Word Segmentation_Natural Language Processing_API Reference_NLP APIs_NLP Fundamentals APIs

Introduction

This API is used to segment words in the text.

For details about endpoints, see Endpoints.

URI

URI format

POST /v1/{project_id}/nlp-fundamental/segment

Parameter description
Table 1 URI parameters
Parameter

Mandatory

Description

project_id

Yes

Project ID. For details about how to obtain the project ID, see Obtaining a Project ID.

**Table 1** URI parameters
Parameter	Mandatory	Description
project_id	Yes	Project ID. For details about how to obtain the project ID, see Obtaining a Project ID.

Request

Table 2 describes the request parameters.

**Table 2** Request parameters
Parameter	Type	Mandatory	Description
text	String	Yes	Text to be segmented. The text is encoded using UTF-8 and contains 1 to 2,000 characters.
pos_switch	Integer	No	Whether to enable part-of-speech tagging (POS tagging). The options are 1 (yes) and 0 (no). The default value is 0.
lang	String	No	Supported text language type. English (en) is now supported.
criterion	String	No	Supported word segmentation criterion The default word segmentation criterion for English text is Penn TreeBank. You do not need to configure this parameter.

Response

Table 3 describes the response parameters.

**Table 3** Response parameters
Parameter	Type	Description
words	Array of words	Word segmentation result. For details, see Table 4.
error_code	String	Error code when the API fails to be called. For details, see Error Code. The parameter is not included when the API call succeeds.
error_msg	String	Error message returned when the API fails to be called. The parameter is not included when the API call succeeds.

**Table 4** Word field data structure
Parameter	Type	Description
content	String	Word text.
pos	String	Lexical character corresponding to a word. For details, see Table 5, Table 6, and Table 7.

**Table 5** Part of speech (POS) description (PKU)
Class-1 POS	Class-2 POS	Class-3 POS
n: Noun	nr: Name of a person	nr1: Chinese surname nr2: Chinese given name nrj: Japanese name nrf: Transliterated name
	ns: Place name	nsf: Transliterated place name
	nt: Organization or group name	-
	nz: Other exclusive name	-
	nl: Nominal locution	-
	ng: Nominal morpheme	-
t: Time word	tg: Time morpheme	-
s: Locative word	-	-
f: Positional word	-	-
v: Verb	vd: Adverbial form of a verb	-
	vn: Gerund	-
	vshi: Copula verb	-
	vyou: Verb indicating "has/have"	-
	vf: Directional verb	-
	vx: Formal verb	-
	vi: Intransitive verb	-
	vl: Verbal locution	-
	vg: Verbal morpheme	-
a: Adjective	ad: Adverbial adjective	-
	an: Nominal adjective	-
	ag: Adjective morpheme	-
	al: Adjective locution	-
b: Distinguishing word	bl: Distinguishing locution	-
z: Status word	-	-
r: Pronoun	rr: Personal pronoun	-
	rz: Demonstrative pronoun	rzt: Demonstrative pronoun for time rzs: Demonstrative pronoun for location rzv: Demonstrative pronoun for predicate
	ry: Interrogative pronoun	ryt: Interrogative pronoun for time rys: Interrogative pronoun for location ryv: Interrogative pronoun for predicate
	rg: Pronominal morpheme	-
m: Numeral	mq: Number word	-
m: Numeral	mg: A, B, C, D, E, F, G, H, N, and G	-
q: Classifier	qv: Verbal classifier	-
q: Classifier	qt: Time classifier	-
d: Adverb	-	-
p: Preposition	pba: Preposition ba	-
p: Preposition	pbei: Preposition bei	-
c: Conjunction	cc: Coordinating conjunction	-
u: Particle	uzhe: Particle	-
	ule: Particle	-
	uguo: Particle	-
	ude1: Particle	-
	ude2: Particle	-
	ude3: Particle	-
	usuo: Particle	-
	udeng: Particle	-
	uyy: Particle	-
	udh: Particle	-
	uls: Particle	-
	uzhi: Particle	-
	ulian: Particle	-
e: Exclamation	-	-
y: Discourse word	-	-
o: Onomatopoeia	-	-
h: Prefix	-	-
k: Suffix	-	-
x: character string	xe: Email character string	-
	xs: Weibo session separator	-
	xm: Emoticon	-
	xu: Website URL	-
w: Punctuation	wkz: Chinese left brackets	-
	wky: Chinese right brackets	-
	wyz: Chinese left quotation marks	-
	wyy: Chinese right quotation marks	-
	wj: Chinese full stop	-
	ww: Question marks	-
	wt: Exclamation marks	-
	wd: Commas	-
	wf: Semicolons	-
	wn: Enumeration comma	-
	wm: Colons	-
	ws: Ellipsis	-
	wp: Dashes	-
	wb: Percentile and permil	-
	wh: Unit	-

**Table 6** POS description (CTB)
POS	Description	Example
AD	Adverb	word-1, word-2, word-3
AS	Dynamic particle	word-4, word-5, word-6
BA	"ba" structure	word-7
CC	Coordinating conjunction	word-8, word-9
CD	Quantifier	One, two, three
CS	Subordinating conjunction	Although, if, when
DEC	Complement or nominalization	word-10, word-11
DEG	Conjunctive or possessive	word-12, word-13
DER	Complement de	de
DEV	Adverb di	di
DT	Determiner	word-14, word-15, word-16
ETC	word-17	word-17, word-18
FW	Loanword	A E B
IJ	Exclamation	word-18, word-19
JJ	Modifier for noun	Big, new, small
LB	Long bei structure	word-20, word-21, word-22
LC	Positional word	middle, upper
M	Classifier	Unit, year, dollar
MSP	Particle	Particle-1, particle-2, particle-3
NN	Noun	Economy, enterprise, person
NR	Proper noun	China, Zhejiang
NT	Time noun	Present, last year
OD	Numeral	First, second, top
ON	Onomatopoeia	O
P	Preposition	Preposition-1, preposition-2, preposition-3
PN	Pronoun	He, I, myself
PU	Punctuation	Chinese comma, Chinese full stop
SB	Short bei structure	word-23, word-24
SP	Particle at the end of a sentence	Particle-1, particle-2, particle-3
VA	Predicative adjective	Big, many, good
VC	Linking verb	Verb-1, verb-2, verb-3
VE	Verb indicating "has/have"	Verb-4, verb-5, verb-6
VV	Verb	Verb-7, verb-8, verb-9

**Table 7** POS description (Penn TreeBank)
POS	Description	Example
CC	Coordinating conjunction	and, but, or
CD	Cardinal number	one, two
DT	Determiner	a, the
EX	There be, to exist	there
FW	Foreign word	mea, culpa
IN	Preposition, subordinating conjunction	of, in, by
JJ	Adjective	yellow
JJR	Comparative form of adjectives	bigger
JJS	Superlative form of adjectives	wildest
LS	List item marker	1, 2, One
MD	Modal verb	can, could, might
NN	Noun, countable or uncountable	llama
NNS	Noun, in plural form	llamas
NNP	Proper noun, in singular form	IBM
NNPS	Proper noun, in plural form	Carolinas
PDT	Predeterminer	all, both
POS	Possessive adjective	's
PRP	Personal pronoun	I, me, you,
PRP$	Possessive pronoun	my, your, yours
RB	Adverb	quickly
RBR	Comparative form of adverbs	faster
RBS	Superlative form of adverbs	fastest
RP	Particle	up, off
SYM	Sign (mathematics or science)	+, % ,&
TO	to	to
UH	Exclamation	ah, oops
VB	Basic form of verbs	eat
VBD	Past tense of verbs	ate
VBG	Gerund or present participle	eating
VBN	Past participle	eaten
VBP	Non-third person singular form of verbs	eat
VBZ	Third person singular form of verbs	eats
WDT	wh-determiner	which, that
WP	wh-pronoun	what, who
WP$	wh-possessive pronoun	whose
WRB	wh-adverb	how, where
PU	Punctuation	, . :

Example

Example request

POST https://nlp-ext.ap-southeast-3.myhuaweicloud.com/v1/{project_id}/nlp-fundamental/segment

Request Header:
    Content-Type: application/json
    X-Auth-Token: MIINRwYJKoZIhvcNAQcCoIINODCCDTQCAQExDTALBglghkgBZQMEAgEwgguVBgkqhkiG...

Request Body:
    {    
        "text":"today is a good day.", 
        "pos_switch":1,    
        "lang":"en",
        "criterion":"PKU"
}

Example response

Successful response example

{
    "words": [
        {
            "content": "today",
            "pos": "NN"
        },
        {
            "content": "is",
            "pos": "VBZ"
        },
        {
            "content": "a",
            "pos": "DT"
        },
        {
            "content": "good",
            "pos": "JJ"
        },
        {
            "content": "day",
            "pos": "NN"
        },
        {
            "content": ".",
            "pos": "PU"
        }
    ]
}

Failed response example

{
    "error_code": "NLP.0301",
    "error_msg": "The length of text should be in the range of 1-512"
}

Status code

For details about status codes, see Status Code.

Error Code

For details about error codes, see Error Code.

Word Segmentation

Introduction

URI

Request

Response

Example

Status code

Error Code

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