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Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
Natural Language Checking with Program Checking Tools
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Natural Language Checking with Program Checking Tools

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Written text is an important component in the process of knowledge acquisition and communication. Poorly written text fails to deliver clear ideas to the reader no matter how revolutionary and …

Written text is an important component in the process of knowledge acquisition and communication. Poorly written text fails to deliver clear ideas to the reader no matter how revolutionary and ground-breaking these ideas are. Providing text with good writing style is essential to transfer ideas smoothly. While we have sophisticated tools to check for stylistic problems in program code, we do not apply the same techniques for written text. In this paper we present TextLint, a rule-based tool to check for common style errors in natural language. TextLint provides a structural model of written text and an extensible rule-based checking mechanism.

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  • 1. Natural Language Checking with Program Checking Tools Fabrizio Perin, Lukas Renggli, Jorge Ressia
  • 2. SyntaxStyle Programming Languages Parser Compiler
  • 3. Program Checker Parser Compiler SyntaxStyle Programming Languages
  • 4. Program Checker Parser Compiler SyntaxStyle Programming Languages Natural Languages
  • 5. Program Checker Parser Compiler Spell Checker Grammar Checker SyntaxStyle Programming Languages Natural Languages
  • 6. Program Checker TextLint Parser Compiler Spell Checker Grammar Checker SyntaxStyle Programming Languages Natural Languages
  • 7. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 8. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in .txt .html .tex
  • 9. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 10. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in · The Markup models LATEX or HTML commands depending on the filetype of the input. All document elements answer the message text which returns a plain string representation of the modeled text entity ignoring markup tokens. Furthermore all elements know their source interval in the document. The relationship among the elements in the model are depicted in Figure 3. Element text() interval() Document Paragraph Sentence Phrase 1 * 1 * 1 * SyntacticElement text() interval() Word Punctuation Whitespace Markup 1 * 1 * Fig. 3. The TextLint model and the relationships between its classes. 3 From Strings to Objects To build the high-level document model from the flat input string we use PetitParser [7]. PetitParser is a framework targeted at parsing formal languages (e.g., programming languages), but we employ it in this project to parse natural 4
  • 11. raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI representation of the modeled text entity ignoring markup tokens. Furthermore all elements know their source interval in the document. The relationship among the elements in the model are depicted in Figure 3. Element text() interval() Document Paragraph Sentence Phrase 1 * 1 * 1 * SyntacticElement text() interval() Word Punctuation Whitespace Markup 1 * 1 * Fig. 3. The TextLint model and the relationships between its classes.
  • 12. raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI representation of the modeled text entity ignoring markup tokens. Furthermore all elements know their source interval in the document. The relationship among the elements in the model are depicted in Figure 3. Element text() interval() Document Paragraph Sentence Phrase 1 * 1 * 1 * SyntacticElement text() interval() Word Punctuation Whitespace Markup 1 * 1 * Fig. 3. The TextLint model and the relationships between its classes.
  • 13. raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI representation of the modeled text entity ignoring markup tokens. Furthermore all elements know their source interval in the document. The relationship among the elements in the model are depicted in Figure 3. Element text() interval() Document Paragraph Sentence Phrase 1 * 1 * 1 * SyntacticElement text() interval() Word Punctuation Whitespace Markup 1 * 1 * Fig. 3. The TextLint model and the relationships between its classes.
  • 14. Other Language Models raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI representation of the modeled text entity ignoring markup tokens. Furthermore all elements know their source interval in the document. The relationship among the elements in the model are depicted in Figure 3. Element text() interval() Document Paragraph Sentence Phrase 1 * 1 * 1 * SyntacticElement text() interval() Word Punctuation Whitespace Markup 1 * 1 * Fig. 3. The TextLint model and the relationships between its classes.
  • 15. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 16. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 17. Avoid "a lot" Avoid "a" Avoid "allow to" Avoid "an" Avoid "as to whether" Avoid "can not" Avoid "case" Avoid "certainly" Avoid "could" Avoid "currently" Avoid "different than" Avoid "doubt but" Avoid "each and every one" Avoid "enormity" Avoid "factor" Avoid "funny" Avoid "help but" Avoid "help to" Avoid "however" Avoid "importantly" Avoid "in order to" Avoid "in regards to" Avoid "in terms of" Avoid "insightful" Avoid "interesting" Avoid "irregardless" Avoid "one of the most" Avoid "regarded as" Avoid "required to" Avoid "somehow" Avoid "stuff" Avoid "the fact is" Avoid "the fact that" Avoid "the truth is" Avoid "thing" Avoid "thus" Avoid "true fact" Avoid "would" Avoid comma Avoid connectors repetition Avoid continuous punctuation Avoid continuous word repetition Avoid contraction Avoid joined sentences Avoid long paragraph Avoid long sentence Avoid passive voice Avoid qualifier Avoid whitespace Avoid word repetition raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI
  • 18. Avoid "a lot" Avoid "a" Avoid "allow to" Avoid "an" Avoid "as to whether" Avoid "can not" Avoid "case" Avoid "certainly" Avoid "could" Avoid "currently" Avoid "different than" Avoid "doubt but" Avoid "each and every one" Avoid "enormity" Avoid "factor" Avoid "funny" Avoid "help but" Avoid "help to" Avoid "however" Avoid "importantly" Avoid "in order to" Avoid "in regards to" Avoid "in terms of" Avoid "insightful" Avoid "interesting" Avoid "irregardless" Avoid "one of the most" Avoid "regarded as" Avoid "required to" Avoid "somehow" Avoid "stuff" Avoid "the fact is" Avoid "the fact that" Avoid "the truth is" Avoid "thing" Avoid "thus" Avoid "true fact" Avoid "would" Avoid comma Avoid connectors repetition Avoid continuous punctuation Avoid continuous word repetition Avoid contraction Avoid joined sentences Avoid long paragraph Avoid long sentence Avoid passive voice Avoid qualifier Avoid whitespace Avoid word repetition raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI (self  word:  ‘somehow’)
  • 19. Avoid "a lot" Avoid "a" Avoid "allow to" Avoid "an" Avoid "as to whether" Avoid "can not" Avoid "case" Avoid "certainly" Avoid "could" Avoid "currently" Avoid "different than" Avoid "doubt but" Avoid "each and every one" Avoid "enormity" Avoid "factor" Avoid "funny" Avoid "help but" Avoid "help to" Avoid "however" Avoid "importantly" Avoid "in order to" Avoid "in regards to" Avoid "in terms of" Avoid "insightful" Avoid "interesting" Avoid "irregardless" Avoid "one of the most" Avoid "regarded as" Avoid "required to" Avoid "somehow" Avoid "stuff" Avoid "the fact is" Avoid "the fact that" Avoid "the truth is" Avoid "thing" Avoid "thus" Avoid "true fact" Avoid "would" Avoid comma Avoid connectors repetition Avoid continuous punctuation Avoid continuous word repetition Avoid contraction Avoid joined sentences Avoid long paragraph Avoid long sentence Avoid passive voice Avoid qualifier Avoid whitespace Avoid word repetition raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI (self  punctuation)  ,  (self  punctuation)
  • 20. Avoid "a lot" Avoid "a" Avoid "allow to" Avoid "an" Avoid "as to whether" Avoid "can not" Avoid "case" Avoid "certainly" Avoid "could" Avoid "currently" Avoid "different than" Avoid "doubt but" Avoid "each and every one" Avoid "enormity" Avoid "factor" Avoid "funny" Avoid "help but" Avoid "help to" Avoid "however" Avoid "importantly" Avoid "in order to" Avoid "in regards to" Avoid "in terms of" Avoid "insightful" Avoid "interesting" Avoid "irregardless" Avoid "one of the most" Avoid "regarded as" Avoid "required to" Avoid "somehow" Avoid "stuff" Avoid "the fact is" Avoid "the fact that" Avoid "the truth is" Avoid "thing" Avoid "thus" Avoid "true fact" Avoid "would" Avoid comma Avoid connectors repetition Avoid continuous punctuation Avoid continuous word repetition Avoid contraction Avoid joined sentences Avoid long paragraph Avoid long sentence Avoid passive voice Avoid qualifier Avoid whitespace Avoid word repetition raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI (self  wordIn:  #('am'  'are'  'were'  'being'  ...  ))  ,   (self  separator  star)  ,   ((self  wordSatisfying:  [  :value  |  value  endsWith:  'ed'  ])  /    (self  wordIn:  #('awoken'  'been'  'born'  'beat'  ...  )))
  • 21. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 22. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in scientificPaperStyle  :=  TLTextLintRule  allRules -­‐  TLWordRepetitionInParagraphRule
  • 23. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 24. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 25. (2) we implement an object-oriented model used to represent natural text in Smalltalk; (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and (4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI Fig. 2. Data Flow through TextLint. Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces the natural text model of TextLint and Section 3 details how text documents are parsed and the model is composed. Section 4 presents the rules which model the stylistic checks. Section 5 describes how stylistic rules are defined in
  • 26. raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI
  • 27. raries: For parsing natural languages we use PetitParser [7], a flexible rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. he contributions of this paper are: 1) we apply ideas from program checking to the domain of natural language; 2) we implement an object-oriented model used to represent natural text in Smalltalk; 3) we demonstrate a pattern matcher for the detection of style issues in natural language; and 4) we demonstrate a graphical user interface that presents and explains the problems detected by the tool. Text Parsing Model Validation Failures Rules Styles GUI
  • 28. Validation
  • 29. t t1 t2 t3 t4 Issues Words Fig. 6. Evolution of a paper from beginning to publication. 7.1 History of a Paper
  • 30. Avoid‘currently’-74% Avoid‘certainly’-25% Avoid‘would’-24% Avoid‘factor’-20% Avoidlongparagraph-20% Avoid‘thus’-13% Avoid‘however’-10% Avoid‘case’-7% Avoid‘cannot’-5% Avoid‘could’-5% Avoidpassivevoice-4% Avoid‘insightful’-3% Avoid‘stuff’-3% Avoidjoinedsentences-1% Avoid‘astowhether’0% Avoid‘differentthan’0% Avoid‘doubtbut’0% Avoid‘eachandeveryone’0% Avoid‘enormity’0% Avoid‘helpbut’0% Avoid‘inregardsto’0% Avoid‘irregardless’0% Avoid‘regardedas’0% Avoid‘thefactis’0% Avoid‘thetruthis’0% Avoid‘truefact’0% Avoidcomma0% Avoidqualifier2% Avoid‘funny’5% Avoid‘oneofthemost’5% Avoid‘importantly’9% Avoidlongsentence10% Avoid‘an’10% Avoidcontinuouspunctuation15% Avoid‘interesting’17% Avoid‘requiredto’17% Avoid‘a’23% Avoid‘inorderto’23% Avoidcontinuouswordrepetition24% Avoid‘intermsof’24% Avoid‘somehow’25% Avoid‘helpto’27% Avoid‘thefactthat’32% Avoidwhitespace45% Avoid‘allowto’46% Avoid‘alot’55% Avoid‘thing’70% Avoidcontraction73% Fig.7.EffectivenessofvariousTextLintrules. amorein-depthdiscussionoftoolsthatcommentonwritingstylecouldbeincluded.
  • 31. Future Work ‣ Natural Language Model ‣ Styles for Other Domains ‣ More Rules
  • 32. textlint.lukas-renggli.ch @textlint

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