stages: Germanic: *fall-an ↔ *fall-jan Modern English: to fall ↔ to fell Indo-Eur.: *dʰréng-e- ’to drink’ ↔ *dʰrong-éie- ’to make drink’ Old English: drinc-an ’to drink’ ← → drenċ-an ’to make drink’ Modern English: to drink ← | → to drench 6 / 18
stages: Germanic: *fall-an ↔ *fall-jan Modern English: to fall ↔ to fell Indo-Eur.: *dʰréng-e- ’to drink’ ↔ *dʰrong-éie- ’to make drink’ Old English: drinc-an ’to drink’ ← → drenċ-an ’to make drink’ Modern English: to drink ← | → to drench Historical linguistics is interested in diachronic relationships. 6 / 18
stages: Germanic: *fall-an ↔ *fall-jan Modern English: to fall ↔ to fell Indo-Eur.: *dʰréng-e- ’to drink’ ↔ *dʰrong-éie- ’to make drink’ Old English: drinc-an ’to drink’ ← → drenċ-an ’to make drink’ Modern English: to drink ← | → to drench Historical linguistics is interested in diachronic relationships. Word formation can best be described in synchronic results. 6 / 18
stages: Germanic: *fall-an ↔ *fall-jan Modern English: to fall ↔ to fell Indo-Eur.: *dʰréng-e- ’to drink’ ↔ *dʰrong-éie- ’to make drink’ Old English: drinc-an ’to drink’ ← → drenċ-an ’to make drink’ Modern English: to drink ← | → to drench Historical linguistics is interested in diachronic relationships. Word formation can best be described in synchronic results. → How do we combine these perspectives? 6 / 18
Fisch - English fish partial cognates: Latin piscator - English fisher borrowings: Latin piscatorius → English piscatory Only by knowing the synchronic relationships can we determine the diachronic ones. 7 / 18
Fisch - English fish partial cognates: Latin piscator - English fisher borrowings: Latin piscatorius → English piscatory Only by knowing the synchronic relationships can we determine the diachronic ones. And we might be interested in historical synchronic stages of languages for their own sake. 7 / 18
large amounts of data finding patterns increasing transparency and retraceability lack human intuition → need to be provided exhaustive information 8 / 18
historical comparative linguistics used to analyze large amounts of language data LexStat: based on detecting regular sound correspondences: works in principle like comparative method 9 / 18
historical comparative linguistics used to analyze large amounts of language data LexStat: based on detecting regular sound correspondences: works in principle like comparative method partial cognacy and context-dependent sound shifts can seriously hamper results 9 / 18
historical comparative linguistics used to analyze large amounts of language data LexStat: based on detecting regular sound correspondences: works in principle like comparative method partial cognacy and context-dependent sound shifts can seriously hamper results → Solution: Provide framework of possible relations between words to computer 9 / 18
of computers human-readable: comfortable and easy to use standardized: facilitating collaboration and re-use of data and scripts exhaustive: both synchronic and diachronic relations word formation sound changes analogy borrowing 10 / 18
the CLTF-standard one row for each word form one column for each type of annotation cognate morphemes linked via cross-IDs additional file for specifying word relations 11 / 18
fish *pisḱos p i s c + o s 1 0 2 English fish fish f i ʃ 1 3 Latin fish piscis p i s k + i s 1 0 4 English fishing fishing f i ʃ + i ŋ 1 2 5 Latin to fish piscari p i s k + aː r iː 1 3 *pisḱos piscis fish fishing piscari Source Target Change 1 2 sound change 1 3 sound change 2 4 word formation 3 5 word formation 12 / 18
word formation ID DOCULECT CONCEPT FORM TOKENS CROSSIDS ROOTIDS 1 Indo-European to drink *dʰrénge- dʰ r é n g + e 1 0 1 0 2 Indo-European to make drink *dʰrongéie- dʰ r o n g + é i e 2 0 1 0 3 English to drink drink d r ɪ ŋ k 1 1 4 English to drench drench d r ɛ n tʃ 2 1 *dʰrénge- *dʰrongéie- drink drench Source Target Change 1 2 causative 1 3 sound change 2 4 sound change 13 / 18
words as possible Transparency of data vs. interpretation Python library of standard procedures in annotation Fully annotated example wordlists to be used for research Automatic visualization tools for data exploration and analysis 17 / 18
List (Group leader) Dr. Yunfan Lai (Post-Doc) Dr. Tiago Tresoldi (Post-Doc) Mei-Shin Wu (Doctoral student) Nathanael E. Schweikhard (Doctoral student) http://calc.digling.org/ 18 / 18
Schweikhard
schweikhard
skipperchong
carmenhchung
keathley
productmarketing
clairelew
thoeni
jcasabona
cherdarchuk
lauravandoore
