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Performs cleaning operations on text
Category: Text Analytics
Applies to: Machine Learning Studio (classic) only
Similar drag-and-drop modules are available in Azure Machine Learning designer.
This article describes how to use the Preprocess Text module in Machine Learning Studio (classic), to clean and simplify text. By preprocessing the text, you can more easily create meaningful features from text.
For example, the Preprocess Text module supports these common operations on text:
- Removal of stop-words
- Using regular expressions to search for and replace specific target strings
- Lemmatization, which converts multiple related words to a single canonical form
- Filtering on specific parts of speech
- Case normalization
- Removal of certain classes of characters, such as numbers, special characters, and sequences of repeated characters such as "aaaa"
- Identification and removal of emails and URLs
You can choose which cleaning options to use, and optionally specify a custom list of stop-words.
The module currently supports six languages: English, Spanish, French, Dutch, German and Italian.
How to configure Text Preprocessing
Add the Preprocess Text module to your experiment in Studio (classic). You can find this module under Text Analytics.
Connect a dataset that has at least one column containing text.
If the text you are preprocessing is all in the same language, select the language from the Language dropdown list. With this option, the text is preprocessed using linguistic rules specific to the selected language.
To preprocess text that might contain multiple languages, choose the Column contains language option.
Then, use the Culture-language column property to choose a column in the dataset that indicates the language used in each row. The column must contain a standard language identifier, such as "English" or
Based on this identifier, the module applies appropriate linguistic resources to process the text.
If your dataset does not contain such identifiers, use the Detect Language module to analyze the language beforehand, and generate an identifier.
An error is raised if an unsupported language is included. See the Technical notes section for more information.
Remove by part of speech: Select this option if you want to apply part-of-speech analysis. You can then use the part-of-speech tags to remove certain classes of words.
- Remove nouns: Select this option to remove nouns.
- Remove adjectives: Select this option to remove adjectives.
- Remove verbs: Select this option to remove verbs.
For more information about the part-of-speech identification method used, see the Technical notes section.
Text column to clean: Select the column or columns that you want to preprocess.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Stopword lists are language dependent and customizable; for more information, see the Technical notes section.
Lemmatization: Select this option if you want words to be represented in their canonical form. This option is useful for reducing the number of unique occurrences of otherwise similar text tokens.
The lemmatization process is highly language-dependent; see the Technical notes section for details.
Detect sentences: Select this option if you want the module to insert a sentence boundary mark when performing analysis.
This module uses a series of three pipe characters
|||to represent the sentence terminator.
Optionally, you can perform custom find-and-replace operations using regular expressions.
- Custom regular expression: Define the text you are searching for.
- Custom replacement string: Define a single replacement value.
Normalize case to lowercase: Select this option if you want to convert ASCII uppercase characters to their lowercase forms.
If characters are not normalized, the same word in uppercase and lowercase letters is considered two different words: for example,
AMis the same as
Optionally, you can remove the following types of characters or character sequences from the processed output text:
- Remove numbers: Select this option to remove all numeric characters for the specified language.
Identification of what constitutes a number is domain dependent and language dependent. If numeric characters are an integral part of a known word, the number might not be removed.
Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe
For more about special characters, see the Technical notes section.
Remove duplicate characters: Select this option to remove any sequences that repeat characters. For example, a sequence like "aaaaa" would be removed.
Remove email addresses: Select this option to remove any sequence of the format
Remove URLs: Select this option to remove any sequence that includes the following URL prefixes:
Expand verb contractions: This option applies only to languages that use verb contractions; currently, English only.
For example, by selecting this option, you could replace the phrase "wouldn't stay there" with "would not stay there".
Normalize backslashes to slashes: Select this option to map all instances of
Split tokens on special characters: Select this option if you want to break words on characters such as
-, and so forth.
For example, the string
MS-WORDwould be separated into two tokens,
The following examples in the Azure AI Gallery illustrate the use of the Preprocess Text module:
This section provides more information about the underlying text pre-processing technology, and how to specify custom text resources.
Currently Machine Learning supports text preprocessing in these languages:
Additional languages are planned. See the Microsoft Machine Learning blog for announcements.
Lemmatization is the process of identifying a single canonical form to represent multiple word tokens.
The natural language processing libraries included in Machine Learning Studio (classic) combine the following multiple linguistic operations to provide lemmatization:
Sentence separation: In free text used for sentiment analysis and other text analytics, sentences are frequently run-on or punctuation might be missing. Input texts might constitute an arbitrarily long chunk of text, ranging from a tweet or fragment to a complete paragraph, or even document.
The natural language tools used by Studio (classic) perform sentence separation as part of the underlying lexical analysis. However, sentences are not separated in the output. Optionally, you can specify that a sentence boundary be marked to aid in other text processing and analysis.
Tokenization: The rules that determine the boundaries of words are language-dependent and can be complex even in languages that use spaces between words.
Some languages (such as Chinese or Japanese) do not use any white space between words, and separation of words requires morphological analysis.
Therefore, the tokenization methods and rules used in this module provide different results from language to language. These tokenization rules are determined by text analysis libraries provided by Microsoft Research for each supported language, and cannot be customized.
Part-of-speech identification: In any sequence of words, it can be difficult to computationally identify the exact part of speech for each word. Even an apparently simple sentence such as "Time flies like an arrow" can have many dozen parses (a famous example). Parts of speech are also very different depending on the morphology of different languages.
In Machine Learning, a disambiguation model is used to choose the single most likely part of speech, given the current sentence context. The part-of-speech information is used to help filter words used as features and aid in key-phrase extraction. However, the output of this module does not explicitly include POS tags and therefore cannot be used to generate POS-tagged text.
Generating dictionary form: A word may have multiple lemmas, or dictionary forms, each coming from a different analysis. For instance, the English word building has two possible lemmas: building if the word is a noun ("the tall building"), or build if the word is a verb ("they are building a house"). In Machine Learning, only the single most probable dictionary form is generated.
Sample lemmatization output
|Source||Lemmatized with case conversion|
|He is swimming||he i swim|
|He is going for a swim||he i go for a swim|
|Swimming is good for building muscle||swim be good for build muscle|
|He is building a building||he i build a build|
|We are all building buildings||we be all build building|
The language models used to generate dictionary form have been trained and tested against a variety of general purpose and technical texts, and are used in many other Microsoft products that require natural language APIs. However, natural language is inherently ambiguous and 100% accuracy on all vocabulary is not feasible. For example, lemmatization can be affected by other parts of speech, or by the way that the sentence is parsed.
If you need to perform additional pre-processing, or perform linguistic analysis using a specialized or domain-dependent vocabulary, we recommend that you use customizable NLP tools, such as those available in Python and R.
Special characters are defined as single characters that cannot be identified as any other part of speech, and can include punctuation: colons, semi-colons, and so forth.
A stop word (or stopword) is a word that is often removed from indexes because it is common and provides little value for information retrieval, even though it might be linguistically meaningful.
For example, many languages make a semantic distinction between definite and indefinite articles ("the building" vs "a building"), but for machine learning and information retrieval, the information is sometimes not relevant. Hence you might decide to discard these words.
The Machine Learning environment includes lists of the most common stopwords for each of the supported languages.
|Language||Number of stopwords||Examples|
|Dutch||49||aan, af, al|
|English||312||a, about, above|
|French||154||de, des, d', la|
|German||602||a, ab, aber|
|Italian||135||a, adesso, ai|
|Spanish||368||ésa, ésta, éste|
For your convenience, a zipped file containing the default stopwords for all current languages has been made available in Azure storage: Stopwords.zip.
How to modify the stopword list
We expect that many users want to create their own stopword lists, or change the terms included in the default list. The following experiment in the Cortana Intelligence Gallery demonstrates how you can customize a stop word list.
If you modify the list, or create your own stop word list, observe these requirements:
The file must contain a single text column. You might get the following error if an additional column is present: "Preprocess Text Error Column selection pattern "Text column to clean" is expected to provide 1 column(s) selected in input dataset, but 2 column(s) is/are actually provided. ( Error 0022 )"
If this happens, look for spaces, tabs, or hidden columns present in the file from which the stopword list was originally imported. Depending on how the file was prepared, tabs or commas included in text can also cause multiple columns to be created.
Each row can contain only one word. For the purposes of parsing the file, words are determined by insertion of spaces.
The stopword list cannot be empty.
Order of operations
In this module, you can apply multiple operations to text. However, the order in which these operations are applied cannot be changed. This can affect the expected results.
For example, if you apply lemmatization to text, and also use stopword removal, all the words are converted to their lemma forms before the stopword list is applied. Therefore, if your text includes a word that is not in the stopword list, but its lemma is in the stopword list, the word would be removed.
Be sure to test target terms in advance to guarantee the correct results.
If your text column includes languages not supported by Machine Learning, we recommend that you use only those options that do not require language-dependent processing. This can help avoid strange results.
Also, if you use the option Column contains language, you must ensure that no unsupported languages are included in the text that is processed. If an unsupported language or its identifier is present in the dataset, the following run-time error is generated:
"Preprocess Text Error (0039): Please specify a supported language."
To avoid failing the entire experiment because an unsupported language was detected, use the Split Data module, and specify a regular expression to divide the dataset into supported and unsupported languages.
For example, the following regular expression splits the dataset based on the detected language for the column
\"Sentence Language" Dutch|English|French|Italian|Spanish
If you have a column that contains the language identifier, or if you have generated such a column, you can use a regular expression such as the following to filter on the identifier column:
\"Sentence Iso6391 Name" nl|en|fr|it|es
|Dataset||Data Table||Input data|
|Stop words||Data Table||Optional custom list of stop words to remove|
|Required||English||Select the language to preprocess|
|Text column to clean||Column Selection||Required||StringFeature||Select the text column to clean|
|Custom regular expression||String||Optional||Specify the custom regular expression|
|Custom replacement string||String||Optional||Specify the custom replacement string for the custom regular expression|
|Remove stop words||Boolean||Required||true||Remove stop words|
|Remove by part of speech||True False Type||true
|Required||False||Indicate whether part-of-speech analysis should be used to identify and remove certain word classes|
|Remove nouns||Boolean||Applies when the Filter by part of speech option is selected||true||Remove nouns|
|Remove adjectives||Boolean||Applies when the Filter by part of speech option is selected||true||Remove adjectives|
|Remove verbs||Boolean||Applies when the Filter by part of speech option is selected||true||Remove verbs|
|Detect sentences||Boolean||Required||true||Detect sentences by adding a sentence terminator \"|||\" that can be used by the n-gram features extractor module|
|Normalize case to lowercase||Boolean||Required||true||Normalize case to lowercase|
|Remove numbers||Boolean||Required||true||Remove numbers|
|Remove special characters||Boolean||Required||true||Remove non-alphanumeric special characters and replace them with \"|\" character|
|Remove duplicate characters||Boolean||Required||true||Remove duplicate characters|
|Remove email addresses||Boolean||Required||true||Remove email addresses|
|Results dataset||Data Table||Results dataset|
|Error 0003||An exception occurs if one or more of inputs are null or empty.|
|Error 0030||an exception occurs in when it is not possible to download a file.|
|Error 0048||An exception occurs when it is not possible to open a file.|
|Error 0049||An exception occurs when it is not possible to parse a file.|
For a list of errors specific to Studio (classic) modules, see Machine Learning Error codes.
For a list of API exceptions, see Machine Learning REST API Error Codes.