:
# FileNotFoundError: [Errno 2] No such file or directory:
# '/usr/local/lib/python3.6/dist-packages/pythainlp/corpus/ttc'

pythainlp.corpus.provinces(details: bool = False) → FrozenSet[str] | List[str][source]

Return a frozenset of Thailand province names in Thai such as “กระบี่”, “กรุงเทพมหานคร”, “กาญจนบุรี”, and “อุบลราชธานี”.

(See: dev/pythainlp/corpus/thailand_provinces_th.txt)

param bool details:

return details of provinces or not

return:

frozenset containing province names of Thailand (if details is False) or list containing dict of province names and details such as [{‘name_th’: ‘นนทบุรี’, ‘abbr_th’: ‘นบ’, ‘name_en’: ‘Nonthaburi’, ‘abbr_en’: ‘NBI’}].

rtype:

frozenset or list

pythainlp.corpus.thai_stopwords() → FrozenSet[str][source]

Return a frozenset of Thai stopwords such as “มี”, “ไป”, “ไง”, “ขณะ”, “การ”, and “ประการหนึ่ง”.

(See: dev/pythainlp/corpus/stopwords_th.txt)

We using stopword lists by thesis’s เพ็ญศิริ ลี้ตระกูล.

See Also:

เพ็ญศิริ ลี้ตระกูล . การเลือกประโยคสำคัญในการสรุปความภาษาไทยโดยใช้แบบจำลองแบบลำดับชั้น. กรุงเทพมหานคร : มหาวิทยาลัยธรรมศาสตร์; 2551.

return:: frozenset containing stopwords.
rtype:: frozenset

pythainlp.corpus.thai_words() → FrozenSet[str][source]

Return a frozenset of Thai words such as “กติกา”, “กดดัน”, “พิษ”, and “พิษภัย”.

(See: dev/pythainlp/corpus/words_th.txt)

return:

frozenset containing words in Thai language.

rtype:

frozenset

pythainlp.corpus.thai_syllables() → FrozenSet[str][source]

Return a frozenset of Thai syllables such as “กรอบ”, “ก็”, “๑”, “โมบ”, “โมน”, “โม่ง”, “กา”, “ก่า”, and, “ก้า”.

(See: dev/pythainlp/corpus/syllables_th.txt)

We using thai syllables list from KUCut.

return:: frozenset containing syllables in Thai language.
rtype:: frozenset

pythainlp.corpus.thai_negations() → FrozenSet[str][source]

Return a frozenset of Thai negation words including “ไม่” and “แต่”.

(See: dev/pythainlp/corpus/negations_th.txt)

return:

frozenset containing negations in Thai language.

rtype:

frozenset

pythainlp.corpus.thai_family_names() → FrozenSet[str][source]

Return a frozenset of Thai family names

(See: dev/pythainlp/corpus/family_names_th.txt)

return:

frozenset containing Thai family names.

rtype:

frozenset

pythainlp.corpus.thai_female_names() → FrozenSet[str][source]

Return a frozenset of Thai female names

(See: dev/pythainlp/corpus/person_names_female_th.txt)

return:

frozenset containing Thai female names.

rtype:

frozenset

pythainlp.corpus.thai_male_names() → FrozenSet[str][source]

Return a frozenset of Thai male names

(See: dev/pythainlp/corpus/person_names_male_th.txt)

return:

frozenset containing Thai male names.

rtype:

frozenset

ConceptNet

ConceptNet is an open, multilingual knowledge graph See: https://github.com/commonsense/conceptnet5/wiki/API

pythainlp.corpus.conceptnet.edges(word: str, lang: str = 'th')[source]

Get edges from ConceptNet API. ConceptNet is a public semantic network, designed to help computers understand the meanings of words that people use.

For example, the term “ConceptNet” is a “knowledge graph”, and “knowledge graph” has “common sense knowledge” which is a part of “artificial inteligence”. Also, “ConcepNet” is used for “natural language understanding” which is a part of “artificial intelligence”.

“ConceptNet” –is a–> “knowledge graph” –has–> “common sense” –a part of–> “artificial intelligence”

“ConceptNet” –used for–> “natural language understanding” –a part of–> “artificial intelligence”

With this illustration, it shows relationships (represented as Edge) between the terms (represented as Node)

Parameters:

word (str) – word to be sent to ConceptNet API
lang (str) – abbreviation of language (i.e. th for Thai, en for English, or ja for Japan). By default, it is th (Thai).

Returns:

return edges of the given word according to the ConceptNet network.

Return type:

list[dict]

Example:

from pythainlp.corpus.conceptnet import edges

edges('hello', lang='en')
# output:
# [{
#   '@id': '/a/[/r/IsA/,/c/en/hello/,/c/en/greeting/]',
#   '@type': 'Edge',
#   'dataset': '/d/conceptnet/4/en',
#   'end': {'@id': '/c/en/greeting',
#   '@type': 'Node',
#   'label': 'greeting',
#   'language': 'en',
#   'term': '/c/en/greeting'},
#   'license': 'cc:by/4.0',
#   'rel': {'@id': '/r/IsA', '@type': 'Relation', 'label': 'IsA'},
#   'sources': [
#   {
#   '@id': '/and/[/s/activity/omcs/vote/,/s/contributor/omcs/bmsacr/]',
#   '@type': 'Source',
#   'activity': '/s/activity/omcs/vote',
#   'contributor': '/s/contributor/omcs/bmsacr'
#   },
#   {
#     '@id': '/and/[/s/activity/omcs/vote/,/s/contributor/omcs/test/]',
#     '@type': 'Source',
#     'activity': '/s/activity/omcs/vote',
#     'contributor': '/s/contributor/omcs/test'}
#   ],
#   'start': {'@id': '/c/en/hello',
#   '@type': 'Node',
#   'label': 'Hello',
#   'language': 'en',
#   'term': '/c/en/hello'},
#   'surfaceText': '[[Hello]] is a kind of [[greeting]]',
#   'weight': 3.4641016151377544
# }, ...]

edges('สวัสดี', lang='th')
# output:
# [{
#  '@id': '/a/[/r/RelatedTo/,/c/th/สวัสดี/n/,/c/en/prosperity/]',
#  '@type': 'Edge',
#  'dataset': '/d/wiktionary/en',
#  'end': {'@id': '/c/en/prosperity',
#  '@type': 'Node',
#  'label': 'prosperity',
#  'language': 'en',
#  'term': '/c/en/prosperity'},
#  'license': 'cc:by-sa/4.0',
#  'rel': {
#      '@id': '/r/RelatedTo', '@type': 'Relation',
#      'label': 'RelatedTo'},
#  'sources': [{
#  '@id': '/and/[/s/process/wikiparsec/2/,/s/resource/wiktionary/en/]',
#  '@type': 'Source',
#  'contributor': '/s/resource/wiktionary/en',
#  'process': '/s/process/wikiparsec/2'}],
#  'start': {'@id': '/c/th/สวัสดี/n',
#  '@type': 'Node',
#  'label': 'สวัสดี',
#  'language': 'th',
#  'sense_label': 'n',
#  'term': '/c/th/สวัสดี'},
#  'surfaceText': None,
#  'weight': 1.0
# }, ...]

TNC

pythainlp.corpus.tnc.word_freqs() → List[Tuple[str, int]][source]: Get word frequency from Thai National Corpus (TNC)

(See: dev/pythainlp/corpus/tnc_freq.txt)

Credit: Korakot Chaovavanich https://bit.ly/3wSkZsF

pythainlp.corpus.tnc.unigram_word_freqs() → defaultdict[source]: Get unigram word frequency from Thai National Corpus (TNC)

pythainlp.corpus.tnc.bigram_word_freqs() → defaultdict[source]: Get bigram word frequency from Thai National Corpus (TNC)

pythainlp.corpus.tnc.trigram_word_freqs() → defaultdict[source]: Get trigram word frequency from Thai National Corpus (TNC)

TTC

pythainlp.corpus.ttc.word_freqs() → List[Tuple[str, int]][source]: Get word frequency from Thai Textbook Corpus (TTC)

(See: dev/pythainlp/corpus/ttc_freq.txt)

pythainlp.corpus.ttc.unigram_word_freqs() → defaultdict[source]: Get unigram word frequency from Thai Textbook Corpus (TTC)

OSCAR

pythainlp.corpus.oscar.word_freqs() → List[Tuple[str, int]][source]: Get word frequency from OSCAR Corpus (icu word tokenize)

pythainlp.corpus.oscar.unigram_word_freqs() → defaultdict[source]: Get unigram word frequency from OSCAR Corpus (icu word tokenize)

Util

pythainlp.corpus.util.find_badwords(tokenize: Callable[[str], List[str]], training_data: Iterable[Iterable[str]]) → Set[str][source]

Find words that do not work well with the tokenize function for the provided training_data.

Parameters:

tokenize (Callable[[str], List[str]]) – a tokenize function
training_data (Iterable[Iterable[str]]) – tokenized text, to be used as a training set

Returns:

words that considered making tokenize perform unwell

Return type:

Set[str]

pythainlp.corpus.util.revise_wordset(tokenize: Callable[[str], List[str]], orig_words: Iterable[str], training_data: Iterable[Iterable[str]]) → Set[str][source]

Revise a set of word that could improve tokenization performance of a dictionary-based tokenize function.

orign_words will be used as a base set for the dictionary. Words that do not performed well with training_data will be removed. The remaining words will be returned.

Parameters:

tokenize (Callable[[str], List[str]]) – a tokenize function, can be any function that takes a string as input and returns a List[str]
orig_words (Iterable[str]) – words that used by the tokenize function, will be used as a base for revision
training_data (Iterable[Iterable[str]]) – tokenized text, to be used as a training set

Returns:

words that considered making tokenize perform unwell

Return type:

Set[str]

Example::

from pythainlp.corpus import thai_words from pythainlp.corpus.util import revise_wordset from pythainlp.tokenize.longest import segment

base_words = thai_words() more_words = {

“ถวิล อุดล”, “ทองอินทร์ ภูริพัฒน์”, “เตียง ศิริขันธ์”, “จำลอง ดาวเรือง”

} base_words = base_words.union(more_words) dict_trie = Trie(wordlist)

tokenize = lambda text: segment(text, dict_trie)

training_data = [: [str, str, str. …], [str, str, str, str, …], …

]

revised_words = revise_wordset(tokenize, wordlist, training_data)

pythainlp.corpus.util.revise_newmm_default_wordset(training_data: Iterable[Iterable[str]]) → Set[str][source]

Revise a set of word that could improve tokenization performance of pythainlp.tokenize.newmm, a dictionary-based tokenizer and a default tokenizer for PyThaiNLP.

Words from pythainlp.corpus.thai_words() will be used as a base set for the dictionary. Words that do not performed well with training_data will be removed. The remaining words will be returned.

Parameters:: training_data (Iterable[Iterable[str]]) – tokenized text, to be used as a training set
Returns:: words that considered making tokenize perform unwell
Return type:: Set[str]

WordNet

PyThaiNLP API is an exact copy of NLTK WordNet API. See: https://www.nltk.org/howto/wordnet.html

pythainlp.corpus.wordnet.synsets(word: str, pos: str | None = None, lang: str = 'tha')[source]

This function return the synonym sets for all lemmas given the word with an optional argument to constrain the part of speech of the word.

Parameters:

word (str) – word to find its synsets
pos (str) – the part of speech constraint (i.e. n for Noun, v for Verb, a for Adjective, s for Adjective satellites, and r for Adverb)
lang (str) – abbreviation of language (i.e. eng, tha). By default, it is tha

Returns:

Synset for all lemmas for the word constrained with the argument pos.

Return type:

list[Synset]

Example:

>>> from pythainlp.corpus.wordnet import synsets
>>>
>>> synsets("ทำงาน")
[Synset('function.v.01'), Synset('work.v.02'),
 Synset('work.v.01'), Synset('work.v.08')]
>>>
>>> synsets("บ้าน", lang="tha"))
[Synset('duplex_house.n.01'), Synset('dwelling.n.01'),
 Synset('house.n.01'), Synset('family.n.01'), Synset('home.n.03'),
 Synset('base.n.14'), Synset('home.n.01'),
 Synset('houseful.n.01'), Synset('home.n.07')]

When specifying the part of speech constrain. For example, the word “แรง” cound be interpreted as force (n.) or hard (adj.).

>>> from pythainlp.corpus.wordnet import synsets
>>> # By default, accept all part of speech
>>> synsets("แรง", lang="tha")
>>>
>>> # only Noun
>>> synsets("แรง", pos="n", lang="tha")
[Synset('force.n.03'), Synset('force.n.02')]
>>>
>>> # only Adjective
>>> synsets("แรง", pos="a", lang="tha")
[Synset('hard.s.10'), Synset('strong.s.02')]

pythainlp.corpus.wordnet.synset(name_synsets)[source]

This function return the synonym set (synset) given the name of synset (i.e. ‘dog.n.01’, ‘chase.v.01’).

Parameters:

name_synsets (str) – name of the sysset

Returns:

Synset of the given name

Return type:

Synset

Example:

>>> from pythainlp.corpus.wordnet import synset
>>>
>>> difficult = synset('difficult.a.01')
>>> difficult
Synset('difficult.a.01')
>>>
>>> difficult.definition()
'not easy; requiring great physical or mental effort to accomplish
           or comprehend or endure'

pythainlp.corpus.wordnet.all_lemma_names(pos: str | None = None, lang: str = 'tha')[source]

This function returns all lemma names for all synsets for the given part of speech tag and language. If part of speech tag is not specified, all synsets for all part of speech will be used.

Parameters:

pos (str) – the part of speech constraint (i.e. n for Noun, v for Verb, a for Adjective, s for Adjective satellites, and r for Adverb). By default, pos is None.
lang (str) – abbreviation of language (i.e. eng, tha). By default, it is tha.

Returns:

Synset of lemmas names given the pos and language

Return type:

list[Synset]

Example:

>>> from pythainlp.corpus.wordnet import all_lemma_names
>>>
>>> all_lemma_names()
['อเมริโก_เวสปุชชี',
 'เมืองชีย์เอนเน',
 'การรับเลี้ยงบุตรบุญธรรม',
 'ผู้กัด',
 'ตกแต่งเรือด้วยธง',
 'จิโอวานนิ_เวอร์จินิโอ',...]
>>>
>>> len(all_lemma_names())
80508
>>>
>>> all_lemma_names(pos="a")
['ซึ่งไม่มีแอลกอฮอล์',
 'ซึ่งตรงไปตรงมา',
 'ที่เส้นศูนย์สูตร',
 'ทางจิตใจ',...]
>>>
>>> len(all_lemma_names(pos="a"))
5277

pythainlp.corpus.wordnet.all_synsets(pos: str | None = None)[source]

This function iterates over all synsets constrained by given part of speech tag.

Parameters:

pos (str) – part of speech tag

Returns:

list of synsets constrained by given part of speech tag.

Return type:

Iterable[Synset]

Example:

>>> from pythainlp.corpus.wordnet import all_synsets
>>>
>>> generator = all_synsets(pos="n")
>>> next(generator)
Synset('entity.n.01')
>>> next(generator)
Synset('physical_entity.n.01')
>>> next(generator)
Synset('abstraction.n.06')
>>>
>>>  generator = all_synsets()
>>> next(generator)
Synset('able.a.01')
>>> next(generator)
Synset('unable.a.01')

pythainlp.corpus.wordnet.langs()[source]

This function return a set of ISO-639 language codes.

Returns:

ISO-639 language codes

Return type:

list[str]

Example:

>>> from pythainlp.corpus.wordnet import langs
>>> langs()
['eng', 'als', 'arb', 'bul', 'cat', 'cmn', 'dan',
 'ell', 'eus', 'fas', 'fin', 'fra', 'glg', 'heb',
 'hrv', 'ind', 'ita', 'jpn', 'nld', 'nno', 'nob',
 'pol', 'por', 'qcn', 'slv', 'spa', 'swe', 'tha',
 'zsm']

pythainlp.corpus.wordnet.lemmas(word: str, pos: str | None = None, lang: str = 'tha')[source]

This function returns all lemmas given the word with an optional argument to constrain the part of speech of the word.

Parameters:

word (str) – word to find its lammas
pos (str) – the part of speech constraint (i.e. n for Noun, v for Verb, a for Adjective, s for Adjective satellites, and r for Adverb)
lang (str) – abbreviation of language (i.e. eng, tha). By default, it is tha.

Returns:

Synset for all lemmas for the word constraine with the argument pos.

Return type:

list[Lemma]

Example:

>>> from pythainlp.corpus.wordnet import lemmas
>>>
>>> lemmas("โปรด")
[Lemma('like.v.03.โปรด'), Lemma('like.v.02.โปรด')]

>>> print(lemmas("พระเจ้า"))
[Lemma('god.n.01.พระเจ้า'), Lemma('godhead.n.01.พระเจ้า'),
 Lemma('father.n.06.พระเจ้า'), Lemma('god.n.03.พระเจ้า')]

When specify the part of speech tag.

>>> from pythainlp.corpus.wordnet import lemmas
>>>
>>> lemmas("ม้วน")
[Lemma('roll.v.18.ม้วน'), Lemma('roll.v.17.ม้วน'),
 Lemma('roll.v.08.ม้วน'),  Lemma('curl.v.01.ม้วน'),
 Lemma('roll_up.v.01.ม้วน'), Lemma('wind.v.03.ม้วน'),
 Lemma('roll.n.11.ม้วน')]
>>>
>>> # only lammas with Noun as the part of speech
>>> lemmas("ม้วน", pos="n")
[Lemma('roll.n.11.ม้วน')]

pythainlp.corpus.wordnet.lemma(name_synsets)[source]

This function return lemma object given the name.

Note

Support only English language (eng).

Parameters:

name_synsets (str) – name of the synset

Returns:

lemma object with the given name

Return type:

Lemma

Example:

>>> from pythainlp.corpus.wordnet import lemma
>>>
>>> lemma('practice.v.01.exercise')
Lemma('practice.v.01.exercise')
>>>
>>> lemma('drill.v.03.exercise')
Lemma('drill.v.03.exercise')
>>>
>>> lemma('exercise.n.01.exercise')
Lemma('exercise.n.01.exercise')

pythainlp.corpus.wordnet.lemma_from_key(key)[source]

This function returns lemma object given the lemma key. This is similar to lemma() but it needs to supply the key of lemma instead of the name.

Note

Support only English language (eng).

Parameters:

key (str) – key of the lemma object

Returns:

lemma object with the given key

Return type:

Lemma

Example:

>>> from pythainlp.corpus.wordnet import lemma, lemma_from_key
>>>
>>> practice = lemma('practice.v.01.exercise')
>>> practice.key()
exercise%2:41:00::
>>> lemma_from_key(practice.key())
Lemma('practice.v.01.exercise')

pythainlp.corpus.wordnet.path_similarity(synsets1, synsets2)[source]

This function returns similarity between two synsets based on the shortest path distance from the equation as follows.

\[path\_similarity = {1 \over shortest\_path\_distance(synsets1, synsets2) + 1}\]

The shortest path distance is calculated by the connection through the is-a (hypernym/hyponym) taxonomy. The score is in the ranage 0 to 1. Path similarity of 1 indicates identicality.

Parameters:

synsets1 (Synset) – first synset supplied to measures the path similarity
synsets2 (Synset) – second synset supplied to measures the path similarity

Returns:

path similarity between two synsets

Return type:

float

Example:

>>> from pythainlp.corpus.wordnet import path_similarity, synset
>>>
>>> entity = synset('entity.n.01')
>>> obj = synset('object.n.01')
>>> cat = synset('cat.n.01')
>>>
>>> path_similarity(entity, obj)
0.3333333333333333
>>> path_similarity(entity, cat)
0.07142857142857142
>>> path_similarity(obj, cat)
0.08333333333333333

pythainlp.corpus.wordnet.lch_similarity(synsets1, synsets2)[source]

This function returns Leacock Chodorow similarity (LCH) between two synsets, based on the shortest path distance and the maximum depth of the taxonomy. The equation to calculate LCH similarity is shown below:

\[lch\_similarity = {-log(shortest\_path\_distance(synsets1, synsets2) \over 2 * taxonomy\_depth}\]

Parameters:

synsets1 (Synset) – first synset supplied to measures the LCH similarity
synsets2 (Synset) – second synset supplied to measures the LCH similarity

Returns:

LCH similarity between two synsets

Return type:

float

Example:

>>> from pythainlp.corpus.wordnet import lch_similarity, synset
>>>
>>> entity = synset('entity.n.01')
>>> obj = synset('object.n.01')
>>> cat = synset('cat.n.01')
>>>
>>> lch_similarity(entity, obj)
2.538973871058276
>>> lch_similarity(entity, cat)
0.9985288301111273
>>> lch_similarity(obj, cat)
1.1526795099383855

pythainlp.corpus.wordnet.wup_similarity(synsets1, synsets2)[source]

This function returns Wu-Palmer similarity (WUP) between two synsets, based on the depth of the two senses in the taxonomy and their Least Common Subsumer (most specific ancestor node).

Parameters:

synsets1 (Synset) – first synset supplied to measures the WUP similarity
synsets2 (Synset) – second synset supplied to measures the WUP similarity

Returns:

WUP similarity between two synsets

Return type:

float

Example:

>>> from pythainlp.corpus.wordnet import wup_similarity, synset
>>>
>>> entity = synset('entity.n.01')
>>> obj = synset('object.n.01')
>>> cat = synset('cat.n.01')
>>>
>>> wup_similarity(entity, obj)
0.5
>>> wup_similarity(entity, cat)
0.13333333333333333
>>> wup_similarity(obj, cat)
0.35294117647058826

pythainlp.corpus.wordnet.morphy(form, pos: str | None = None)[source]

This function finds a possible base form for the given form, with the given part of speech.

Parameters:

form (str) – the form to finds the base form
pos (str) – part of speech tag of words to be searched

Returns:

base form of the given form

Return type:

str

Example:

>>> from pythainlp.corpus.wordnet import morphy
>>>
>>> morphy("dogs")
'dogs'
>>>
>>> morphy("thieves")
'thief'
>>>
>>> morphy("mixed")
'mix'
>>>
>>> morphy("calculated")
'calculate'

pythainlp.corpus.wordnet.custom_lemmas(tab_file, lang: str)[source]

This function reads a custom tab file (see: http://compling.hss.ntu.edu.sg/omw/) containing mappings of lemmas in the given language.

Parameters:

tab_file – Tab file as a file or file-like object
lang (str) – abbreviation of language (i.e. eng, tha).

Definition

Synset: a set of synonyms that share a common meaning.