The Discriminativeness of Internal Syntactic Representations in Automatic Genre Classification

Genre characterizes a document differently from a subject that has been the focus of most document retrieval and classification applications. This work hypothesizes a close interaction between syntactic variation and genre differentiation by introspecting stylistic cues in functional and structural...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of quantitative linguistics Ročník 28; číslo 2; s. 138 - 171
Hlavní autoři: Wan, Mingyu, Fang, Alex Chengyu, Huang, Chu-Ren
Médium: Journal Article
Jazyk:angličtina
Vydáno: Lisse Routledge 03.04.2021
Taylor & Francis Ltd
Témata:
Academic writing
Classification
Computer generated language analysis
Cues
Differentiation
Discrimination
Error analysis
Fiction
Genre
Linguistic complexity
Machine learning
News media
Noun phrases
Phrases
Prepositional phrases
Retrieval
Speech
Speeches
Structural aspects
Syntactic features
Syntactic structures
Writing
ISSN:0929-6174, 1744-5035
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Genre characterizes a document differently from a subject that has been the focus of most document retrieval and classification applications. This work hypothesizes a close interaction between syntactic variation and genre differentiation by introspecting stylistic cues in functional and structural aspects beyond word level. It has engineered 14 syntactic feature sets of internal representations for genre classification through Machine Learning devices. Experiment results show significant superiority of fusing structural and lexical features for genre classification (F ∆max. = 9.2%, sig. = 0.001), suggesting the effectiveness of incorporating syntactic cues for genre discrimination. In addition, the PCA analysis reports the noun phrases (NP) as the most principle component (66%) for genre variation and prepositional phrases (PP) the second. Particularly, noun phrases with dominant structures of prepositional complements and pronouns functioning as a subject are most effective for identifying printed texts of high formality, while prepositional phrases are useful for identifying speeches of low formality. Error analysis suggests that the phrasal features are particularly useful for classifying four groups of genre classes, i.e. unscripted speech, fiction, news reports, and academic writing, all distributed with distinct structural characteristics, and they demonstrate an incremental degree of formality in the continuum of language complexity.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0929-6174
1744-5035
DOI:10.1080/09296174.2019.1663655