Towards a Network Model of the Coreness of Texts: An Experiment in Classifying Latin Texts Using the TTLab Latin Tagger

Mehler, Alexander; Brück, Tim vor der; Gleim, Rüdiger; Geelhaar, Tim

doi:10.1007/978-3-319-12655-5_5

Cited by 10 publications

(10 citation statements)

References 25 publications

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“…For Latin, we choose a subset of ColLex.LA (Mehler et al, 2015) as our dictionary of choice and for English, we use ColLex.EN (vor der Brück et al, 2014). 14 Table 3 gives the number of entries in both lexicons as well as OOV numbers.…”

Section: Evaluation Setupmentioning

confidence: 99%

A Comparison of Four Character-Level String-to-String Translation Models for (OCR) Spelling Error Correction

Eger¹,

Brück²,

Mehler³

2016

The Prague Bulletin of Mathematical Linguistics

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We consider the isolated spelling error correction problem as a specific subproblem of the more general string-to-string translation problem. In this context, we investigate four general string-to-string transformation models that have been suggested in recent years and apply them within the spelling error correction paradigm. In particular, we investigate how a simple ‘k-best decoding plus dictionary lookup’ strategy performs in this context and find that such an approach can significantly outdo baselines such as edit distance, weighted edit distance, and the noisy channel Brill and Moore model to spelling error correction. We also consider elementary combination techniques for our models such as language model weighted majority voting and center string combination. Finally, we consider real-world OCR post-correction for a dataset sampled from medieval Latin texts.

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Section: Evaluation Setupmentioning

confidence: 99%

A Comparison of Four Character-Level String-to-String Translation Models for (OCR) Spelling Error Correction

Eger¹,

Brück²,

Mehler³

2016

The Prague Bulletin of Mathematical Linguistics

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“…Finally, we conclude with a prospect on future work. To this end, the texts are automatically preprocessed and converted into TEI P5 2 (TEI Consortium 2007) -see Mehler et al (2015) for the most current description of this procedure by example of Latin texts. A sidebar to the left allows for selecting the working language (currently, English, German or Latin -see Fig.…”

Section: Resultsmentioning

confidence: 99%

Towards a Theoretical Framework for Analyzing Complex Linguistic Networks

Mehler¹,

Lcking²,

Banisch³

et al. 2016

Understanding Complex Systems

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Complexity is an interdisciplinary program publishing the best research and academiclevel teaching on both fundamental and applied aspects of complex systems-cutting across all traditional disciplines of the natural and life sciences, engineering, economics, medicine, neuroscience, social and computer science.Complex Systems are systems that comprise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous formation of distinctive temporal, spatial or functional structures. Models of such systems can be successfully mapped onto quite diverse "real-life" situations like the climate, the coherent emission of light from lasers, chemical reaction-diffusion systems, biological cellular networks, the dynamics of stock markets and of the internet, earthquake statistics and prediction, freeway traffic, the human brain, or the formation of opinions in social systems, to name just some of the popular applications.Although their scope and methodologies overlap somewhat, one can distinguish the following main concepts and tools: self-organization, nonlinear dynamics, synergetics, turbulence, dynamical systems, catastrophes, instabilities, stochastic processes, chaos, graphs and networks, cellular automata, adaptive systems, genetic algorithms and computational intelligence.The three major book publication platforms of the Springer Complexity program are the monograph series "Understanding Complex Systems" focusing on the various applications of complexity, and the "Springer Series in Synergetics", which is devoted to the quantitative theoretical and methodological foundations, and the "SpringerBriefs in Complexity" which are concise and topical working reports, case-studies, surveys, essays and lecture notes of relevance to the field. In addition to the books in these two core series, the program also incorporates individual titles ranging from textbooks to major reference works.Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition-typically many different kinds of components interacting simultaneously and nonlinearly with each other and their environments on multiple levels-and in the rich diversity of behavior of which they are capable.The Springer Series in Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of complex systems at all levels of description and in all scientific fields, especially newly emerging areas within the life, social, behavioral, economic, neuro-and cognitive sciences (and derivatives thereof); second, to encourage novel applications of these ideas in various fields of engineering and computation such as robotics, nano-technology...

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“…Our lexicon named Collex.LA (Mehler et al, 2015) consists both of manually created lexicon entries as well as of automatically extracted entries from several freely available Web resources, in particular AGFL (Koster and Verbruggen, 2002), LemLat (Passerotti, 2004), Perseus Digital Li-brary (Smith et al, 2000), Whitaker word list 4 , Thomisticum 5 (Busa, 1980;McGilivray et al, 2009), Ramminger word list 6 , and several others. In total it consists of 8 347 062 word forms, 119 595 lemmas and 104 905 superlemmas.…”

Section: Lexiconmentioning

confidence: 99%

Lexicon-assisted tagging and lemmatization in Latin: A comparison of six taggers and two lemmatization models

Brück

Eger

Mehler

2015

Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)

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We present a survey of tagging accuracies-concerning part-of-speech and full morphological tagging-for several taggers based on a corpus for medieval church Latin (see www.comphistsem.org). The best tagger in our sample, Lapos, has a PoS tagging accuracy of close to 96% and an overall tagging accuracy (including full morphological tagging) of about 85%. When we 'intersect' the taggers with our lexicon, the latter score increases to almost 91% for Lapos. A conservative assessment of lemmatization accuracy on our data estimates a score of 93-94% for a lexicon-based lemmatization strategy and a score of 94-95% for lemmatizing via trained lemmatizers.

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Towards a Network Model of the Coreness of Texts: An Experiment in Classifying Latin Texts Using the TTLab Latin Tagger

Cited by 10 publications

References 25 publications

A Comparison of Four Character-Level String-to-String Translation Models for (OCR) Spelling Error Correction

A Comparison of Four Character-Level String-to-String Translation Models for (OCR) Spelling Error Correction

Towards a Theoretical Framework for Analyzing Complex Linguistic Networks

Lexicon-assisted tagging and lemmatization in Latin: A comparison of six taggers and two lemmatization models

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