Add one character string to rip off from tokens, such as parenthesis or question marks. Ignored if using prefix, infix or suffix patterns.

One character string to split tokens inside, such as hyphens. Ignored if using infix, prefix or suffix patterns.

Annotator that identifies points of analysis in a useful manner

List of 1 character string to rip off from tokens, such as parenthesis or question marks. Ignored if using prefix, infix or suffix patterns.

Gets annotation column name going to generate

Regex to identify subtokens that come in the beginning of the token. Regex has to start with \\A and must contain groups (). Each group will become a separate token within the prefix. Defaults to non-letter characters. e.g. quotes or parenthesis

List of 1 character string to split tokens inside, such as hyphens. Ignored if using infix, prefix or suffix patterns.

List of 1 character string to split tokens inside, such as hyphens. Ignored if using infix, prefix or suffix patterns.

Regex to identify subtokens that are in the end of the token. Regex has to end with \\z and must contain groups (). Each group will become a separate token within the prefix. Defaults to non-letter characters. e.g. quotes or parenthesis

Basic regex rule to identify a candidate for tokenization. Defaults to \\S+ which means anything not a space

Regex patterns that match tokens within a single target. groups identify different sub-tokens. multiple defaults

Infix patterns must use regex group. Notice each group will result in separate token

Example:

import org.apache.spark.ml.Pipeline
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.DocumentAssembler

val textDf = sqlContext.sparkContext.parallelize(Array("l'une d'un l'un, des l'extrême des l'extreme")).toDF("text")
val documentAssembler = new DocumentAssembler().setInputCol("text").setOutputCol("sentences")
val tokenizer = new Tokenizer().setInputCols("sentences").setOutputCol("tokens").setInfixPatterns(Array("([\\p{L}\\w]+'{1})([\\p{L}\\w]+)"))
new Pipeline().setStages(Array(documentAssembler, tokenizer)).fit(textDf).transform(textDf).select("tokens.result").show(false)

This will yield: l', une, d', un, l', un, , , des, l', extrême, des, l', extreme

Input annotator type : DOCUMENT

columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

Output annotator type : TOKEN

Regex with groups and begins with \\A to match target prefix. Overrides contextCharacters Param

List of 1 character string to rip off from tokens, such as parenthesis or question marks. Ignored if using prefix, infix or suffix patterns.

List of tokens to not alter at all. Allows composite tokens like two worded tokens that the user may not want to split.

Set a list of Regex patterns that match tokens within a single target. Groups identify different sub-tokens. multiple defaults

Overrides required annotators column if different than default

Overrides annotation column name when transforming

Regex to identify subtokens that come in the beginning of the token. Regex has to start with \\A and must contain groups (). Each group will become a separate token within the prefix. Defaults to non-letter characters. e.g. quotes or parenthesis

List of 1 character string to split tokens inside, such as hyphens. Ignored if using infix, prefix or suffix patterns.

Regex pattern to separate from the inside of tokens. Takes priority over splitChars.

Regex to identify subtokens that are in the end of the token. Regex has to end with \\z and must contain groups (). Each group will become a separate token within the prefix. Defaults to non-letter characters. e.g. quotes or parenthesis

Pattern to separate from the inside of tokens. takes priority over splitChars.

This pattern will be applied to the tokens which where extracted with the target pattern previously

Example:

import org.apache.spark.ml.Pipeline

import com.johnsnowlabs.nlp.annotators.Tokenizer

import com.johnsnowlabs.nlp.DocumentAssembler

val textDf = sqlContext.sparkContext.parallelize(Array("Tokens in this-text will#be#split on hashtags-and#dashes")).toDF("text")

val documentAssembler = new DocumentAssembler().setInputCol("text").setOutputCol("sentences")

val tokenizer = new Tokenizer().setInputCols("sentences").setOutputCol("tokens").setSplitPattern("-|#")

new Pipeline().setStages(Array(documentAssembler, tokenizer)).fit(textDf).transform(textDf).select("tokens.result").show(false)

This will yield: Tokens, in, this, text, will, be, split, on, hashtags, and, dashes

Regex with groups and ends with \\z to match target suffix. Overrides contextCharacters Param

Pattern to grab from text as token candidates. (Default: "\\S+")

Defaults to: "\\S+" which means anything not a space will be matched and considered as a token candidate, This will cause text to be split on on white spaces to yield token candidates.

This rule will be added to the BREAK_PATTERN varaible, which is used to yield token candidates.

import org.apache.spark.ml.Pipeline
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.DocumentAssembler

val textDf = sqlContext.sparkContext.parallelize(Array("I only consider lowercase characters and NOT UPPERCASED and only the numbers 0,1, to 7 as tokens but not 8 or 9")).toDF("text")
val documentAssembler = new DocumentAssembler().setInputCol("text").setOutputCol("sentences")
val tokenizer = new Tokenizer().setInputCols("sentences").setOutputCol("tokens").setTargetPattern("a-z-0-7")
new Pipeline().setStages(Array(documentAssembler, tokenizer)).fit(textDf).transform(textDf).select("tokens.result").show(false)

This will yield: only, consider, lowercase, characters, and, and, only, the, numbers, 0, 1, to, 7, as, tokens, but, not, or

Clears out rules and constructs a new rule for every combination of rules provided . The strategy is to catch one token per regex group. User may add its own groups if needs targets to be tokenized separately from the rest

requirement for pipeline transformation validation. It is called on fit()

takes a Dataset and checks to see if all the required annotation types are present.