Linking Etymological Database: A case study in Germanic Christian Chiarcos, Maria Sukhareva Goethe University Frankfurt am Main LDL – 2014, LREC Reykjavik, Iceland 27th May 2014

Overview 1.Background 2.Linked Etymological Dictionaries 3.Enriching of Linked Etymological Dictionaries 4.Application 5.Conclusion

Background

ACoLi Lab TITUS DDD Referenzkorpus Althochdeutsch Background 1.Empirical Linguistics Thesaurus of Indo-European Text and Language Materials (TITUS) 2.ACoLi Lab (Applied Computational Linguistics) 3.LOEWE Cluster “Digital Humanities” 4.DFG-funded Old German Reference Corpus (DDD) Processing of Old Germanic Languages at Goethe University Frankfurt, in collaboration between:

Linked Etymological Data

Linkability: representation of relations within and beyond lexicons Interoperability: (meta)data representation through community-maintained vocabularies (lexvo, Glottolog, OLiA, lemon) Inference: filling the logical gaps of the original XML representation – Symmetric closure of cross-references Conversion of etymological dictionaries to RDF

Linked Etymological Data lemonet:translates a relation between lemon:LexicalEntrys lemonet:etym links between languages, transitive and symmetric. Subproperty of lemon:lexicalVariant all language identifiers were mapped from the original abbreviations and assigned ISO codes wherever possible.

Linked Etymological Data Original XML (lemma) RDF Triples Symmetric closure of etymological relations generated by SPARQL pattern Links to external resources

Enriching Etymological Dictionaries

(parentheses indicate marginal fragments with less than 50,000 tokens) Germanic parallel Bible corpus

Enriching Etymological Dictionaries

Application

Thematical Alignment of Bible paraphrases – E.g., cross references within the Bible and between the Bible and gospel harmonies an interlinked index of thematically similar sections in the gospels and OS/OHG gospel harmonies – OS Heliand and OHG Tatian section level alignment (Sievers, 1872) has been digitized – 4560 inter-text groups based on the Eusebian canon Basis for a more fine-grained level of alignment

Application Character-based similarity measures: – GEOMETRY: δ = difference between the relative positions of w OS and w OHG – IDENTITY: δ(w OS ;w OHG ) = 1 iff w OHG = w OS (0 otherwise); – ORTHOGRAPHY: relative Levenshtein distance & statistical character replacement probability (Neubig et al., 2012) – NORMALIZATION: norm(w OS ;w OHG ) = δ(w’ OS ;w OHG ), with w’ OS being the OHG ‘normalization’ (Bollmann et al., 2011) – COOCCURRENCES: δ(w OS ;w OHG ) = P(w OS |w OHG) P(w OHG |w OS ) similarity metrics δ(w OS ;w OHG ) for every OS word w OS and its potential OHG cognate w OHG Lexicon-based similarity measures: δ lex (w OS ;w OHG ) = 1 iff w OHG 2 W (0 otherwise) where W is a set of possible OHG translations for w OS suggested by a lexicon, i.e., either:  ETYM: etymological link in (the symmetric closure of the etymological dictionaries,  ETYM-INDIRECT: shared German gloss in the etymological dictionaries,  TRANSLATIONAL DIRECT: link in the translational dictionaries,  TRANSLATIONAL INDIRECT: indirectly linked in the translational dictionaries through a third language.

Application Character-based similarity measures: – GEOMETRY: δ = difference between the relative positions of w OS and w OHG – IDENTITY: δ(w OS ;w OHG ) = 1 iff w OHG = w OS (0 otherwise); – ORTHOGRAPHY: relative Levenshtein distance & statistical character replacement probability (Neubig et al., 2012) – NORMALIZATION: norm(w OS ;w OHG ) = δ(w’ OS ;w OHG ), with w’ OS being the OHG ‘normalization’ (Bollmann et al., 2011) – COOCCURRENCES: δ(w OS ;w OHG ) = P(w OS |w OHG) P(w OHG |w OS ) similarity metrics δ(w OS ;w OHG ) for every OS word w OS and its potential OHG cognate w OHG Lexicon-based similarity measures: δ lex (w OS ;w OHG ) = 1 iff w OHG 2 W (0 otherwise) where W is a set of possible OHG translations for w OS suggested by a lexicon, i.e., either:  ETYM: etymological link in (the symmetric closure of the etymological dictionaries,  ETYM-INDIRECT: shared German gloss in the etymological dictionaries,  TRANSLATIONAL DIRECT: link in the translational dictionaries,  TRANSLATIONAL INDIRECT: indirectly linked in the translational dictionaries through a third language.

Conclusion & Discussion

Conclusion 1.Application of Linked Data Paradigm to modeling of etymological dictionaries 2.Adopting of Lemon core model 3.Representation of Köbler’s dictionary in a machine-readable format 4.Enriching etymological dictionaries by automatically obtained translation pairs 5.Initial experiment on usage of dictionaries for quasi-parallel alignment

lemon & etymology: A square peg for a round hole ? lemon gained a lot of popularity as a shared vocabulary for lexical resources in the LLOD. L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L!

lemon & etymology: A square peg for a round hole ? lemon gained a lot of popularity as a shared vocabulary for lexical resources in the LLOD. … but many of these resources are created by (or for) linguists rather than ontologists. The original motivation for lemon was to lexicalize ontologies. Quite a different problem from the inter- operability issues that linguists are trying to solve by using it. L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L! L!L!L!L!

lemon & etymology: A square peg for a round hole ? lemon gained a lot of popularity as a shared vocabulary for lexical resources in the LLOD. But obviously, our usage of lemon is slightly abusive. 1.Etymological and translational links between WordForms ? 2.No external ontology to ground senses ? 3.No word senses at all ? But that is symptomatic for linguistic resources in a strict sense 4. Similar problems observed by Cysouw & Moran on multilingual dictionaries for South American indigeneous languages.

lemon & etymology: A square peg for a round hole ? lemon gained a lot of popularity as a shared vocabulary for lexical resources in the LLOD. But obviously, our usage of lemon is slightly abusive. 1.Etymological and translational links between word forms ? 2.No external ontology to ground senses ? 3.No word senses at all ? But that is symptomatic for linguistic resources in a strict sense What can we do about this state of affairs ? Would there have been alternative ways to model our data ? Shall we extend/abandon/replace/adjust lemon?

Takk fyrir!