Exploring the role of lexis and grammar for the stable identification of register in an unrestricted corpus of web documents.
The Internet offers great possibilities for many scientific disciplines that utilize text data. However, the potential of online data can be limited by the lack of information on the genre or register of the documents, as register—whether a text is, e.g., a news article or a recipe—is arguably the m...
| Publicado en: | Language Resources & Evaluation Vol. 55; no. 3; pp. 757 - 789 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Sep2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=151686297&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 151686297 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2021 vid: 55 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 151686297 10.1007/s10579-020-09519-z ppf: 757 ppct: 32 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.7MB tig: atl: Exploring the role of lexis and grammar for the stable identification of register in an unrestricted corpus of web documents. aug: au: Laippala, Veronika Egbert, Jesse Biber, Douglas Kyröläinen, Aki-Juhani affil: University of Turku, Turku, Finland Northern Arizona University, Flagstaff, USA McMaster University & Brock University, Hamilton, ON, Canada su: Grammar Automatic identification Corpora Forecasting Linguists sug: subj: Grammar Automatic identification Corpora Forecasting Linguists keyword: Discriminative features Model stability Online data Online registers SVM Text classification Web genre identification Web genres Web-as-corpus ab: The Internet offers great possibilities for many scientific disciplines that utilize text data. However, the potential of online data can be limited by the lack of information on the genre or register of the documents, as register—whether a text is, e.g., a news article or a recipe—is arguably the most important predictor of linguistic variation (see Biber in Corpus Linguist Linguist Theory 8:9–37, 2012). Despite having received significant attention in recent years, the modeling of online registers has faced a number of challenges, and previous studies have presented contradictory results. In particular, these have concerned (1) the extent to which registers can be automatically identified in a large, unrestricted corpus of web documents and (2) the stability of the models, specifically the kinds of linguistic features that achieve the best performance while reflecting the registers instead of corpus idiosyncrasies. Furthermore, although the linguistic properties of registers vary importantly in a number of ways that may affect their modeling, this variation is often bypassed. In this article, we tackle these issues. We model online registers in the largest available corpus of online registers, the Corpus of Online Registers of English (CORE). Additionally, we evaluate the stability of the models towards corpus idiosyncrasies, analyze the role of different linguistic features in them, and examine how individual registers differ in these two aspects. We show that (1) competitive classification performance on a large-scale, unrestricted corpus can be achieved through a combination of lexico-grammatical features, (2) the inclusion of grammatical information improves the stability of the model, whereas many of the previously best-performing feature sets are less stable, and that (3) registers can be placed in a continuum based on the discriminative importance of lexis and grammar. These register-specific characteristics can explain the variation observed in previous studies concerning the automatic identification of online registers and the importance of different linguistic features for them. Thus, our results offer explanations for the jungle-likeness of online data and provide essential information on online registers for all studies using online data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2021. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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