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This dissertation deals with the lexical, morphological, syntactic, and semantic properties of (VP )idioms and their behavior in combination with restrictive relative clauses, raising, constituent fronting, wh-movement, VP-ellipsis, pronominalization, the progressive form, verb placement, passivization, conjunction modification, and the N-after-N construction. It provides empirical evidence towards a combinatorial analysis of both semantically non-decomposable idioms (SNDIs) and semantically decomposable idioms (SDIs) and contributes to the (formal) formulation of such an account.
The Introduction (Chapter 1) first motivates why idioms are an exciting and challenging phenomenon and then gives a definition of the term idiom, a classification of idioms, and an overview of the wide spectrum of idiom analyses found in the linguistic literature.
Chapter 2, “Idioms as evidence for the proper analysis of relative clauses”, shows that the Modification Analysis beats the other two major analyses of restrictive relative clauses (RRCs), namely Raising and Matching, as (i) the latter two lead to a loss of numerous empirical generalizations in syntax and morphology, and (ii) contrary to the assumption in the literature, idioms in RRCs can, in fact, be licensed without literal syntactic movement of the RRC-head, which makes modification fully compatible with idiom reconstruction effects.
Chapter 3, “How frozen are frozen idioms?”, presents new empirical observations on the lexical, morphological, and syntactic flexibility of kick the bucket and displays that this idiom is not completely frozen with respect to its NP complement, the progressive form, and, in some contexts, even passivization. The chapter concludes that analyses of kick the bucket as a single lexical entry should be replaced by analyses of this and other SNDIs with a syntactically regular shape as consisting of individual word-level lexical entries that combine according to the standard rules of syntax.
This idea is taken up in Chapter 4, “The syntactic flexibility of semantically non-decomposable idioms”, which – based on the differences between English and German with regard to verb placement, constituent fronting, and passivization as well as a short outlook on Estonian and French – spells out a combinatorial analysis of SNDIs and augments it with a semantic analysis formulated in Lexical Resource Semantics, according to which some idiom parts make identical semantic contributions to the overall meaning of the idiom. The analysis further suggests that the syntactic flexibility of idioms is due to the semantic and pragmatic constraints on the involved constructions, rather than the syntactic encoding of the idioms.
Chapter 5, “Modification of literal meanings in semantically non-decomposable idioms”, reviews Ernst’s (1981) classical three types of idiom modification (internal, external, and conjunction) to then closely investigate the most challenging type, namely conjunction modification, in SNDIs. Based on naturally occurring examples of four SNDIs (two English, two German), it sketches an analysis in terms of two or more conjoined independent propositions, each of which can be the result of figurative reinterpretation. One of the propositions contains the idiomatic meaning, in (one of) the other(s), the meaning of the modifier applies to the literal meaning of the idiom’s noun.
Chapter 6, “Semantically decomposable idioms in the N-after-N construction”, offers a formal syntactic and semantic account of SDIs like pull strings in the N-after-N construction, as in Kim pulled string after string to get Alex into a good college. While the idiom contributes the type of entity at stake (‘string’ in the case of pull strings), N-after-N contributes that there are several instantiations of that type of entity and that these are subject to temporal or spatial succession. The chapter first summarizes the empirical properties of N-after-N, then provides an account of N-after-N in Head-driven Phrase Structure Grammar (HPSG), presents an updated version of the account of SDIs suggested in Chapter 2 within HPSG, and combines it with the HPSG account of N-after-N.
This thesis investigates the structure of research articles in the field of Computational Linguistics with the goal of establishing that a set of distinctive linguistic features is associated with each section type. The empirical results of the study are derived from the quantitative and qualitative evaluation of research articles from the ACL Anthology Corpus. More than 20,000 articles were analyzed for the purpose of retrieving the target section types and extracting the predefined set of linguistic features from them. Approximately 1,100 articles were found to contain all of the following five section types: abstract, introduction, related work, discussion, and conclusion. These were chosen for the purpose of comparing the frequency of occurrence of the linguistic features across the section types. Making use of frameworks for Natural Language Processing, the Stanford CoreNLP Module, and the Python library SpaCy, as well as scripts created by the author, the frequency scores of the features were retrieved and analyzed with state-of-the-art statistical techniques.
The results show that each section type possesses an individual profile of linguistic features which are associated with it more or less strongly. These section-feature associations are shown to be derivable from the hypothesized purpose of each section type.
Overall, the findings reported in this thesis provide insights into the writing strategies that authors employ so that the overall goal of the research paper is achieved.
The results of the thesis can find implementation in new state-of-the-art applications that assist academic writing and its evaluation in a way that provides the user with a more sophisticated, empirically based feedback on the relationship between linguistic mechanisms and text type. In addition, the potential of the identification of text-type specific linguistic characteristics (a text-feature mapping) can contribute to the development of more robust language-based models for disinformation detection.