Natural Language Checking with Program Checking Tools

Natural Language Checking with Program Checking Tools Fabrizio Perin, Lukas Renggli, Jorge Ressia

Programming Languages Parser Syntax Compiler Style

Programming Languages Parser Syntax Compiler Program Style Checker

Programming Natural Languages Languages Parser Syntax Compiler Program Style Checker

Programming Natural Languages Languages Spell Checker Parser Syntax Grammar Compiler Checker Program Style Checker

Programming Natural Languages Languages Spell Checker Parser Syntax Grammar Compiler Checker Program Style TextLint Checker

(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

(2) we implement an object-oriented model used to represent natural text in Smalltalk; .txt (3) we demonstrate a pattern matcher for the detection of style issues in natural language; and .html (4) we demonstrate a graphical user interface that presents and explains the .tex 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 L TEX or HTML commands depending on the filetype A (2) we implement an object-oriented model used to represent natural text in 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 Smalltalk; all elements know their source interval in the document. The relationship among the elements in the model are depicted in Figure 3. Element (3) we demonstrate a pattern matcher for the detection of style issues in text() interval() 1* 1* 1* natural language; and Document Paragraph Sentence Phrase 1 1 * SyntacticElement (4) we demonstrate a graphical user interface that presents and explains the * text() interval() problems detected by the tool. Word Punctuation Whitespace Fig. 3. The TextLint model and the relationships between its classes. Markup 3 From Strings to Objects Text Parsing To build the high-level document model from the flat input string we use Model 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 Validation Failures 4 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

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() 1* 1* 1* Document Paragraph Sentence Phrase 1 1 * SyntacticElement * text() interval() 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 Word Punctuation Whitespace Markup 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 Fig. 3. The TextLint model and the relationships between its classes. 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 Other Language the model are depicted in Figure 3. elements in Models Element text() interval() 1* 1* 1* Document Paragraph Sentence Phrase 1 1 * SyntacticElement * text() interval() 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 Word Punctuation Whitespace Markup 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 Fig. 3. The TextLint model and the relationships between its classes. Model Validation Failures Rules Styles GUI

Avoid "a lot" Avoid "irregardless" Avoid "a" Avoid "one of the most" Avoid "allow to" Avoid "regarded as" Avoid "an" Avoid "required to" Avoid "as to whether" Avoid "somehow" Avoid "can not" Avoid "stuff" Avoid "case" Avoid "the fact is" Avoid "certainly" Avoid "the fact that" Avoid "could" Avoid "the truth is" Avoid "currently" Avoid "thing" Avoid "different than" Avoid "thus" Avoid "doubt but" Avoid "true fact" Avoid "each and every one" Avoid "would" Avoid "enormity" Avoid comma Avoid "factor" Avoid connectors repetition Avoid "funny" Avoid continuous punctuation Avoid "help but" Avoid continuous word repetition Avoid "help to" Avoid contraction Avoid "however" Avoid joined sentences Avoid "importantly" raries: For parsing natural languages we use PetitParser [7], a flexible Avoid long paragraph Avoid "in order to" rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour Avoid long sentence Avoid "in regards to" , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. Avoid passive voice Avoid "in terms of" he contributions of this paper are: Avoid qualifier Avoid "insightful" 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 Avoid whitespace Smalltalk; Avoid "interesting" 3) we demonstrate a pattern matcher for the detection of style issues in Avoid word repetition 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

Avoid "a lot" Avoid "irregardless" Avoid "a" Avoid "one of the most" Avoid "allow to" Avoid "regarded as" Avoid "an" Avoid "required to" Avoid "as to whether" Avoid "somehow" Avoid "can not" Avoid "stuff" Avoid "case" Avoid "the fact is" Avoid "certainly" Avoid "the fact that" Avoid "could" Avoid "the truth is" Avoid "currently" Avoid "thing" Avoid "different than" (self word: ‘somehow’) Avoid "thus" Avoid "doubt but" Avoid "true fact" Avoid "each and every one" Avoid "would" Avoid "enormity" Avoid comma Avoid "factor" Avoid connectors repetition Avoid "funny" Avoid continuous punctuation Avoid "help but" Avoid continuous word repetition Avoid "help to" Avoid contraction Avoid "however" Avoid joined sentences Avoid "importantly" raries: For parsing natural languages we use PetitParser [7], a flexible Avoid long paragraph Avoid "in order to" rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour Avoid long sentence Avoid "in regards to" , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. Avoid passive voice Avoid "in terms of" he contributions of this paper are: Avoid qualifier Avoid "insightful" 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 Avoid whitespace Smalltalk; Avoid "interesting" 3) we demonstrate a pattern matcher for the detection of style issues in Avoid word repetition 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

Avoid "a lot" Avoid "irregardless" Avoid "a" Avoid "one of the most" Avoid "allow to" Avoid "regarded as" Avoid "an" Avoid "required to" Avoid "as to whether" Avoid "somehow" Avoid "can not" Avoid "stuff" Avoid "case" Avoid "the fact is" Avoid "certainly" Avoid "the fact that" Avoid "could" Avoid "the truth is" (self punctuation) , (self punctuation) Avoid "currently" Avoid "thing" Avoid "different than" Avoid "thus" Avoid "doubt but" Avoid "true fact" Avoid "each and every one" Avoid "would" Avoid "enormity" Avoid comma Avoid "factor" Avoid connectors repetition Avoid "funny" Avoid continuous punctuation Avoid "help but" Avoid continuous word repetition Avoid "help to" Avoid contraction Avoid "however" Avoid joined sentences Avoid "importantly" raries: For parsing natural languages we use PetitParser [7], a flexible Avoid long paragraph Avoid "in order to" rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour Avoid long sentence Avoid "in regards to" , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. Avoid passive voice Avoid "in terms of" he contributions of this paper are: Avoid qualifier Avoid "insightful" 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 Avoid whitespace Smalltalk; Avoid "interesting" 3) we demonstrate a pattern matcher for the detection of style issues in Avoid word repetition 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

Avoid "a lot" Avoid "irregardless" Avoid "a" Avoid "one of the most" Avoid "allow to" Avoid "regarded as" Avoid "an" Avoid "required to" Avoid "as to whether" Avoid "somehow" Avoid "can not" Avoid "stuff" Avoid "case" Avoid "the fact is" Avoid "certainly" Avoid "the fact that" (self wordIn: #('am' 'are' 'were' 'being' ... )) , Avoid "could" Avoid "the truth is" Avoid "currently" Avoid "thing" (self separator star) , Avoid "different than" Avoid "thus" ((self wordSatisfying: [ :value | value endsWith: 'ed' ]) / Avoid "doubt but" Avoid "true fact" Avoid "each and every one" Avoid "would" (self wordIn: #('awoken' 'been' 'born' 'beat' ... ))) Avoid "enormity" Avoid comma Avoid "factor" Avoid connectors repetition Avoid "funny" Avoid continuous punctuation Avoid "help but" Avoid continuous word repetition Avoid "help to" Avoid contraction Avoid "however" Avoid joined sentences Avoid "importantly" raries: For parsing natural languages we use PetitParser [7], a flexible Avoid long paragraph Avoid "in order to" rsing framework that makes it easy to define parsers and to dynamically use, compose, transform and extend grammars. Furthermore, we use Glamour Avoid long sentence Avoid "in regards to" , an engine for scripting browsers. Glamour reifies the notion of a browser d defines the flow of data between different user interface widgets. Avoid passive voice Avoid "in terms of" he contributions of this paper are: Avoid qualifier Avoid "insightful" 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 Avoid whitespace Smalltalk; Avoid "interesting" 3) we demonstrate a pattern matcher for the detection of style issues in Avoid word repetition 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

(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. scientificPaperStyle := TLTextLintRule allRules Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces -‐ TLWordRepetitionInParagraphRule 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

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

Validation

Issues Words t t1 t2 t3 t4 Fig. 6. Evolution of a paper from beginning to publication. 7.1 History of a Paper

-74% Avoid ‘currently’ -25% Avoid ‘certainly’ -24% Avoid ‘would’ -20% Avoid ‘factor’ -20% Avoid long paragraph -13% Avoid ‘thus’ -10% Avoid ‘however’ -7% Avoid ‘case’ -5% Avoid ‘can not’ -5% Avoid ‘could’ -4% Avoid passive voice -3% Avoid ‘insightful’ -3% Avoid ‘stuff’ -1% Avoid joined sentences Avoid ‘as to whether’ 0% Avoid ‘different than’ 0% Avoid ‘doubt but’ 0% Avoid ‘each and every one’ 0% Avoid ‘enormity’ 0% Avoid ‘help but’ 0% Avoid ‘in regards to’ 0% Avoid ‘irregardless’ 0% Avoid ‘regarded as’ 0% Avoid ‘the fact is’ 0% Avoid ‘the truth is’ 0% Avoid ‘true fact’ 0% Avoid comma 0% Avoid qualifier 2% Avoid ‘funny’ 5% Avoid ‘one of the most’ 5% Avoid ‘importantly’ 9% Avoid long sentence 10% Avoid ‘an’ 10% Avoid continuous punctuation 15% Avoid ‘interesting’ 17% Avoid ‘required to’ 17% Avoid ‘a’ 23% Avoid ‘in order to’ 23% Avoid continuous word repetition 24% Avoid ‘in terms of’ 24% Avoid ‘somehow’ 25% Avoid ‘help to’ 27% Avoid ‘the fact that’ 32% Avoid whitespace 45% Avoid ‘allow to’ 46% Avoid ‘a lot’ 55% Avoid ‘thing’ 70% Avoid contraction 73% Fig. 7. Effectiveness of various TextLint rules. a more in-depth discussion of tools that comment on writing style could be included.

Future Work ‣ Natural Language Model ‣ Styles for Other Domains ‣ More Rules

textlint.lukas-renggli.ch @textlint

Natural Language Checking with Program Checking Tools

by Lukas Renggli

Accessibility

Categories

Upload Details

Usage Rights

2 Embeds 2,673

Statistics

Natural Language Checking with Program Checking Tools Presentation Transcript

http://scg.unibe.ch	2671
http://translate.googleusercontent.com	2