A comparative analysis of metal subgenres in terms of lexical richness and keyness

Metal music is realized under a vast variety of subgenres all of which have their unique (or shared) characteristics not only in sound but also in their lyrics. Much research has been done to distinguish or classify subgenres but little has addressed the linguistic differences across them. This study seeks to find out the lexical richness and keyness levels of heavy metal, thrash metal and death metal using a corpus of 200 songs from each subgenre with a total of 600 songs. The selection of the bands and songs was carried out finding references in the metal literature. The metal literature in the present study takes into account the academic books and articles on metal as well as noteworthy media productions, websites and metal blogs such as Metal Evolution and Encyclopaedia Metallum. The song lyrics were manually processed and meta-data, mark-ups and repeats have been removed so that the differences in repeat lengths do not affect the comparisons. Furthermore, the analyses used in the study are sensitive to repeats as they measure the frequencies and repeat ratios of the words. The song lengths – after the processing – were limited to lower and upper thresholds of 100 and 400 words. The songs were analyzed for their lexical richness levels in three aspects: 1) lexical variation, 2) lexical sophistication and 3) lexical density. Lexical variation was operationalized as TTR, Guiraud, Uber and HD-D. Lexical sophistication was measured using lexical frequency profile with two different frequency lists – the GSL and the BNC/COCA – by looking at the ratios of tokens and types which fell beyond the most frequent two thousand words (Laufer 1995). Another sophistication measure – P_Lex – which also runs on GSL, was applied. Lexical density analysis was based on the ratio of content words to all tokens in the texts. In order to complement this quantitative and data-driven approach, a keyness analysis was administered to add a qualitative dimension to the research. All lexical richness analyses pointed out to statistically significant differences between all subgenres, marking heavy metal as the least and death metal as the most lexically rich one. Keyness analysis indicated differences among all three subgenres as well. Heavy metal key words tended to be Dionysian whereas thrash and death metal keywords were more Chaotic as proposed by Weinstein (2000). Finally, a correlation analysis showed that all lexical richness measures were statistically significantly correlated to each other. Based on the findings, it could be claimed that 1) these three subgenres differ from each other not only in terms of music but also of lexical richness levels and key words and 2) lexical richness analyses, coupled with keyness, are capable of reflecting the genre differences in song lyrics. However, as a result of a discriminant analysis of the present corpus, a reverse approach whereby genres are attempted to be classified based on lexical features does not provide a pattern which fully corresponds to the existing classifications

A comparative analysis of metal subgenres in terms of lexical richness and keyness

Metal music is realized under a vast variety of subgenres all of which have their unique (or shared) characteristics not only in sound but also in their lyrics. Much research has been done to distinguish or classify subgenres but little has addressed the linguistic differences across them. This study seeks to find out the lexical richness and keyness levels of heavy metal, thrash metal and death metal using a corpus of 200 songs from each subgenre with a total of 600 songs. The selection of the bands and songs was carried out finding references in the metal literature. The metal literature in the present study takes into account the academic books and articles on metal as well as noteworthy media productions, websites and metal blogs such as Metal Evolution and Encyclopaedia Metallum. The song lyrics were manually processed and meta-data, mark-ups and repeats have been removed so that the differences in repeat lengths do not affect the comparisons. Furthermore, the analyses used in the study are sensitive to repeats as they measure the frequencies and repeat ratios of the words. The song lengths – after the processing – were limited to lower and upper thresholds of 100 and 400 words. The songs were analyzed for their lexical richness levels in three aspects: 1) lexical variation, 2) lexical sophistication and 3) lexical density. Lexical variation was operationalized as TTR, Guiraud, Uber and HD-D. Lexical sophistication was measured using lexical frequency profile with two different frequency lists – the GSL and the BNC/COCA – by looking at the ratios of tokens and types which fell beyond the most frequent two thousand words (Laufer 1995). Another sophistication measure – P_Lex – which also runs on GSL, was applied. Lexical density analysis was based on the ratio of content words to all tokens in the texts. In order to complement this quantitative and data-driven approach, a keyness analysis was administered to add a qualitative dimension to the research. All lexical richness analyses pointed out to statistically significant differences between all subgenres, marking heavy metal as the least and death metal as the most lexically rich one. Keyness analysis indicated differences among all three subgenres as well. Heavy metal key words tended to be Dionysian whereas thrash and death metal keywords were more Chaotic as proposed by Weinstein (2000). Finally, a correlation analysis showed that all lexical richness measures were statistically significantly correlated to each other. Based on the findings, it could be claimed that 1) these three subgenres differ from each other not only in terms of music but also of lexical richness levels and key words and 2) lexical richness analyses, coupled with keyness, are capable of reflecting the genre differences in song lyrics. However, as a result of a discriminant analysis of the present corpus, a reverse approach whereby genres are attempted to be classified based on lexical features does not provide a pattern which fully corresponds to the existing classifications

Enabling Auditing and Intrusion Detection of Proprietary Controller Area Networks

The goal of this dissertation is to provide automated methods for security researchers to overcome ‘security through obscurity’ used by manufacturers of proprietary Industrial Control Systems (ICS). `White hat\u27 security analysts waste significant time reverse engineering these systems\u27 opaque network configurations instead of performing meaningful security auditing tasks. Automating the process of documenting proprietary protocol configurations is intended to improve independent security auditing of ICS networks. The major contributions of this dissertation are a novel approach for unsupervised lexical analysis of binary network data flows and analysis of the time series data extracted as a result. We demonstrate the utility of these methods using Controller Area Network (CAN) data sampled from passenger vehicles

Valency and transitivity in a contact variety: the evidence from Cameroon Pidgin English

We explore valency and transitivity patterns in Cameroon Pidgin English (cpe) from a language contact perspective, with particular focus on (a) lexical and (b) constructional phenomena. With respect to (a), many verbs of English origin surface in cpe with additional senses and valency properties to those they display in the lexifier, illustrating the drive towards polysemy in a language with a relatively small lexicon. We also describe category change, whereby English non-verbal expressions (typically adjectives) emerge as verbs in cpe. In terms of (b), verbs undergo valency changes as a consequence of participation in productive serial verb constructions. These constructions are built around a small set of high-frequency verbs, some of which also occur in the light verb construction, which represents another strategy for the creation of complex predicates. We review the evidence for constructional substrate influence. The data under discussion are drawn from two small corpora of spoken cpe

Multiword expressions at length and in depth

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

A guide to the methodology

Corpora are widely used in linguistics, but not always wisely. This book attempts to frame corpus linguistics systematically as a variant of the observational method. The first part introduces the reader to the general methodological discussions surrounding corpus data as well as the practice of doing corpus linguistics, including issues such as the scientific research cycle, research design, extraction of corpus data and statistical evaluation. The second part consists of a number of case studies from the main areas of corpus linguistics (lexical associations, morphology, grammar, text and metaphor), surveying the range of issues studied in corpus linguistics while at the same time showing how they fit into the methodology outlined in the first part

Corpus linguistics

Corpora are widely used in linguistics, but not always wisely. This book attempts to frame corpus linguistics systematically as a variant of the observational method. The first part introduces the reader to the general methodological discussions surrounding corpus data as well as the practice of doing corpus linguistics, including issues such as the scientific research cycle, research design, extraction of corpus data and statistical evaluation. The second part consists of a number of case studies from the main areas of corpus linguistics (lexical associations, morphology, grammar, text and metaphor), surveying the range of issues studied in corpus linguistics while at the same time showing how they fit into the methodology outlined in the first part