Add part-of-speech tags to documents
Use addPartOfSpeechDetails to add part-of-speech tags to
documents.
The function supports English, Japanese, German, and Korean text.
updatedDocuments = addPartOfSpeechDetails(documents)documents and updates the token
details. The function, by default, retokenizes the text for part-of-speech tagging.
For example, the function splits the word "you're" into the tokens "you" and "'re".
To get the part-of-speech details from updatedDocuments, use
tokenDetails.
updatedDocuments = addPartOfSpeechDetails(documents,Name,Value)
Tip
Use addPartOfSpeechDetails before using the
lower, upper,
erasePunctuation,
normalizeWords, removeWords,
and removeStopWords functions as
addPartOfSpeechDetails uses information that is removed by
these functions.
Load the example data. The file sonnetsPreprocessed.txt contains preprocessed versions of Shakespeare's sonnets. The file contains one sonnet per line, with words separated by a space. Extract the text from sonnetsPreprocessed.txt, split the text into documents at newline characters, and then tokenize the documents.
filename = "sonnetsPreprocessed.txt";
str = extractFileText(filename);
textData = split(str,newline);
documents = tokenizedDocument(textData);View the token details of the first few tokens.
tdetails = tokenDetails(documents); head(tdetails)
ans=8×5 table
Token DocumentNumber LineNumber Type Language
___________ ______________ __________ _______ ________
"fairest" 1 1 letters en
"creatures" 1 1 letters en
"desire" 1 1 letters en
"increase" 1 1 letters en
"thereby" 1 1 letters en
"beautys" 1 1 letters en
"rose" 1 1 letters en
"might" 1 1 letters en
Add part-of-speech details to the documents using the addPartOfSpeechDetails function. This function first adds sentence information to the documents, and then adds the part-of-speech tags to the table returned by tokenDetails. View the updated token details of the first few tokens.
documents = addPartOfSpeechDetails(documents); tdetails = tokenDetails(documents); head(tdetails)
ans=8×7 table
Token DocumentNumber SentenceNumber LineNumber Type Language PartOfSpeech
___________ ______________ ______________ __________ _______ ________ ______________
"fairest" 1 1 1 letters en adjective
"creatures" 1 1 1 letters en noun
"desire" 1 1 1 letters en verb
"increase" 1 1 1 letters en noun
"thereby" 1 1 1 letters en adverb
"beautys" 1 1 1 letters en verb
"rose" 1 1 1 letters en noun
"might" 1 1 1 letters en auxiliary-verb
Tokenize Japanese text using tokenizedDocument.
str = [
"恋に悩み、苦しむ。"
"恋の悩みで 苦しむ。"
"空に星が輝き、瞬いている。"
"空の星が輝きを増している。"
"駅までは遠くて、歩けない。"
"遠くの駅まで歩けない。"
"すもももももももものうち。"];
documents = tokenizedDocument(str);For Japanese text, you can get the part-of-speech details using tokenDetails. For English text, you must first use addPartOfSpeechDetails.
tdetails = tokenDetails(documents); head(tdetails)
ans=8×8 table
Token DocumentNumber LineNumber Type Language PartOfSpeech Lemma Entity
_______ ______________ __________ ___________ ________ ____________ _______ __________
"恋" 1 1 letters ja noun "恋" non-entity
"に" 1 1 letters ja adposition "に" non-entity
"悩み" 1 1 letters ja verb "悩む" non-entity
"、" 1 1 punctuation ja punctuation "、" non-entity
"苦しむ" 1 1 letters ja verb "苦しむ" non-entity
"。" 1 1 punctuation ja punctuation "。" non-entity
"恋" 2 1 letters ja noun "恋" non-entity
"の" 2 1 letters ja adposition "の" non-entity
Tokenize German text using tokenizedDocument.
str = [
"Guten Morgen. Wie geht es dir?"
"Heute wird ein guter Tag."];
documents = tokenizedDocument(str)documents =
2x1 tokenizedDocument:
8 tokens: Guten Morgen . Wie geht es dir ?
6 tokens: Heute wird ein guter Tag .
To get the part of speech details for German text, first use addPartOfSpeechDetails.
documents = addPartOfSpeechDetails(documents);
To view the part of speech details, use the tokenDetails function.
tdetails = tokenDetails(documents); head(tdetails)
ans=8×7 table
Token DocumentNumber SentenceNumber LineNumber Type Language PartOfSpeech
________ ______________ ______________ __________ ___________ ________ ____________
"Guten" 1 1 1 letters de adjective
"Morgen" 1 1 1 letters de noun
"." 1 1 1 punctuation de punctuation
"Wie" 1 2 1 letters de adverb
"geht" 1 2 1 letters de verb
"es" 1 2 1 letters de pronoun
"dir" 1 2 1 letters de pronoun
"?" 1 2 1 punctuation de punctuation
documents — Input documentstokenizedDocument arrayInput documents, specified as a tokenizedDocument array.
Specify optional
comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN.
'DiscardKnownValues',true specifies to discard
previously computed details and recompute them.'RetokenizeMethod' — Method to retokenize documents'part-of-speech' (default) | 'none'Method to retokenize documents, specified as one of the following:
'part-of-speech' – Transform the tokens
for part-of-speech tagging. The function performs these
tasks:
Split compound words. For example, split the
compound word "wanna" into the
tokens "want" and
"to". This includes compound
words containing apostrophes. For example, the
function splits the word "don't"
into the tokens "do" and
"n't".
Merge periods that do not end sentences with
preceding tokens. For example, merge the tokens
"Mr" and "."
into the token "Mr.".
For German text, merge abbreviations that span
multiple tokens. For example, merge the tokens
"z", ".",
"B", and "."
into the single token "z.
B.".
Merge runs of periods into ellipses. For example,
merge three instances of "." into
the single token "...".
'none' – Do not retokenize the
documents.
'Abbreviations' — List of abbreviationsList of abbreviations for sentence detection, specified as a string array, character vector, cell array of character vectors, or a table.
If the input documents do not contain sentence details, then the
function first runs the addSentenceDetails function and specifies the
abbreviation list given by 'Abbreviations'. To
specify more options for sentence detection (for example, sentence
starters) use the addSentenceDetails function
before using addPartOfSpeechDetails details.
If Abbreviations is a string array, character vector, or cell array of character vectors, then the function treats these as regular abbreviations. If the next word is a capitalized sentence starter, then the function breaks at the trailing period. The function ignores any differences in the letter case of the abbreviations. Specify the sentence starters using the Starters name-value pair.
To specify different behaviors when splitting sentences at abbreviations, specify Abbreviations as a table. The table must have variables named Abbreviation and Usage, where Abbreviation contains the abbreviations, and Usage contains the type of each abbreviation. The following table describes the possible values of Usage, and the behavior of the function when passed abbreviations of these types.
| Usage | Behavior | Example Abbreviation | Example Text | Detected Sentences |
|---|---|---|---|---|
regular | If the next word is a capitalized sentence starter, then break at the trailing period. Otherwise, do not break at the trailing period. | "appt." | "Book an appt. We'll meet then." |
|
"Book an appt. today." | "Book an appt. today." | |||
inner | Do not break after trailing period. | "Dr." | "Dr. Smith." | "Dr. Smith." |
reference | If the next token is not a number, then break at a trailing period. If the next token is a number, then do not break at the trailing period. | "fig." | "See fig. 3." | "See fig. 3." |
"Try a fig. They are nice." |
| |||
unit | If the previous word is a number and the following word is a capitalized sentence starter, then break at a trailing period. | "in." | "The height is 30 in. The width is 10 in." |
|
| If the previous word is a number and the following word is not capitalized, then do not break at a trailing period. | "The item is 10 in. wide." | "The item is 10 in. wide." | ||
| If the previous word is not a number, then break at a trailing period. | "Come in. Sit down." |
|
The default value is the output of the abbreviations function. For Japanese and Korean text, abbreviations do not
usually impact sentence detection.
Tip
By default, the function treats single letter abbreviations, such as "V.", or tokens with mixed single letters and periods, such as "U.S.A." as regular abbreviations. You do not need to include these abbreviations in Abbreviations.
Data Types: char | string | table | cell
'DiscardKnownValues' — Option to discard previously computed detailsfalse (default) | trueOption to discard previously computed details and recompute them, specified as
true or false.
Data Types: logical
updatedDocuments — Updated documentstokenizedDocument arrayUpdated documents, returned as a tokenizedDocument array. To get the token details from
updatedDocuments, use tokenDetails.
The addPartOfSpeechDetails function adds
part-of-speech tags to the table returned by the tokenDetails function. The function tags each token with a
categorical tag with one of the following class names:
"adjective" – Adjective
"adposition" – Adposition
"adverb" – Adverb
"auxiliary-verb" – Auxiliary verb
"coord-conjunction" – Coordinating conjunction
"determiner" – Determiner
"interjection" – Interjection
"noun" – Noun
"numeral" – Numeral
"particle" – Particle
"pronoun" – Pronoun
"proper-noun" – Proper noun
"punctuation" – Punctuation
"subord-conjunction" – Subordinating conjucntion
"symbol" – Symbol
"verb" – Verb
"other" – Other
If the input documents do not contain sentence details, then the function first runs
addSentenceDetails.
addEntityDetails | addLanguageDetails | addLemmaDetails | addSentenceDetails | addTypeDetails | normalizeWords | tokenDetails | tokenizedDocument
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