Publications

We present an approach to detect stylistic variation across social variables (here: gender and social class), considering also diachronic change in language use. For detection of stylistic variation, we use relative entropy, measuring the difference between probability distributions at different linguistic levels (here: lexis and grammar). In addition, by relative entropy, we can determine which linguistic units are related to stylistic variation.

• naaclhlt2018-degaetano     (0.54MB)
• https://aclanthology.org/W18-1601
• https://doi.org/10.18653/v1/W18-1601

@inproceedings{Degaetano-Ortlieb2018,
title = {Stylistic Variation over 200 Years of Court. Proceedings According to Gender and Social Class},
author = {Stefania Degaetano-Ortlieb},
url = {https://aclanthology.org/W18-1601},
doi = {https://doi.org/10.18653/v1/W18-1601},
year = {2018},
date = {2018},
booktitle = {Proceedings of the 2nd Workshop on Stylistic Variation collocated with NAACL HLT 2018, June 1-6. ACL},
pages = {1-10},
publisher = {Association for Computational Linguistics},
address = {New Orleans},
abstract = {We present an approach to detect stylistic variation across social variables (here: gender and social class), considering also diachronic change in language use. For detection of stylistic variation, we use relative entropy, measuring the difference between probability distributions at different linguistic levels (here: lexis and grammar). In addition, by relative entropy, we can determine which linguistic units are related to stylistic variation.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

In the digital humanities, topic models are a widely applied text mining method (Meeks and Weingart, 2012). While their use for mining literary texts is not entirely straightforward (Schmidt, 2012), there is ample evidence for their use on factual text (e.g. Au Yeung and Jatowt, 2011; Thompson et al., 2016). We present an approach for exploring the research fields of selected authors in a corpus of late modern scientific English by topic modelling, looking at the topics assigned to an author’s texts over the author’s lifetime. Areas of applications we target are history of science, where we may be interested in the evolution of scientific disciplines over time (Thompson et al., 2016; Fankhauser et al., 2016), or diachronic linguistics, where we may be interested in the formation of languages for specific purposes (LSP) or specific scientific “styles” (cf. Bazerman, 1988; Degaetano-Ortlieb and Teich, 2016). We use the Royal Society Corpus (RSC, Kermes et al., 2016), which is based on the first two centuries (1665–1869) of the Philosophical Transactions and the Proceedings of the Royal Society of London. The corpus contains 9,779 texts (32 million tokens) and is available at https://fedora.clarin-d.uni-saarland.de/rsc/. As we are interested in the development of individual authors, we focus on the single-author texts (81%) of the corpus. In total, 2,752 names are annotated in the single-author papers, but the activity of authors varies. Figure 1 shows that a small group of authors wrote a large portion of the texts. In fact, the twelve authors used for our analysis wrote 11% of the single-author articles.

• https://dh2018.adho.org/en/using-topic-modelling-to-explore-authors-research-fields-in-a-corpus-of-historical-scientific-english/

@inproceedings{fischer-etal2018,
title = {Using Topic Modelling to Explore Authors’ Research Fields in a Corpus of Historical Scientific English},
author = {Stefan Fischer and J{\"o}rg Knappen and Elke Teich},
url = {https://dh2018.adho.org/en/using-topic-modelling-to-explore-authors-research-fields-in-a-corpus-of-historical-scientific-english/},
year = {2018},
date = {2018},
booktitle = {Proceedings of DH 2018},
address = {Mexico City, Mexico},
abstract = {In the digital humanities, topic models are a widely applied text mining method (Meeks and Weingart, 2012). While their use for mining literary texts is not entirely straightforward (Schmidt, 2012), there is ample evidence for their use on factual text (e.g. Au Yeung and Jatowt, 2011; Thompson et al., 2016). We present an approach for exploring the research fields of selected authors in a corpus of late modern scientific English by topic modelling, looking at the topics assigned to an author’s texts over the author’s lifetime. Areas of applications we target are history of science, where we may be interested in the evolution of scientific disciplines over time (Thompson et al., 2016; Fankhauser et al., 2016), or diachronic linguistics, where we may be interested in the formation of languages for specific purposes (LSP) or specific scientific “styles” (cf. Bazerman, 1988; Degaetano-Ortlieb and Teich, 2016). We use the Royal Society Corpus (RSC, Kermes et al., 2016), which is based on the first two centuries (1665–1869) of the Philosophical Transactions and the Proceedings of the Royal Society of London. The corpus contains 9,779 texts (32 million tokens) and is available at https://fedora.clarin-d.uni-saarland.de/rsc/. As we are interested in the development of individual authors, we focus on the single-author texts (81%) of the corpus. In total, 2,752 names are annotated in the single-author papers, but the activity of authors varies. Figure 1 shows that a small group of authors wrote a large portion of the texts. In fact, the twelve authors used for our analysis wrote 11% of the single-author articles.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

As technology and science permeate nearly all areas of life in modern times, there is a certain trend for standard dictionaries to bolster their technical and scientific vocabulary and to identify more components, for instance more combining forms, in technical terms and terminological phrases. In this paper it is argued that recently built diachronic corpora of scientific journal articles with robust linguistic and metadata-based features are important resources for complementing English corpus-based dictionaries and lexicographical resources for specialized languages. The Royal Society Corpus (RSC, ca. 9,800 digitized texts, 32 million tokens) in combination with the Scientific Text Corpus (SciTex, ca. 5,000 documents, 39 million tokens), as two recently created corpus resources, offer the possibility to provide a fuller picture of the development of specialized vocabulary and of the number of meanings that general and technical terms have accumulated during their history. They facilitate the systematic identification of lexemes with specific linguistic characteristics or from selected disciplines and fields, and allow us to gain a better understanding of the development of academic writing in English scientific periodicals across several centuries, from their beginnings to the present day.

• https://euralex.org/publications/using-diachronic-corpora-of-scientific-journal-articles-for-complementing-english-corpus-based-dictionaries-and-lexicographical-resources-for-specialized-languages/

@inproceedings{Menzel2017b,
title = {Using diachronic corpora of scientific journal articles for complementing English corpus-based dictionaries and lexicographical resources for specialized languages},
author = {Katrin Menzel},
url = {https://euralex.org/publications/using-diachronic-corpora-of-scientific-journal-articles-for-complementing-english-corpus-based-dictionaries-and-lexicographical-resources-for-specialized-languages/},
year = {2018},
date = {2018},
booktitle = {Proceedings of EURALEX2018},
isbn = {978-961-06-0097-8},
publisher = {Ljubljana University Press, Faculty of Arts},
address = {Ljubljana, Slovenia},
abstract = {As technology and science permeate nearly all areas of life in modern times, there is a certain trend for standard dictionaries to bolster their technical and scientific vocabulary and to identify more components, for instance more combining forms, in technical terms and terminological phrases. In this paper it is argued that recently built diachronic corpora of scientific journal articles with robust linguistic and metadata-based features are important resources for complementing English corpus-based dictionaries and lexicographical resources for specialized languages. The Royal Society Corpus (RSC, ca. 9,800 digitized texts, 32 million tokens) in combination with the Scientific Text Corpus (SciTex, ca. 5,000 documents, 39 million tokens), as two recently created corpus resources, offer the possibility to provide a fuller picture of the development of specialized vocabulary and of the number of meanings that general and technical terms have accumulated during their history. They facilitate the systematic identification of lexemes with specific linguistic characteristics or from selected disciplines and fields, and allow us to gain a better understanding of the development of academic writing in English scientific periodicals across several centuries, from their beginnings to the present day.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

We present a data-driven approach to study language use over time according to social variables (henceforth SV), considering also interactions between different variables. Besides sociolinguistic studies on language variation according to SVs (e.g., Weinreich et al. 1968, Bernstein 1971, Eckert 1989, Milroy and Milroy 1985), recently computational approaches have gained prominence (see e.g., Eisenstein 2015, Danescu-Niculescu-Mizil et al. 2013, and Nguyen et al. 2017 for an overview), not least due to an increase in data availability based on social media and an increasing awareness of the importance of linguistic variation according to SVs in the NLP community.

• clavier2017_slingpro_accepted     (0.02MB)
• https://stefaniadegaetano.files.wordpress.com/2017/07/clavier2017_slingpro_accepted.pdf

@inproceedings{Degaetano-Ortlieb2017b,
title = {Variation in language use across social variables: a data-driven approach},
author = {Stefania Degaetano-Ortlieb},
url = {https://stefaniadegaetano.files.wordpress.com/2017/07/clavier2017_slingpro_accepted.pdf},
year = {2017},
date = {2017},
booktitle = {Proceedings of the Corpus and Language Variation in English Research Conference (CLAVIER)},
address = {Bari, Italy},
abstract = {We present a data-driven approach to study language use over time according to social variables (henceforth SV), considering also interactions between different variables. Besides sociolinguistic studies on language variation according to SVs (e.g., Weinreich et al. 1968, Bernstein 1971, Eckert 1989, Milroy and Milroy 1985), recently computational approaches have gained prominence (see e.g., Eisenstein 2015, Danescu-Niculescu-Mizil et al. 2013, and Nguyen et al. 2017 for an overview), not least due to an increase in data availability based on social media and an increasing awareness of the importance of linguistic variation according to SVs in the NLP community.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

We present an empirical approach to analyze the course of usage change in scientific writing. A great amount of linguistic research has dealt with grammatical changes, showing their gradual course of change, which nearly always progresses stepwise (see e.g. Bybee et al. 1994, Hopper and Traugott 2003, Lee 2011, De Smet and Van de Velde 2013). Less well understood is under which conditions these changes occur. According to De Smet (2016), specific expressions increase in frequency in one grammatical context, adopting a more conventionalized use, which in turn makes them available in closely related grammatical contexts.

• clavier2017-degaetano-etal_accepted_final     (0.05MB)
• https://stefaniadegaetano.files.wordpress.com/2017/07/clavier2017-degaetano-etal_accepted_final.pdf

@inproceedings{Degaetano-Ortlieb2017b,
title = {The course of grammatical change in scientific writing: Interdependency between convention and productivity},
author = {Stefania Degaetano-Ortlieb and Katrin Menzel and Elke Teich},
url = {https://stefaniadegaetano.files.wordpress.com/2017/07/clavier2017-degaetano-etal_accepted_final.pdf},
year = {2017},
date = {2017},
booktitle = {Proceedings of the Corpus and Language Variation in English Research Conference (CLAVIER)},
address = {Bari, Italy},
abstract = {We present an empirical approach to analyze the course of usage change in scientific writing. A great amount of linguistic research has dealt with grammatical changes, showing their gradual course of change, which nearly always progresses stepwise (see e.g. Bybee et al. 1994, Hopper and Traugott 2003, Lee 2011, De Smet and Van de Velde 2013). Less well understood is under which conditions these changes occur. According to De Smet (2016), specific expressions increase in frequency in one grammatical context, adopting a more conventionalized use, which in turn makes them available in closely related grammatical contexts.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

• https://www.researchgate.net/publication/321776056_The_diachronic_development_of_combining_forms_in_scientific_writing

@article{Menzel2017,
title = {The diachronic development of combining forms in scientific writing},
author = {Katrin Menzel and Stefania Degaetano-Ortlieb},
url = {https://www.researchgate.net/publication/321776056_The_diachronic_development_of_combining_forms_in_scientific_writing},
year = {2017},
date = {2017},
journal = {Lege Artis. Language yesterday, today, tomorrow. The Journal of University of SS Cyril and Methodius in Trnava. Warsaw: De Gruyter Open},
pages = {185-249},
volume = {2},
number = {2},
abstract = {

Copy BibTeX to Clipboard

@inproceedings{Degaetano-Ortlieb2017b,
title = {An information-theoretic account on the diachronic development of discourse connectors in scientific writing},
author = {Stefania Degaetano-Ortlieb and Stefan Fischer and Vera Demberg and Elke Teich},
year = {2017},
date = {2017},
publisher = {39th DGfS AG1},
address = {Saarbr{\"u}cken, Germany},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

• W17-0503     (0.3MB)
• https://www.researchgate.net/publication/331648134_The_Making_of_the_Royal_Society_Corpus

@inproceedings{Knappen2017,
title = {The making of the Royal Society Corpus},
author = {J{\"o}rg Knappen and Stefan Fischer and Hannah Kermes and Elke Teich and Peter Fankhauser},
url = {https://www.researchgate.net/publication/331648134_The_Making_of_the_Royal_Society_Corpus},
year = {2017},
date = {2017},
booktitle = {21st Nordic Conference on Computational Linguistics (NoDaLiDa) Workshop on Processing Historical language},
pages = {7-11},
publisher = {Workshop on Processing Historical language},
address = {Gothenburg, Sweden},
abstract = {

Copy BibTeX to Clipboard

We present an approach to investigate the differences between lexical words and function words and the respective parts-of-speech from an information-theoretical point of view (cf. Shannon, 1949). We use average surprisal (AvS) to measure the amount of information transmitted by a linguistic unit. We expect to find function words to be more predictable (having a lower AvS) and lexical words to be less predictable (having a higher AvS). We also assume that function words‘ AvS is fairly constant over time and registers, while AvS of lexical words is more variable depending on time and register.

• https://www.birmingham.ac.uk/Documents/college-artslaw/corpus/conference-archives/2017/general/paper207.pdf

@inproceedings{Kermes2017,
title = {Average surprisal of parts-of-speech},
author = {Hannah Kermes and Elke Teich},
url = {https://www.birmingham.ac.uk/Documents/college-artslaw/corpus/conference-archives/2017/general/paper207.pdf},
year = {2017},
date = {2017},
publisher = {Corpus Linguistics 2017},
address = {Birmingham, UK},
abstract = {We present an approach to investigate the differences between lexical words and function words and the respective parts-of-speech from an information-theoretical point of view (cf. Shannon, 1949). We use average surprisal (AvS) to measure the amount of information transmitted by a linguistic unit. We expect to find function words to be more predictable (having a lower AvS) and lexical words to be less predictable (having a higher AvS). We also assume that function words' AvS is fairly constant over time and registers, while AvS of lexical words is more variable depending on time and register.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

We present a data-driven approach to investigate intra-textual variation by combining entropy and surprisal. With this approach we detect linguistic variation based on phrasal lexico-grammatical patterns across sections of research articles. Entropy is used to detect patterns typical of specific sections. Surprisal is used to differentiate between more and less informationally-loaded patterns as well as type of information (topical vs. stylistic). While we here focus on research articles in biology/genetics, the methodology is especially interesting for digital humanities scholars, as it can be applied to any text type or domain and combined with additional variables (e.g. time, author or social group).

• https://aclanthology.org/W17-2209
• https://doi.org/10.18653/v1/W17-2209

@inproceedings{Degaetano-Ortlieb2017,
title = {Modeling intra-textual variation with entropy and surprisal: Topical vs. stylistic patterns},
author = {Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://aclanthology.org/W17-2209},
doi = {https://doi.org/10.18653/v1/W17-2209},
year = {2017},
date = {2017},
booktitle = {Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature},
pages = {68-77},
publisher = {Association for Computational Linguistics},
address = {Vancouver, Canada},
abstract = {We present a data-driven approach to investigate intra-textual variation by combining entropy and surprisal. With this approach we detect linguistic variation based on phrasal lexico-grammatical patterns across sections of research articles. Entropy is used to detect patterns typical of specific sections. Surprisal is used to differentiate between more and less informationally-loaded patterns as well as type of information (topical vs. stylistic). While we here focus on research articles in biology/genetics, the methodology is especially interesting for digital humanities scholars, as it can be applied to any text type or domain and combined with additional variables (e.g. time, author or social group).},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665-1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.

• https://aclanthology.org/L16-1305

@inproceedings{Kermes2016,
title = {The Royal Society Corpus: From Uncharted Data to Corpus},
author = {Hannah Kermes and Stefania Degaetano-Ortlieb and J{\"o}rg Knappen and Ashraf Khamis and Elke Teich},
url = {https://aclanthology.org/L16-1305},
year = {2016},
date = {2016},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
pages = {1928-1931},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665-1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.

• https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787

@inproceedings{Fankhauser2016,
title = {Topical Diversification over Time in the Royal Society Corpus},
author = {Peter Fankhauser and J{\"o}rg Knappen and Elke Teich},
url = {https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

• https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing

@inproceedings{Kermes2016a,
title = {The Royal Society Corpus. Towards a high-quality resource for studying diachronic variation in scientific writing},
author = {Hannah Kermes and J{\"o}rg Knappen and Ashraf Khamis and Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {

Copy BibTeX to Clipboard

We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic
measures of entropy and surprisal.

• https://aclanthology.org/W16-2121
• https://doi.org/10.18653/v1/W16-2121

@inproceedings{Degaetano-Ortlieb2016a,
title = {Information-based modeling of diachronic linguistic change: from typicality to productivity},
author = {Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://aclanthology.org/W16-2121},
doi = {https://doi.org/10.18653/v1/W16-2121},
year = {2016},
date = {2016},
booktitle = {Proceedings of Language Technologies for the Socio-Economic Sciences and Humanities (LATECH'16), Association for Computational Linguistics (ACL)},
pages = {165-173},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic measures of entropy and surprisal.},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

Social and historical linguistic studies benefit from corpora encoding contextual metadata (e.g. time, register, genre) and relevant structural information (e.g. document structure). While small, handcrafted corpora control over selected contextual variables (e.g. the Brown/LOB corpora encoding variety, register, and time) and are readily usable for analysis, big data (e.g. Google or Microsoft n-grams) are typically poorly contextualized and considered of limited value for linguistic analysis (see, however, Lieberman et al. 2007). Similarly, when we compile new corpora, sources may not contain all relevant metadata and structural data (e.g. the Old Bailey sources vs. the richly annotated corpus in Huber 2007).

• https://www.ashrafkhamis.com/d2e2015.pdf

@inproceedings{Degaetano-etal2015,
title = {The taming of the data: Using text mining in building a corpus for diachronic analysis},
author = {Stefania Degaetano-Ortlieb and Hannah Kermes and Ashraf Khamis and Noam Ordan and Elke Teich},
url = {https://www.ashrafkhamis.com/d2e2015.pdf},
year = {2015},
date = {2015-10-01},
booktitle = {Varieng - From Data to Evidence (d2e)},
address = {University of Helsinki},
abstract = {Social and historical linguistic studies benefit from corpora encoding contextual metadata (e.g. time, register, genre) and relevant structural information (e.g. document structure). While small, handcrafted corpora control over selected contextual variables (e.g. the Brown/LOB corpora encoding variety, register, and time) and are readily usable for analysis, big data (e.g. Google or Microsoft n-grams) are typically poorly contextualized and considered of limited value for linguistic analysis (see, however, Lieberman et al. 2007). Similarly, when we compile new corpora, sources may not contain all relevant metadata and structural data (e.g. the Old Bailey sources vs. the richly annotated corpus in Huber 2007).},
pubstate = {published},
type = {inproceedings}
}

Copy BibTeX to Clipboard

• https://www.researchgate.net/publication/331648570_A_resource_for_the_diachronic_study_of_scientific_English_Introducing_the_Royal_Society_Corpus

@inproceedings{Khamis-etal2015,
title = {A resource for the diachronic study of scientific English: Introducing the Royal Society Corpus},
author = {Ashraf Khamis and Stefania Degaetano-Ortlieb and Hannah Kermes and J{\"o}rg Knappen and Noam Ordan and Elke Teich},
url = {https://www.researchgate.net/publication/331648570_A_resource_for_the_diachronic_study_of_scientific_English_Introducing_the_Royal_Society_Corpus},
year = {2015},
date = {2015-07-01},
booktitle = {Corpus Linguistics 2015},
address = {Lancaster},
abstract = {

Copy BibTeX to Clipboard

• https://www.researchgate.net/publication/331648534_Information_Density_in_Scientific_Writing_A_Diachronic_Perspective

@inproceedings{Degaetano-etal2015b,
title = {Information Density in Scientific Writing: A Diachronic Perspective},
author = {Stefania Degaetano-Ortlieb and Hannah Kermes and Ashraf Khamis and J{\"o}rg Knappen and Elke Teich},
url = {https://www.researchgate.net/publication/331648534_Information_Density_in_Scientific_Writing_A_Diachronic_Perspective},
year = {2015},
date = {2015-07-01},
booktitle = {"Challenging Boundaries" - 42nd International Systemic Functional Congress (ISFCW2015)},
address = {RWTH Aachen University},
abstract = {

Copy BibTeX to Clipboard

• http://link.springer.com/article/10.1007/s13218-015-0391-y/fulltext.html
• https://doi.org/10.1007/s13218-015-0391-y

@article{crocker:demberg:teich,
title = {Information Density and Linguistic Encoding (IDeaL)},
author = {Matthew W. Crocker and Vera Demberg and Elke Teich},
url = {http://link.springer.com/article/10.1007/s13218-015-0391-y/fulltext.html},
doi = {https://doi.org/10.1007/s13218-015-0391-y},
year = {2015},
date = {2015},
journal = {KI - K{\"u}nstliche Intelligenz},
pages = {77-81},
volume = {30},
number = {1},
abstract = {

Copy BibTeX to Clipboard