Abstract
The analysis of longitudinal corpora of historical texts requires the integrated development of tools for automatically preprocessing these texts and for building representation models of their genre- and register-related dynamics. In this chapter we present such a joint endeavor that ranges from resource formation via preprocessing to network-based text representation and classification. We start with presenting the so-called TTLab Latin Tagger (TLT) that preprocesses texts of classical and medieval Latin. Its lexical resource in the form of the Frankfurt Latin Lexicon (FLL) is also briefly introduced. As a first test case for showing the expressiveness of these resources, we perform a tripartite classification task of authorship attribution, genre detection and a combination thereof. To this end, we introduce a novel text representation model that explores the core structure (the so-called coreness) of lexical network representations of texts. Our experiment shows the expressiveness of this representation format and mediately of our Latin preprocessor.
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- 1.
According to Anne Bohnenkamp-Renken, Goethe-House Frankfurt, personal communication.
- 2.
TTLab is an acronym that denotes the Frankfurt Text-technology lab (www.hucompute.org).
- 3.
The FLL results from a cooperation of historians and computer scientists—see the project website for more information: www.comphistsem.org.
- 4.
- 5.
- 6.
- 7.
- 8.
Provided by Michael Trauth, Trier University. See also [36].
- 9.
collex.hucompute.org. is our interface for this human computation of a Latin resource.
- 10.
www.dmgh.de/de/fs1/object/display/bsb00000820_meta:titlePage.html?sortIndex=020:030:0001:010:00:00.
- 11.
By the members of Bernhard Jussen’s lab at Goethe-University Frankfurt: Silke Schwandt, Tim Geelhaar, and colleagues.
- 12.
- 13.
Models of text representation as small as the one introduced here are common in quantitative text linguistics—an early example is [40]. The difference is that while these models mostly consider well-established indices like TTR or the rate of hapax legomena, we are concerned with inventing a complete new set of quantitative text characteristics based on the same notion of text organization.
- 14.
A test of this hypothesis will be the object of a forthcoming paper.
- 15.
Obviously, for any v: u(v) ≥ σ(v).
- 16.
- 17.
The underlying texts of this corpus have been selected by Silke Schwandt from the Patrologia Latina [17]. They are accessible via the eHumanities Desktop (hudesktop.hucompute.org).
- 18.
Note that this goal also requires a semantic disambiguation and sense tagging [61]—beyond PoS tagging— which is not yet provided by the TLT.
References
Heyer G (2014) Digital and computational humanities. www.dagstuhl.de/mat/Files/14/14301/14301.HeyerGerhard.ExtAbstract.pdf
Hearst MA (1999) Untangling text data mining. In: Proceedings of ACL’99: the 37th annual meeting of the association for computational linguistics, University of Maryland
Mehler A (2004) Textmining. In: Lobin H, Lemnitzer L, (eds) Texttechnologie. Perspektiven und Anwendungen, Stauffenburg, Tübingen, pp 329–352
de Saussure F (1916) Cours de linguistique générale. Payot, Lausanne/Paris
Peirce CS (1993) Semiotische Schriften 1906–1913, vol 3. Suhrkamp, Frankfurt am
Crane G, Wulfman C (2003) Towards a cultural heritage digital library. In: Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries (JCDL ’03), Washington. IEEE Computer Society, pp 75–86
Bamman D, Passarotti M, Busa R, Crane G (2008) The annotation guidelines of the latin dependency treebank and index thomisticus treebank. In: Proceedings of LREC 2008, Marrakech, Morocco, ELRA
Bamman D, Crane, G (2009) Structured knowledge for low-resource languages: The Latin and Ancient Greek dependency treebanks. In: Proceeding of the text mining services 2009, Leipzig. Springer, New York
Passarotti M (2010) Leaving behind the less-resourced status. The case of Latin through the experience of the Index Thomisticus Treebank. In: Proceedings of the 7th SaLTMiL workshop on the creation and use of basic lexical resources for less-resourced languages (LREC 2010), La Valletta, Malta, ELDA
Gleim R, Hoenen A, Diewald N, Mehler A, Ernst A (2011) Modeling, building and maintaining lexica for corpus linguistic studies by example of Late Latin. In: Corpus Linguistics 2011, Birmingham, 20–22 July 2011
Büchler M, Heyer G, Gründer S (2008) eAQUA–bringing modern text mining approaches to two thousand years old ancient texts. In: Proceedings of e-Humanities–An emerging discipline, workshop at the 4th IEEE international conference on e-Science
Jussen B, Mehler A, Ernst A (2007) A corpus management system for historical semantics. Sprache und Datenverarbeitung. Int J Lang Data Proc 31(1–2):81–89
Büchler M, Geßner A, Heyer G, Eckart T (2010) Detection of citations and text reuse on ancient Greek texts and its applications in the classical studies: eAQUA project. In: Proceedings of digital humanities 2010, London
Mehler A, Schwandt S, Gleim R, Ernst A (2012) Inducing linguistic networks from historical corpora: Towards a new method in historical semantics. In: Durrell M et al (eds) Proceedings of the Conference on new methods in historical corpora, April 29–30, 2011, Manchester. Corpus linguistics and Interdisciplinary perspectives on language (CLIP). Narr, Tübingen, pp 257–274
Crane, G (1996) Building a digital library: the perseus project as a case study in the humanities. In: Proceedings of the first ACM international conference on Digital libraries (DL ’96), New York. ACM, USA, pp 3–10+++
Smith DA, Rydberg-Co JA, Crane GR (2000) The Perseus Project: A digital library for the humanities. Lit Linguistic Comput 15(1):15–25
Jordan MD (ed) (1995) Patrologia latina database. Chadwyck-Healey, Cambridge
Amancio DR, Antiqueira L, Pardo TAS, Costa LdF, Oliveira ON, Nunes MDGV (2008) Complex networks analysis of manual and machine translations. Int J Mod Phys C 19(4):583–598
Amancio DR, Jr, ONO, da Fontoura Costa L (2012) Identification of literary movements using complex networks to represent texts. New J Phys 14:043029
Liu J, Wang J, Wang C (2008) A text network representation model. In: FSKD ’08: Proceedings of the 2008 fifth international conference on fuzzy systems and knowledge discovery, Washington. IEEE computer society, pp 150–154
Mehler A (2008) Large text networks as an object of corpus linguistic studies. In: Lüdeling A, Kytö M (eds) Corpus Linguistics. An international handbook of the science of language and society. De Gruyter, Berlin, pp 328–382
Koster CHA (2005) Constructing a parser for Latin. In: Gelbukh AF (ed) Proceedings of the 6th international conference on computational linguistics and intelligent text processing (CICLing 2005). LNCS, vol 3406. Springer, New York, pp 48–59
Passarotti M, Dell’Orletta F (2010) Improvements in parsing the index thomisticus treebank. Revision, combination and a feature model for medieval Latin. In: Proceedings of LREC 2010, Malta, ELDA
Voutilainen A (1995) A syntax-based part-of-speech analyzser. In: Proceedings of the 7th conference of the European chapter of the association for computational linguistics (EACL), Belfield, Ireland pp 157–164
Jurafsky D, Martin JH (2000) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Prentice Hall, Upper Saddle River
Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT Press, Cambridge
Ratnaparkhi A (1996) A maximum entropy model for part-of-speech tagging. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP). Philadelphia, Pennsylvania
Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6:1453–1484
Nguyen N, Guo Y (2007) Comparisons of sequence labeling algorithms and extensions. In: Proceedings of the 24th International conference on machine learning (ICML). ACM, New York
Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th international conference on machine learning. St. Petersburg/Russia
Constant M, Sigogne A (2011) MWU-aware part-of-speech tagging with a CRF model and lexical resources. In: MWE ’11 Proceedings of the workshop on multiword expressions: from parsing and generation to the real world. Stroudsburg, pp 49–56
Simionescu R (2011) Hybrid pos tagger. In: Proceedings of the workshop on language resources and tools with industrial applications, Cluj-Napoca
Mehler A, Gleim R, Waltinger U, Diewald N (2010) Time series of linguistic networks by example of the Patrologia Latina. In: Fähnrich KP, Franczyk B, (eds) Proceedings of INFORMATIK 2010: service science, September 27—October 01, 2010, Leipzig. Volume 2 of Lecture Notes in Informatics, GI, pp 609–616+++
Passarotti M (2000) Development and perspectives of the Latin morphological analyser LEMLAT (1). Linguistica Computazionale 3:397–414
Schmid H (1994) Probabilistic part-of-speech tagging using decision trees. In: Jones D, Somers H (eds) New methods in language processing studies in computational linguistics. UCL Press, London
Springmann U, Najock D, Morgenroth H, Schmid H, Gotscharek A, Fink, F (2014) OCR of historical printings of Latin texts: problems, prospects, progress. In: Antonacopoulos A, Schulz KU (eds) Digital access to textual cultural heritage 2014 (DATeCH 2014), Madrid. ACM, May 19–20, pp 71–75
Okazaki N (2007) CRFsuite: a fast implementation of conditional random fields (CRFs). http://www.chokkan.org/software/crfsuite/manual.html
Zipf GK (1972) Human behavior and the principle of least effort. An introduction to human ecology. Hafner Publishing, New York
Panhuis DG (2009) Latin grammar. University of Michigan Press, Ann Arbor
Liiv H, Tuldava J (1993) On classifying texts with the help of cluster analysis. In: Hřebíček L, Altmann G (eds) Quantitative text analysis. Wissenschaftlicher Verlag, Trier, pp 253–262
Schuhmacher M, Ponzetto SP (2014) Knowledge-based graph document modeling. In: Proceedings of the 7th ACM international conference on web search and data mining (WSDM ’14), New York. ACM, pp 543–552
Seidman SB (1983) Network structure and minimum degree. Soc Networks 5:269–287
Batagelj V, Zavervsnik M (2003) An O(m) algorithm for cores decomposition of networks. http://vlado.fmf.uni-lj.si/vlado/vladounp.html. arXiv:cs/0310049
Ashraf M, Sinha S (2012) Core-periphery organization of graphemes in written sequences: decreasing positional rigidity with increasing core order. In: Gelbukh A (ed) Computational linguistics and intelligent text processing. Lecture notes in computer science, vol 7181. Springer, New York, pp 142–153
Fortunato S (1983) Community detection in graphs. Phys Rep 486(3–5):75–174
Giatsidis C, Thilikos DM, Vazirgiannis M (2011) Evaluating cooperation in communities with the k-core structure. In: Proceedings of the 2011 international conference on advances in social networks analysis and mining (ASONAM ’11), Washington. IEEE Computer Society, pp 87–93
Alvarez-Hamelin JI, Dall’Asta L, Barrat A, Vespignani A (2008) k-core decomposition of internet graphs: hierarchies, self-similarity and measurement biases. Net Heterogeneous Media 3(2):371–393
Halliday MAK, Hasan R (1989) Language, context, and text: aspects of language in a socialsemiotic perspective. Oxford University Press, Oxford
Dehmer M (2008) Information processing in complex networks: Graph entropy and information functionals. Appl Math Comput 201:82–94
Dehmer M, Mowshowitz A (2011) A history of graph entropy measures. Inform Sci 181(1):57–78
Mehler A (2011) A quantitative graph model of social ontologies by example of Wikipedia. In: Dehmer M, Emmert-Streib F, Mehler A (eds) Towards an information theory of complex networks: statistical methods and applications. Birkhäuser, Boston, pp 259–319
Cover TM, Thomas JA (2006) Elements of information theory. Wiley-Interscience, Hoboken
Botafogo RA, Rivlin E, Shneiderman B (1992) Structural analysis of hypertexts: identifying hierarchies and useful metrics. ACM Trans Infor Syst 10(2):142–180
Mehler A (2008) Structural similarities of complex networks: A computational model by example of wiki graphs. Appl Artif Intell 22(7,8):619–683
Mehler A, Pustylnikov O, Diewald N (2011) Geography of social ontologies: testing a variant of the Sapir-Whorf Hypothesis in the context of Wikipedia. Comput Speech Lang 25(3):716–740
Pieper U (1975) Differenzierung von Texten nach numerischen Kriterien. Folia Linguistica VII:61–113
Frank-Job B (1994) Die textgestalt als zeichen. Lateinische handschriftentradition und die verschriftlichung der romanischen sprachen, ScriptOralia, vol 67. Narr, Tübingen
Frank-Job B (2003) Diskurstraditionen im Verschriftlichungsprozeß der romanischen Sprachen. In: Aschenberg H, Wilhelm R (eds) Romanische sprachgeschichte und diskurstraditionen. Narr, Tübingen, pp 19–35
Köhler R, Galle M (1993) Dynamic aspects of text characteristics. In: Hřebíček L, Altmann G (eds) Quantitative text analysis. Wissenschaftlicher Verlag, Trier, pp 46–53
McCarthy PM, Jarvis S (2010) Mtld, vocd-d, and hd-d: A validation study of sophisticated approaches to lexical diversity assessment. Behav Res Methods 42(2):381–392
Schütze H (1998) Automatic word sense discrimination. Computat Linguistics 24(1):97–123
Stamatatos E (2011) Plagiarism detection based on structural information. In: Proceedings of the 20th ACM international conference on information and knowledge management (CIKM ’11), New York. ACM, pp 1221–1230
Evert S (2008) Corpora and collocations. In: Lüdeling A, Kytö M (eds) Corpus linguistics. An international handbook of the science of language and society. Mouton de Gruyter, Berlin, pp 1212–1248
Miller GA (1956) The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol Rev 63:81–97
van Dijk TA, Kintsch W (1983) Strategies of Discourse Comprehension. Academic Press, New York
Rieger B (1998) Warum fuzzy Linguistik? Überlegungen und Ansätze zu einer computerlinguistischen Neuorientierung. In: Krallmann D, Schmitz HW (eds) Perspektiven einer Kommunikationswissenschaft. Internationales gerold ungeheuer symposium, Essen 1995. Nodus, Münster pp 153–183
Acknowledgements
Financial support by the BMBF-project Computational Historical Semantics (www.comphistsem.org) as part of the research center on Digital Humanities is gratefully acknowledged. We also thank both anonymous reviewers for their valuable hints, Barbara Job and Silke Schwandt for their fruitful hints on distinguishing monastic and scholastic texts and, finally, Andy Lücking for assisting in producing TikZ graphics.
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Mehler, A., vor der Brück, T., Gleim, R., Geelhaar, T. (2014). Towards a Network Model of the Coreness of Texts: An Experiment in Classifying Latin Texts Using the TTLab Latin Tagger. In: Biemann, C., Mehler, A. (eds) Text Mining. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-12655-5_5
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