Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-05T02:40:45.710Z Has data issue: false hasContentIssue false

Part IV - Applications of Classification-Based Approaches

Published online by Cambridge University Press:  06 May 2022

Ole Schützler
Affiliation:
Universität Leipzig
Julia Schlüter
Affiliation:
Universität Bamberg
Get access
Type
Chapter
Information
Data and Methods in Corpus Linguistics
Comparative Approaches
, pp. 289 - 352
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Further Reading

Evert, Stefan. 2006. How Random Is a Corpus? The Library Metaphor. Zeitschrift für Anglistik und Amerikanistik 54(2). 177–90.Google Scholar
Grimmer, Justin, and Stewart, Brandon. 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21(3). 267–97.Google Scholar
Jurafsky, Dan, and Martin, James H.. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
Schneider, Gerold, and Lauber, Max. 2019. Introduction to Statistics for Linguists. Pressbooks. https://dlf.uzh.ch/openbooks/statisticsforlinguists/Google Scholar
Sinclair, John McHardy, and Carter, Ronald. 2004. Trust the Text: Language, Corpus and Discourse. London: Routledge.Google Scholar

References

Aarts, Bas. 1992. Comments. In Svartvik, Jan, ed. Directions in Corpus Linguistics: Proceedings of Nobel Symposium 82. Stockholm, 4–8 August 1991. Berlin: Mouton de Gruyter. 180–3.Google Scholar
Aarts, Bas. 2019. Syntactic Argumentation. In Aarts, Bas, Jill, Bowie and Popova, Gergana, eds. The Oxford Handbook of English Grammar. Oxford: Oxford University Press.Google Scholar
Abney, Steven. 1995. Chunks and Dependencies: Bringing Processing Evidence to Bear on Syntax. In Cole, Jennifer, Green, Georgia and Morgan, Jerry, eds. Computational Linguistics and the Foundations of Linguistic Theory. Chicago: University of Chicago Press. 145–64.Google Scholar
Ananiadou, Sophia, Kell, Douglas B. and Tsujii, Jun-Ichi. 2006. Text Mining and Its Potential Applications in Systems Biology. Trends in Biotechnology 24(12). 5719.Google Scholar
Anderson, Chris. 2008. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired Magazine 06.2008. www.wired.com/2008/06/pb-theory (accessed 20 February 2020).Google Scholar
Arppe, Antti, Gilquin, Gaëtanelle, Glynn, Dylan, Hilpert, Martin and Zeschel, Arne. 2010. Cognitive Corpus Linguistics: Five Points of Debate on Current Theory and Methodology. Corpora 5(1). 127.Google Scholar
Baron, Alistair, and Rayson, Paul. 2008. VARD 2: A Tool for Dealing with Spelling Variation in Historical Corpora. In Proceedings of the Postgraduate Conference in Corpus Linguistics. Birmingham: Aston University. http://ucrel.lancs.ac.uk/people/paul/publications/BaronRaysonAston2008.pdf.Google Scholar
Biber, Douglas. 2003. Compressed Noun-Phrase Structures in Newspaper Discourse: The Competing Demands of Popularization vs. Economy. In Aitchison, Jean and Lewis, Diana M., eds. New Media Language. London: Routledge. 169–81.Google Scholar
Biber, Douglas, Finegan, Edward and Atkinson, Dwight. 1994. ARCHER and Its Challenges: Compiling and Exploring a Representative Corpus of Historical English Registers. In Fries, Udo, Tottie, Gunnel and Schneider, Peter, eds. Creating and Using English Language Corpora: Papers from the 14th International Conference on English Language Research on Computerized Corpora, Zurich 1993. 113.Google Scholar
Biber, Douglas, and Conrad, Susan. 2009. Register, Genre, and Style. Cambridge: Cambridge University Press.Google Scholar
Brants, Thorsten. 2020. Inter-Annotator Agreement for a German Newspaper Corpus. In Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00). Luxembourg: European Language Resources Association (ELRA).Google Scholar
Denison, David. 1998. Syntax. In Romaine, Suzanne, ed. The Cambridge History of the English Language, vol. 4, 1776–1997. Cambridge: Cambridge University Press. 92329.Google Scholar
Dorman, Carsten F., Elith, Jane, Bacher, Sven et al. 2013. Collinearity: A Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance. Ecography 36. 2746. https://damariszurell.github.io/files/Dormann_etal_Ecography_2013.pdf.CrossRefGoogle Scholar
Evert, Stefan. 2006. How Random Is a Corpus? The Library Metaphor. Zeitschrift für Anglistik und Amerikanistik 54(2). 177–90.Google Scholar
Finn, Aidan, and Kushmerick, Nicolas. 2003. Learning to Classify Documents According to Genre. Proceedings of IJCAI-03 Workshop on Computational Approaches to Style Analysis and Synthesis. www.aidanf.net/publications/finn03learninggenre.pdf.Google Scholar
Gries, Stefan T. 2010. Corpus Linguistics and Theoretical Linguistics: A Love-Hate Relationship? Not Necessarily … International Journal of Corpus Linguistics 15(3). 327–43.Google Scholar
Gries, Stefan T. 2015. The Most Under-Used Statistical Method in Corpus Linguistics: Multi-Level (and Mixed-Effects) Models. Corpora 10(1). 95125.Google Scholar
Gries, Stefan Th., and Hilpert, Martin. 2008. The Identification of Stages in Diachronic Data: Variability-Based Neighbor Clustering. Corpora 3 (1). 5981.Google Scholar
Grimmer, Justin, and Stewart, Brandon. 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21(3). 267–97.Google Scholar
Grover, Claire, and Tobin, Richard. 2006. Rule-Based Chunking and Reusability. In Calzolari, Nicoletta, Choukri, Khalid, Gangemi, Aldo, Maegaard, Bente, Mariani, Joseph, Odijk, Jan and Tapias, Daniel, eds. Proceedings of LREC 2006, Genoa, Italy: European Language Resources Association (ELRA). 873–8.Google Scholar
Gulordava, Kristina, and Merlo, Paola. 2015. Diachronic Trends in Word Order Freedom and Dependency Length in Dependency-Annotated Corpora of Latin and Ancient Greek. International Conference on Dependency Linguistics, Uppsala. www.aclweb.org/anthology/W15-2115.Google Scholar
Harrell, Frank E. 2015. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Cham: Springer.CrossRefGoogle Scholar
Hawkins, John A. 2004. Efficiency and Complexity in Grammars. Oxford: Oxford University Press.CrossRefGoogle Scholar
Hilpert, Martin, and Gries, Stefan. 2016. Quantitative Approaches to Diachronic Corpus Linguistics. In Kytö, Merja and Pahta, Paivi, eds. The Cambridge Handbook of English Historical Linguistics. Cambridge: Cambridge University Press. 3653.Google Scholar
Hopper, Paul J., and Traugott, Elizabeth Closs. 2003. Grammaticalization. 2nd ed. Cambridge Textbooks in Linguistics. Cambridge: Cambridge University Press.Google Scholar
Hundt, Marianne, Denison, David and Schneider, Gerold. 2012. Relative Complexity in Scientific Discourse. English Language and Linguistics 16(2). 209–40.CrossRefGoogle Scholar
Jurafsky, Dan, and Martin, James H.. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
Klein, Dan, and Manning, Christopher. 2004. Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04). 478–85. www.aclweb.org/anthology/P04–1000.Google Scholar
Kroch, Anthony, and Taylor, Ann. 2000. Verb-Object Order in Early Middle English. In Pintzuk, Susan, Tsoulas, George and Warner, Anthony, eds. Diachronic Syntax: Models and Mechanisms. Oxford: Oxford University Press. 132–87.Google Scholar
Lapata, Mirella, and Keller, Frank. 2005. Web-Based Models for Natural Language Processing. ACM Transactions on Speech and Language Processing. 2(1). 131.Google Scholar
Larsson, Tove, Plonsky, Luke and Hancock, Gregory R.. 2020. On the Benefits of Structural Equation Modeling for Corpus Linguists. Corpus Linguistics and Linguistic Theory, published ahead of print. https://doi.org/10.1515/cllt-2020-0051.Google Scholar
Leech, Geoffrey, Hundt, Marianne, Mair, Christian and Smith, Nicholas. 2009. Change in Contemporary English: A Grammatical Study. Cambridge: Cambridge University Press.Google Scholar
López-Couso, Maria José, Aarts, Bas and Méndez-Naya, Belén. 2012. Late Modern English Syntax. In Bergs, Alexander and Brinton, Laurel J., eds. Historical Linguistics of English: An International Handbook. Volume I. Handbooks of Linguistics and Communication Science [HSK] 34.1. Berlin: Mouton de Gruyter. 869–87.Google Scholar
Los, Bettelou. 2005. The Rise of the to-Infinitive. Oxford: Oxford University Press.Google Scholar
Paul, Ranjit. 2017. Multicollinearity: Causes, Effects and Remedies. New Delhi: Indian Agricultural Research Institute. www.researchgate.net/publication/255640558_MULTICOLLINEARITY_CAUSES_EFFECTS_AND_REMEDIES.Google Scholar
Pfenninger, Simone. 2009. Grammaticalization Paths of English and High German Existential Constructions. Bern: Peter Lang.Google Scholar
Rayson, Paul, Archer, Dawn, Baron, Alistair, Culpeper, Jonathan and Smith, Nicholas. 2007. Tagging the Bard: Evaluating the Accuracy of a Modern POS Tagger on Early Modern English Corpora. In Davies, Matthew, Rayson, Paul, Hunston, Susan and Danielsson, Pernilla, eds. Proceedings of Corpus Linguistics 2007. University of Birmingham.Google Scholar
Röthlisberger, Melanie, and Schneider, Gerold. 2013. Of-Genitive versus s-Genitive: A Corpus-Based Analysis of Possessive Constructions in 20th Century English. In Bennett, Paul, Durrell, Martin, Silke Scheible, Richard J. Whitt, Holger Keibel, Kupietz, Marc and Mair, Christian, eds. New Methods in Historical Corpora. Tübingen: Narr. 163–80.Google Scholar
Marina, Santini. 2004. A Shallow Approach to Syntactic Feature Extraction for Genre Classification. Proceedings of the 7th Annual Colloquium for the UK Special Interest Group for Computational Linguistics (CLUK 2004). Birmingham, UK. https://pdfs.semanticscholar.org/4de8/23dac38edce09f60cfb3eb93524204f57e7e.pdf.Google Scholar
Saville-Troike, Muriel, and Barto, Karen. 2017. Introducing Second Language Acquisition. 3rd ed. Cambridge: Cambridge University Press.Google Scholar
Schmid, Helmut. 1994. Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proceedings of International Conference on New Methods in Language Processing. www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/data/tree-tagger1.pdf.Google Scholar
Schneider, Gerold. 2008. Hybrid Long-Distance Functional Dependency Parsing. Doctoral thesis. Institute of Computational Linguistics, University of Zurich.Google Scholar
Schneider, Gerold. 2018. Differences between Swiss High German and German High German via Data-Driven Methods. In 3rd Swiss Text Analytics Conference (SwissText 2018). Winterthur, 12 June 2018–13 June 2018. CEUR-WS, 1725. www.zora.uzh.ch/id/eprint/162838/.Google Scholar
Schneider, Gerold. 2020. Spelling Normalisation of Late Modern English: Comparison and Combination of VARD and Character-Based Statistical Machine Translation. In Kytö, Merja and Smitterberg, Erik, eds. Late Modern English: Novel Encounters. Studies in Language Series. Amsterdam: John Benjamins. 243–68.Google Scholar
Schneider, Gerold, Lehmann, Hans Martin and Schneider, Peter. 2014. Parsing Early and Late Modern English Corpora. Literary and Linguistic Computing 30(3). 423–39. https://doi.org/10.1093/llc/fqu001.Google Scholar
Schneider, Gerold, Hundt, Marianne and Oppliger, Rahel. 2016. Part-of-Speech in Historical Corpora: Tagger Evaluation and Ensemble Systems on ARCHER. In Dipper, Stefanie, Neubarth, Friedrich and Zinsmeister, Heike, eds. Proceedings of the 13th Conference on Natural Language Processing, KONVENS 2016, September 19–21, 2016. Bochumer Linguistische Arbeitsberichte 16. Bochum: Germany.Google Scholar
Schneider, Gerold, Pettersson, Eva and Percillier, Michael. 2017. Comparing Rule-Based and SMT-Based Spelling Normalisation for English Historical Texts. In Bouma, Gerlof and Adesam, Yvonne, eds. Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language. Linköping: Linköping University Electronic Press. 40–6.Google Scholar
Schreiber-Gregory, Deanna. 2018. Regulation Techniques for Multicollinearity: Lasso, ridge, and Elastic Nets. Proceedings of Western Users of SAS Software Conferences 2018, September 5–7, 2018. Sacramento, CA. www.lexjansen.com/wuss/2018/131_Final_Paper_PDF.pdf.Google Scholar
Shannon, Claude E. 1951. Prediction and Entropy of Printed English. The Bell System Technical Journal 30. 5064.Google Scholar
Sinclair, John McHardy, and Carter, Ronald. 2004. Trust the Text: Language, Corpus and Discourse. London: Routledge.Google Scholar
Szmrecsanyi, Benedikt, Rosenbach, Anette, Bresnan, Joan and Wolk, Christoph. 2014. Culturally Conditioned Language Change? A Multi-Variate Analysis of Genitive Constructions in ARCHER. In Hundt, Marianne, ed. Late Modern English Syntax. Cambridge: Cambridge University Press. 133–52.Google Scholar
Tesnière, Lucien. 1959. Eléments de syntaxe structurale. Paris: Librairie Klincksieck.Google Scholar
Tognini-Bonelli, Elena. 2001. Corpus Linguistics at Work. Amsterdam: John Benjamins.Google Scholar
van Noord, Gertjan and Bouma, Gosse. 2009. Parsed Corpora for Linguistics. In Baldwin, Timothy and Kordoni, Valia, eds. Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous? Association for Computational Linguistics. 33–9. www.aclweb.org/anthology/W09–0107.Google Scholar
Xiao, Richard. 2009. Theory-Driven Corpus Research: Using Corpora to Inform Aspect Theory. In Lüdeling, Anke and Kytö, Merja, eds., Corpus Linguistics: An International Handbook, vol. 2. Berlin: Mouton de Gruyter. 9871008.Google Scholar
Yang, Li-Gong, Jian, Zhu and Shi-Ping., Tang 2013. Keywords Extraction Based on Classification. Advanced Materials Research 765. 1604–9.Google Scholar

Further Reading

Aijmer, Karin. 2020. Contrastive Pragmatics and Corpora. Contrastive Pragmatics 1(1). 2857.Google Scholar
Gast, Volker. 2015. On the Use of Translation Corpora in Contrastive Linguistics: A Case Study of Impersonalization in English and German. Languages in Contrast 15(1). 433.Google Scholar
Grosz, Patrick Georg. 2020. Discourse Particles. In Gutzmann, Daniel, Matthewson, Lisa, Meier, Cécile, Rullmann, Hotze and Zimmermann, Thomas, eds. The Wiley Blackwell Companion to Semantics. Part F. Meaning, Use, and Cognition. Hoboken, NJ: John Wiley & Sons. 134.Google Scholar
Jurafsky, Dan, and Martin, James H.. 2019. Speech and Language Processing. 3rd draft. https://web.stanford.edu/~jurafsky/slp3/ (accessed 23 October 2020).Google Scholar

References

Aijmer, Karin. 2009. Does English Have Modal Particles? In Renouf, Antoinette and Kehoe, Andrew, eds. Corpus Linguistics Reassessed. Papers from the 27th International Conference on English Language. Amsterdam and New York: Rodopi. 111–30.Google Scholar
Aijmer, Karin. 2020. Contrastive Pragmatics and Corpora. Contrastive Pragmatics 1(1). 2857.CrossRefGoogle Scholar
Borst, Dieter. 1985. Die affirmativen Modalpartikeln doch, ja, und schon: Ihre Bedeutung, Funktion, Stellung und ihr Vorkommen. Tübingen: Niemeyer.Google Scholar
Bublitz, Wolfram. 1979. Ausdrucksweisen der Sprechereinstellung im Deutschen und im Englischen: Untersuchungen zur Syntax, Semantik und Pragmatik der deutschen Modalpartikeln und Vergewisserungsfragen. Tübingen: Niemeyer.Google Scholar
Cartoni, Bruno, and Meyer, Thomas. 2012. Extracting Directional and Comparable Corpora from a Multilingual Corpus for Translation Studies. In Calzolari, Nicoletta, Choukri, Khalid, Declerck, Thierry et al., eds. Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12). Istanbul: European Language Resources Association (ELRA).Google Scholar
Cortez, Paulo. 2020. rminer: Data Mining Classification and Regression Methods. R package version 1.4.5.Google Scholar
Cortez, Paulo, and Embrechts, Mark J.. 2013. Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models. Information Sciences 225. 117.Google Scholar
Cumming, Geoff. 2014. The New Statistics: Why and How. Psychological Science 25(1). 729.Google Scholar
Devlin, Jacob, Chang, Ming-Wei, Lee, Kenton and Toutanova, Kristina. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Burstein, Jill, Doran, Christy and Solorio, Thamar, eds. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, Long and Short Papers. Minneapolis, MN: Association for Computational Linguistics. 4171–86.Google Scholar
Dietterich, Thomas G. 1998. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation 10(7). 1895–923.Google Scholar
Doherty, Monika. 1985. Epistemische Bedeutung. Berlin: Akademie Verlag.Google Scholar
Fillmore, Charles. 1984. Remarks on Contrastive Pragmatics. In Fisiak, Jacek, ed. Contrastive Linguistics: Prospects and Problems. Berlin: Mouton de Gruyter. 119–41.Google Scholar
Fischer, Kerstin. 2007. Grounding and Common Ground: Modal Particles and Their Translation Equivalents. In Fetzer, Anita and Fischer, Kerstin, eds. Lexical Markers of Common Grounds. Amsterdam: Elsevier. 4766.Google Scholar
Fischer, Kerstin, and Heide, Maiken. 2018. Inferential Processes in English and the Question Whether English Has Modal Particles. Open Linguistics 4(1). 509–36.Google Scholar
Gao, Qin, and Vogel, Stephan. 2008. Parallel Implementations of Word Alignment Tool. In Bretonnel Cohen, K. and Carpenter, Bob, eds. Software Engineering, Testing, and Quality Assurance for Natural Language Processing. Columbus, OH: Association for Computational Linguistics. 4957.Google Scholar
Gast, Volker. 2008. Modal Particles and Context Updating: The Functions of German ja, doch, wohl and etwa. In Vater, Heinz, Letnes, Ole and Maagerø, Eva, eds. Modalverben und Grammatikalisierung. Trier: Wissenschaftlicher Verlag. 153–77.Google Scholar
Gast, Volker. 2015. On the Use of Translation Corpora in Contrastive Linguistics. A Case Study of Impersonalization in English and German. Languages in Contrast 15(1). 433.Google Scholar
Gast, Volker, Bierkandt, Lennart and Rzymski, Christoph. 2015a. Annotating Modals with GraphAnno, a Configurable Lightweight Tool for Multi-Level Annotation. In Nissim, Malvina and Pietrandrea, Paola, eds. Proceedings of the Workshop on Models for Modality Annotation, held in conjunction with IWCS 11, 2015. Stroudsburg, PA. 1928.Google Scholar
Gast, Volker, Bierkandt, Lennart and Rzymski, Christoph. 2015b. Creating and Retrieving Tense and Aspect Annotations with GraphAnno, a Lightweight Tool For Multi-level Annotation. In Bunt, H., ed. Proceedings of the 11th Joint ACL-ISO Workshop on Interoperable Annotation. Tilburg: Tilburg Center for Cognition and Communication. 23–8.Google Scholar
Grosz, Patrick Georg. 2020. Discourse Particles. In Gutzmann, Daniel, Matthewson, Lisa, Meier, Cécile, Rullmann, Hotze and Zimmermann, Thomas, eds. The Wiley Blackwell Companion to Semantics. Part F. Meaning, Use, and Cognition. Hoboken, NJ: John Wiley & Sons. 134.Google Scholar
Hentschel, Elke. 1986. Funktion und Geschichte deutscher Partikeln: ja, doch, halt und eben. Tübingen: Niemeyer.Google Scholar
Jurafsky, Dan, and Martin, James H.. 2019. Speech and Language Processing. 3rd draft. https://web.stanford.edu/~jurafsky/slp3/ (accessed 23 October 2020).Google Scholar
Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt and Zeileis, Achim. 2004. Kernlab – An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9). 120.Google Scholar
Koehn, Philipp. 2005. Europarl: A Parallel Corpus for Statistical Machine Translation. In Tsujii, Junichi, ed. Proceedings of MT Summit X. Phuket, Thailand. 7986.Google Scholar
König, Ekkehard, and Gast, Volker. 2018. Understanding English-German Contrasts. 4th ed. Berlin: Erich Schmidt-Verlag.Google Scholar
König, Ekkehard, Stark, Detlef and Requardt, Susanne. 1990. Adverbien und Partikeln: Ein deutsch-englisches Wörterbuch. Heidelberg: Julius Groos.Google Scholar
Marneffe, Marie-Catherine de, Dozat, Timothy, Silvaire, Natalia et al. 2014. Universal Stanford Dependencies: A Cross-Linguistic Typology. In Calzolari, Nicoletta, Choukri, Khalid, Declerck, Thierry et al., eds. Proceedings of the International Conference on Language Resources and Evaluation (LREC). Reykjavik. 4585–92.Google Scholar
Och, Franz Josef, and Ney, Hermann. 2003. A Systematic Comparison of Various Statistical Alignment Models. Computational Linguistics 29(1). 1951.Google Scholar
Peng, Qi, Zhang, Yuhao, Zhang, Yuhui, Bolton, Jason and Manning, Christopher D.. 2020. Stanza: A Python Natural Language Processing toolkit for many human languages. arXiv preprint arXiv:2003.07082.Google Scholar
Santorini, Beatrice. 1991. Part-of-Speech Tagging Guidelines for the Penn Treebank Project. www.personal.psu.edu/xxl13/teaching/sp07/apling597e/resources/Tagset.pdf.Google Scholar
Schneider, Gerold, and Graën, Johannes. 2018. NLP Corpus Observatory: Looking for Constellations in Parallel Corpora to Improve Learners’ Collocational Skills. In Pilán, Ildikó, Volodina, Elena, Alfter, David and Borin, Lars, eds. Proceedings of the 7th workshop on NLP for Computer Assisted Language Learning. Stockholm: LiU Electronic Press. 6978.Google Scholar
Thurmair, Maria. 1989. Modalpartikeln und ihre Kombinationen, vol. 223, Linguistische Arbeiten. Berlin/New York: de Gruyter.Google Scholar
Wang, Sida, and Manning, Christopher. 2012. Baselines and Bigrams: Simple, Good Sentiment and Topic Classification. In Haizhou, Li, Lin, Chin-Yew, Osborne, Miles, Lee, Gary Geunbae and Park, Jong C., eds. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Volume 2. Short Papers). Jeju Island, Korea: Association for Computational Linguistics. 90–4.Google Scholar
Weydt, Harald. 1969. Abtönungspartikel: Die deutschen Modalwörter und ihre französischen Entsprechungen. Bad Homburg: Gehlen.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×