Exploring the role of lexis and grammar for the stable identification of register in an unrestricted corpus of web documents

Abstract

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.

Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight Monolingual Classification of Registers

“…Previous studies have shown repeatedly that registers vary considerably in terms of how well they are linguistically defined and thus how well they can be automatically identified Egbert, 2018, 2016a;Laippala et al, 2020a). For instance, while texts in the IN (Informational description) and NA (Narrative) classes, such as Encyclopedia articles and Sports reports, have very distinctive characteristics and can be identified with a very high reliability, others, such as Information blogs in the IN class or Advice in the OP (Opinion) class receive much lower scores.…”

“…Biber (2014) showed that registers, such as spoken texts, display functional similarities across languages, which obviously is needed for highquality transfer in register identification. However, analyzing the English CORE registers, Laippala et al (2020a) noted that some registers, such as many blogs, depend highly on lexical characteristics reflecting the discussion topics. These topics, however, may vary extensively between languages.…”

“…Very recently, however, the advances brought to NLP by neural networks have shown that registers can be identified also in a corpus featuring the full range of online language variation (Laippala et al, 2020a). Laippala et al (2019) extended the possibilities of web register identification beyond English by presenting an online register corpus on Finnish (FinCORE) and demonstrating that web registers can be modeled also in a cross-lingual setting.…”

The Effect of Internet Corpus Learning on Students’ Japanese Learning Motivations and Learning Effect

“…Spring (2018) created a concise and comprehensive list of the verb phrase and their meanings, through theoretical knowledge and the concept of corpora. Laippala et al (2021) modeled online registration in the largest available online register corpus, Corpus of Online Registers of English (CORE). Besides, the estimation is implemented on the stability of the model on corpus features, an analysis is conducted on the role of different language features in it, and the differences in individual registers are examined in these two aspects.…”

The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild

“…Despite the considerable benefits of Automatic Genre Identification (AGI), no established classification exists (Sharoff, 2010). The genre researchers are not consistent in the use of terminology, and they refer to genres, text types, functional text dimensions or registers in different ways (Sharoff, 2018;Egbert et al, 2015;Laippala et al, 2021;Lee, 2002). Furthermore, there is no consensus on the genre definition.…”