Adjectivization in Russian: Analyzing participles by means of lexical frequency and constraint grammar

This dissertation explores the factors that restrict and facilitate adjectivization in Russian, an affixless part-of-speech change leading to ambiguity between participles and adjectives. I develop a theoretical framework based on major approaches to adjectivization, and assess the effect of the factors on ambiguity in the empirical data. I build a linguistic model using the Constraint Grammar formalism. The model utilizes the factors of adjectivization and corpus frequencies as formal constraints for differentiating between participles and adjectives in a disambiguation task. The main question that is explored in this dissertation is which linguistic factors allow for the differentiation between adjectivized and unambiguous participles. Another question concerns which factors, syntactic or morphological, predict ambiguity in the corpus data and resolve it in the disambiguation model. In the theoretical framework, the syntactic context signals whether a participle is adjectivized, whereas internal morphosemantic properties (that is, tense, voice, and lexical meaning) cause or prevent adjectivization. The exploratory analysis of these factors in the corpus data reveals diverse results. The syntactic factor, the adverb of measure and degree očenʹ ‘very’, which is normally used with adjectives, also combines with participles, and is strongly associated with semantic classes of their base verbs. Nonetheless, the use of očenʹ with a participle only indicates ambiguity when other syntactic factors of adjectivization are in place. The lexical frequency (including the ranks of base verbs and the ratios of participles to other verbal forms) and several morphological types of participles strongly predict ambiguity. Furthermore, past passive and transitive perfective participles not only have the highest mean ratios among the other morphological types of participles, but are also strong predictors of ambiguity. The linguistic model using weighted syntactic rules shows the highest accuracy in disambiguation compared to the models with weighted morphological rules or the rule based on weights only. All of the syntactic, morphological, and weighted rules combined show the best performance results. Weights are the most effective for removing residual ambiguity (similar to the statistical baseline model), but are outperformed by the models that use factors of adjectivization as constraints

Information-theoretic causal inference of lexical flow

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

Tackling the Toolkit. Plotting Poetry through Computational Literary Studies

In Tackling the Toolkit, we focus on the methodological innovations, challenges, obstacles and even shortcomings associated with applying quantitative methods to poetry specifically and poetics more broadly. Using tools including natural language processing, web ontologies, similarity detection devices and machine learning, our contributors explore not only metres, stanzas, stresses and rhythms but also genres, subgenres, lexical material and cognitive processes. Whether they are testing old theories and laws, making complex concepts machine-readable or developing new lines of textual analysis, their works challenge standard descriptions of norms and variations

Information-theoretic causal inference of lexical flow

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