tokenizers

Documentation

Overview

Example

Output:

Default Mode:
Term: 永和 Start: 0 End: 6 Position: 1 Type: 1
Term: 服装 Start: 6 End: 12 Position: 2 Type: 1
Term: 饰品 Start: 12 End: 18 Position: 3 Type: 1
Term: 有限公司 Start: 18 End: 30 Position: 4 Type: 1
Search Mode:
Term: 永和 Start: 0 End: 6 Position: 1 Type: 1
Term: 服装 Start: 6 End: 12 Position: 2 Type: 1
Term: 饰品 Start: 12 End: 18 Position: 3 Type: 1
Term: 有限 Start: 18 End: 24 Position: 4 Type: 1
Term: 公司 Start: 24 End: 30 Position: 5 Type: 1
Term: 有限公司 Start: 18 End: 30 Position: 6 Type: 1

Example (BeleveSearch)

Index

• Constants
• func JiebaTokenizerConstructor(config map[string]interface{}, cache *registry.Cache) (analysis.Tokenizer, error)
• func NewJiebaTokenizer(dictFilePath string, hmm, searchMode bool) (analysis.Tokenizer, error)
• type JiebaTokenizer
  • func (jt *JiebaTokenizer) Tokenize(input []byte) analysis.TokenStream

Examples

• Package
• Package (BeleveSearch)

Constants

const Name = "jieba"

Name is the jieba tokenizer name.

Functions

func JiebaTokenizerConstructor

func JiebaTokenizerConstructor(config map[string]interface{}, cache *registry.Cache) (
	analysis.Tokenizer, error)

JiebaTokenizerConstructor creates a JiebaTokenizer.

Parameter config should contains at least one parameter:

file: the path of the dictionary file.

hmm: optional, specify whether to use Hidden Markov Model, see NewJiebaTokenizer for details.

search: optional, speficy whether to use search mode, see NewJiebaTokenizer for details.

func NewJiebaTokenizer

func NewJiebaTokenizer(dictFilePath string, hmm, searchMode bool) (analysis.Tokenizer, error)

NewJiebaTokenizer creates a new JiebaTokenizer.

Parameters:

dictFilePath: path of the dictioanry file.

hmm: whether to use Hidden Markov Model to cut unknown words,
i.e. not found in dictionary. For example word "安卓" (means "Android" in
English) not in the dictionary file. If hmm is set to false, it will be
cutted into two single words "安" and "卓", if hmm is set to true, it will
be traded as one single word because Jieba using Hidden Markov Model with
Viterbi algorithm to guess the best possibility.

searchMode: whether to further cut long words into serveral short words.
In Chinese, some long words may contains other words, for example "交换机"
is a Chinese word for "Switcher", if sechMode is false, it will trade
"交换机" as a single word. If searchMode is true, it will further split
this word into "交换", "换机", which are valid Chinese words.

Types

type JiebaTokenizer

type JiebaTokenizer struct {
	// contains filtered or unexported fields
}

JiebaTokenizer is the beleve tokenizer for jiebago.

func (*JiebaTokenizer) Tokenize

func (jt *JiebaTokenizer) Tokenize(input []byte) analysis.TokenStream

Tokenize cuts input into bleve token stream.