This volume seeks to infer large phylogenetic networks from phonetically encoded lexical
data and contribute in this way to the historical study of language varieties. The
technical step that enables progress in this case is the use of causal inference algorithms.
Sample sets of words from language varieties are preprocessed into automatically inferred
cognate sets, and then modeled as information-theoretic variables based on an
intuitive measure of cognate overlap. Causal inference is then applied to these variables
in order to determine the existence and direction of influence among the varieties.
The directed arcs in the resulting graph structures can be interpreted as reflecting
the existence and directionality of lexical flow, a unified model which subsumes inheritance
and borrowing as the two main ways of transmission that shape the basic lexicon
of languages. A flow-based separation criterion and domain-specific directionality detection
criteria are developed to make existing causal inference algorithms more robust
against imperfect cognacy data, giving rise to two new algorithms. The Phylogenetic
Lexical Flow Inference (PLFI) algorithm requires lexical features of proto-languages to
be reconstructed in advance, but yields fully general phylogenetic networks, whereas the
more complex Contact Lexical Flow Inference (CLFI) algorithm treats proto-languages
as hidden common causes, and only returns hypotheses of historical contact situations
between attested languages.
The algorithms are evaluated both against a large lexical database of Northern Eurasia
spanning many language families, and against simulated data generated by a new
model of language contact that builds on the opening and closing of directional contact
channels as primary evolutionary events. The algorithms are found to infer the existence
of contacts very reliably, whereas the inference of directionality remains difficult. This
currently limits the new algorithms to a role as exploratory tools for quickly detecting
salient patterns in large lexical datasets, but it should soon be possible for the framework
to be enhanced e.g. by confidence values for each directionality decision.
xiii, 363 Seiten
400 Sprache::410 Linguistik::410 Linguistik
Information-theoretic causal inference of lexical flow
Language Science Press
Philosophie und Geisteswissenschaften
Institut für Deutsche und Niederländische Philologie