From Generative Linguistics to Categorial Grammars: Overt Subjects in Control Infinitives

The present thesis lies at the interface of logic and linguistics; its object of study are control sentences with overt pronouns in Romance languages (European and Brazilian Portuguese, Italian and Spanish). This is a topic that has received considerably more attention on the part of linguists, especially in recent years, than from logicians. Perhaps for this reason, much remains to be understood about these linguistic structures and their underlying logical properties. This thesis seeks to fill the lacunas in the literature or at least take steps in this direction by way of addressing a number of issues that have so far been under-explored. To this end, we put forward two key questions, one linguistic and the other logical. These are, respectively, (1) What is the syntactic status of the surface pronoun? and (2) What are the available mechanisms to reuse semantic resources in a contraction-free logical grammar? Accordingly, the thesis is divided into two parts: generative linguistics and categorial grammar. Part I starts by reviewing the recent discussion within the generative literature on infinitive clauses with overt subjects, paying detailed attention to the main accounts in the field. Part II does the same on the logical grammar front, addressing in particular the issues of control and of anaphoric pronouns. Ultimately, the leading accounts from both camps will be found wanting. The closing chapter of each of Part I and Part II will thus put forward alternative candidates, that we contend are more successful than their predecessors. More specifically, in Part I, we offer a linguistic account along the lines of Landau’s T/Agr theory of control. In Part II, we present two alternative categorial accounts: one based on Combinatory Categorial Grammar, the other on Type-Logical Grammar. Each of these accounts offers an improved, more fine-grained perspective on control infinitives featuring overt pronominal subjects. Finally, we include an Appendix in which our type-logical proposal is implemented in a categorial parser/theorem-prover.Abstract prepared by María Inés Corbalán.E-mail: [email protected]: http://repositorio.unicamp.br/jspui/handle/REPOSIP/331697

Strong Equivalence of TAG and CCG

Tree-adjoining grammar (TAG) and combinatory categorial grammar (CCG) are two well-established mildly context-sensitive grammar formalisms that are known to have the same expressive power on strings (i.e., generate the same class of string languages). It is demonstrated that their expressive power on trees also essentially coincides. In fact, CCG without lexicon entries for the empty string and only first-order rules of degree at most 2 are sufficient for its full expressive power.

On the Complexity of CCG Parsing

We study the parsing complexity of Combinatory Categorial Grammar (CCG) in the formalism of Vijay-Shanker and Weir ( 1994 ). As our main result, we prove that any parsing algorithm for this formalism will take in the worst case exponential time when the size of the grammar, and not only the length of the input sentence, is included in the analysis. This sets the formalism of Vijay-Shanker and Weir ( 1994 ) apart from weakly equivalent formalisms such as Tree Adjoining Grammar, for which parsing can be performed in time polynomial in the combined size of grammar and input sentence. Our results contribute to a refined understanding of the class of mildly context-sensitive grammars, and inform the search for new, mildly context-sensitive versions of CCG.

Examining Working Memory during Sentence Construction with an ACT-R Model of Grammatical Encoding

We examine working memory use and incrementality using acognitive model of grammatical encoding. Our model combinesan empirically validated framework, ACT-R, with a linguistictheory, Combinatory Categorial Grammar, to target thatphase of language production. By building the model with theSwitchboard corpus, it can attempt to realize a larger set ofsentences. With this methodology, different strategies may becompared according to the similarity of the model’s sentencesto the test sentences. In this way, the model can still be evaluatedby its fit to human data, without overfitting to individualexperiments. The results show that while having more workingmemory available improves performance, using less workingmemory during realization is correlated with a closer fit,even after controlling for sentence complexity. Further, sentencesrealized with a more incremental strategy are also moresimilar to the corpus sentences as measured by edit distance.As high incrementality is correlated with low working memoryusage, this study offers a possible mechanism by whichincrementality can be explained.