service name description parameters returns example call text length limit
declension Declension of noun phrases
This service returns a the input phrase in a different case and/or number.
text
type string desc the noun phrase you want to inflect
n
type string desc output number: Sing-singular, Plur-plural, empty-do not change number
c2
type string desc output case: 1-nominative, 2-genitive, 3-dative, 4-accusative, 5-vocative, 6-locative, 7-instrumental
response
type string desc the input phrase in output case and number
service.py?call=declension&lang=cs&output=json&text=velký růžový slon&c2=4&n=Plur 500
diacritics Get string with diacritics added
This service returns the phrase with the most probable diacritics added.
text
type string desc text without diacritics
text
type string desc text with diacritics
service.py?call=diacritics&lang=cs&output=json&text=dama v treti rade 1000
gen Word forms generator
For the input word (lemma, base form) returns all inflected forms with tags (tagset reference is here or here )
case
type string desc Case (c+number or Nom, Gen, Dat, Acc, Voc, Loc, Ins or name nominative, genitive, ...)
polarity
type string desc Polarity (eA, eN or Pos, Neg)
animacy
type string desc Animacy of masculine (Anim or Inan)
degree
type string desc Degree of adjectives (d1, d2, d3 or Pos, Cmp, Sup)
gender
type string desc Gender (gM, gF, gI, gN or Masc, Fem, Neut)
number
type string desc Number (nP, nS or Plur, Sing)
lemma
type string desc Input word (lemma, base form)
std
type string desc Standard language (true, false, wH, std or col)
forms
type string desc Input lemma, input constraints and list of words and grammar tags
service.py?call=gen&lang=cs&output=json&lemma=divoký&gender=Neut 40
guesser
For the input word returns a possible lemma and tag (see tagset description here )
text
type string desc Input word
forms
type string desc Output lemma and possible tag
service.py?call=guesser&lang=cs&output=json&text=křovinořez 40
hello
This service returns a greeting. It's practically useless since it is only an example.
name
type string desc the person you want to greet or 'World' if not provided
response
type string desc a greeting
service
type string desc service name
service.py?call=hello&lang=en&output=json&text=Dolly 40
inflection Inflection of words
This service returns all input words in the specified form (comma separated list of tag-values).
text
type string desc list of lemmata (comma separated)
tag
type string desc list of tag-values (comma separated)
Lemma
type string desc one input lemma
Annot
type list desc word and tag annotations
service.py?call=inflection&lang=cs&output=json&text=cesta,jed&tag=c7,nS 1000
logic
This service processes an input sentence and returns logical construction in Transparent Intensional Logic of the first possible syntactic tree.
text construction
type list desc The list of constructions
service.py?call=logic&lang=cs&output=json&text=Ministr dopravy ČR navštívil Ostravsko. 1000
majka Morphological analysis of words
This service returns all lemmata and morphological tags for each of the input words (comma separated).
text
type string desc list of words (comma separated)
Word
type string desc one input word
Annot
type list desc lemma and tag annotations
service.py?call=majka&lang=cs&output=json&text=cestou,jedem 1000
ner Czech Named Entity Recognizer.
Detects names of persons, institutions, geopolitical units, dates, media names, addresses, and other named entities.
text result
type list desc List of entities with their type and boundaries.
service.py?call=ner&lang=cs&output=json&text=Od prosince 2019 jsme na adrese Svatopetrská 35/7, 617 00 Brno. Vyvíjíme se, rosteme, a proto dáváme sbohem Podnikatelskému inkubátoru a míříme přímo do Inovačního centra Svatopetrská, jehož hlavním partnerem jsme právě my v TopGisu. Nová adresa v Komárově však s sebou přináší i další pozitiva. 100000
netagger Get tags for tokens, get lemmata for Czech tokens
This service splits the sentences into tokens, and determines tags for each tokens. For some tokens, it detects lemma using majka - the Czech morphological dictionary.
text result
type list desc Each sentence contains tokens, each token contains word, tag, possibly lemma and tagger average confidence over all grammar tags.
service.py?call=netagger&lang=cs&output=json&text=Na okně seděla kočka, byl krásný letní den. Na okně seděla kočka a koukala se ven. 100000
phrases Extract (sub)phrases
This service processes an input sentence and returns all phrases identified in the text (and their tags).
text phrases
type list desc The list of phrases, i.e. lists of phrase text and tag.
service.py?call=phrases&lang=cs&output=json&text=Konečně levný mobil - nástupce kompaktního modelu má být velkým lákadlem 1000
polite
This service returns politeness level of texts.
text politeness
type string desc politeness level: one of polite, rude, veryrude
rudewords
type list desc list of rude words identified in the text
service.py?call=polite&lang=cs&output=json&text=Tento text obsahuje nepěkné slovo kretén. 1000
sa Sentiment Analysis.
Detects sentiment in sentences.
text
type string desc Text - if you want sentiment per sentence, put each sentence on a new line, otherwise, the overall sentiment is calcualted.
result
type list desc List of sentences with their sentiment.
service.py?call=sa&lang=cs&output=json&text=Tohle je katastrofální selhání. 100000
sholva
This service returns negative or positive membership of the word in the following classes: person, person-individual, event, substance.
text concepts
type list desc The output format is a list of positive or negative memberships to some (or all) of the classes.
service.py?call=sholva&lang=cs&output=json&text=šampaňské 1000
tagger
This service splits the text into sentences, split the sentences into tokens, and determines lemma and tag for each token.
lang
type string desc language code
text vertical
type list desc The vertical format is a list of lists, each item in the list is either a structural token (1-item list) such as sentence start or end, or a 3-item list [word, lemma, tag] .
service.py?call=tagger&lang=cs&output=json&text=Na okně seděla kočka, byl krásný letní den. Na okně seděla kočka a koukala se ven. 100000
topics Get topics of a text
This service returns a list of topics found in a short text. The topic candidates are noun phrases ordered by importance.
text response
type list desc list of noun phrases ordered by score
service.py?call=topics&lang=cs&output=json&text=Jednoho letního večera na návsi pod starou lípou hostinský Antonín Kučera vyvalil soudeček s pípou, nebylo to posvícení, nebyla to neděle, v naší obci mezi kopci plnily se korbele. Byl to ten slavný den, kdy k nám byl zaveden elektrický proud, Byl to ten slavný den, kdy k nám byl zaveden elektrický proud, střídavý, střídavý, silný elektrický proud, střídavý, střídavý, zkrátka elektrický proud. 1000
vocative Get vocative for a name
This service returns vocative case for a name plus gender if possible.
text
type string desc a person name (first names, surnames)
gender
type string desc gender of the name (m, f, or nothing)
last name
type string desc the name in vocative case
first
type string desc first name
service.py?call=vocative&lang=cs&output=json&text=Jan Petr Obr 200