Research

The focus of this dissertation is syntactic movement and its relationship to surface semantics, morphology, and licensing relations in syntax, with an emphasis on Spanish and English. Chapter 2 argues that Herburger's (2000) Neo-Davidsonian approach to the semantics of focus, as syntactically implemented by Uriagereka (2005), allows for a unified treatment of new information focus and contrastive focus (focus movement to the left periphery and in situ focus) in Spanish. The diverse positions that the focused element can take in the sentence are claimed to be determined by contextual anchoring mechanisms of Raposo and Uriagereka (1995). This entails a remnant movement approach in cases of new information focus in Spanish (Ordóñez 2000). It is suggested that these processes take place covertly in English, contra Kayne (1998). Chapter 3 and Chapter 4 focus on the relationship between syntactic movement and surface semantics by looking at the syntax of preverbal subject in Spanish and English, respectively. According to Chomsky (2001, and subsequent work) and Uriagereka (2008) a.o., movement yields (at least) scopal and discourse-related properties. Movement to Spec,TP in so-called 'flexible word order' languages, like Spanish (contra Alexiadou and Anagnostopoulou 1998, a.o.), and in so-called 'strict' word order languages, like English, provides the testing ground for this hypothesis. It is argued here that both Spanish and English show surface semantics effects correlating with movement into Spec,TP, in keeping with the idea that syntactic movement has an effect on semantics. Chapter 5 explores a number of challenges for the phase-based system dispensing with grammatically significant Spec,H relations. It is proposed here that under a mixed system adopting phases and Long Distance Agreement and, crucially, a Multiple Spell-Out system (Uriagereka 1999), conceptual arguments against Spec,H relations can be circumvented. This is shown to solve a number of problems that the phase-based framework faces.

The best systems for machine translation of natural language are based on statistical models learned from data. Conventional representation of a statistical translation model requires substantial offline computation and representation in main memory. Therefore, the principal bottlenecks to the amount of data we can exploit and the complexity of models we can use are available memory and CPU time, and current state of the art already pushes these limits. With data size and model complexity continually increasing, a scalable solution to this problem is central to future improvement. Callison-Burch et al. (2005) and Zhang and Vogel (2005) proposed a solution that we call "translation by pattern matching", which we bring to fruition in this dissertation. The training data itself serves as a proxy to the model; rules and parameters are computed on demand. It achieves our desiderata of minimal offline computation and compact representation, but is dependent on fast pattern matching algorithms on text. They demonstrated its application to a common model based on the translation of contiguous substrings, but leave some open problems. Among these is a question: can this approach match the performance of conventional methods despite unavoidable differences that it induces in the model? We show how to answer this question affirmatively. The main open problem we address is much harder. Many translation models are based on the translation of discontiguous substrings. The best pattern matching algorithm for these models is much too slow, taking several minutes per sentence. We develop new algorithms that reduce empirical computation time by two orders of magnitude for these models, making translation by pattern matching widely applicable. We use these algorithms to build a model that is two orders of magnitude larger than the current state of the art and substantially outperforms a strong competitor in Chinese-English translation. We show that a conventional representation of this model would be impractical. Our experiments shed light on some interesting properties of the underlying model. The dissertation also includes the most comprehensive contemporary survey of statistical machine translation.

This article presents three studies that investigate when syntactic constraints become available during the processing of long-distance backwards pronominal dependencies (backwards anaphora or cataphora). Earlier work demonstrated that in such structures the parser initiates an active search for an antecedent for a pronoun, leading to gender mismatch effects in cases where a noun phrase in a potential antecedent position mismatches the gender of the pronoun [Van Gompel, R. P. G. & Liversedge, S. P. (2003). The influence of morphological information on cataphoric pronoun assignment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 128–139]. Results from three self-paced reading studies suggest that structural constraints on coreference, in particular Principle C of the Binding Theory [Chomsky, N. (1981). Lectures on government and binding. Dordrecht, Foris], exert an influence at an early stage of this search process, such that gender mismatch effects are elicited at grammatically licit antecedent positions, but not at grammatically illicit antecedent positions. The results also show that the distribution of gender mismatch effects is unlikely to be due to differences in the predictability of different potential antecedents. These findings suggest that backwards anaphora dependencies are processed with a grammatically constrained active search mechanism, similar to the mechanism used to process another type of long-distance dependency, the wh dependency (e.g., [Stowe, L. (1986). Evidence for online gap creation. Language and Cognitive Processes, 1, 227–245; Traxler, M. J., & Pickering, M. J. (1996). Plausibility and the processing of unbounded dependencies: an eye-tracking study. Journal of Memory and Language, 35, 454–475.]). We suggest that the temporal priority for syntactic information observed here reflects the predictabilityof structural information, rather than the need for an architectural constraint that delays the use of non syntactic information.

This dissertation investigates a set of phenomena in Standard Arabic at the syntax-morphology interface, providing an analysis for each within the assumptions of the minimalist program, particularly those related to mechanisms of formal feature licensing. Among the issues discussed are the subject-verb agreement asymmetry, case-assignment, default agreement, nominative Themes, as well as interactions between tense, negation, and modality heads. In this regard, I provide an analysis for word order alternation in the language in terms of left dislocation rather than via movement, showing that the language does not show A-movement effects in SVO orders, passives, raising constructions, or object shift. The same is also shown to hold in what is usually referred to as raising-to-object constructions. The proposed analysis shows that formal features such as case and agreement can be licensed in absence of movement, a conclusion more compatible with the Agree-based approach to formal feature licensing in minimalism than with the Spec-head approach. Finally, I propose to extend Agree to head-head relations in the functional domain, accounting for the interesting, though rather intricate, paradigm of inflecting negatives as well as person-less imperatives in Standard Arabic and languages that exhibit similar behavior.

This dissertation investigates the acquisition of language-specific constraints on scope interpretation by Japanese preschool children. Several constructions in Japanese do not allow scope interpretations that the corresponding English sentences do allow. First, in Japanese transitive sentences with multiple quantificational arguments, an inverse scope interpretation is disallowed, due to the Rigid Scope Constraint. Second, Japanese logical connectives cannot be interpreted under the scope of local negation, due to their Positive Polarity. Thirdly, in Japanese infinitival complement constructions with implicative matrix verbs like wasureru ("forget") the inverse scope interpretation is required, due to the Anti-Reconstruction Constraint. The main goal of this research is to determine how Japanese children learn these constraints on scope interpretations. To that end, three properties of the acquisition task that have an influence on the learnability of linguistic knowledge are examined: productivity, no negative evidence, and arbitrariness. The results of experimental investigations show that Japanese children productively generate scope interpretations that are never exemplified in the input. For example, with sentences that contain two quantificational arguments, Japanese children accessed inverse scope interpretations that Japanese adults do not allow. Also, Japanese children interpret the disjunction ka under the scope of local negation, which is not a possible interpretive option in the adult language. These findings clearly show that children do not acquire these scope constraints through conservative learning, and raise the question of how they learn to purge their non-adult interpretations. It is argued that input data do not provide learners with negative evidence (direct or indirect) against particular scope interpretations. Two inherent properties of input data about possible scope interpretations, data sparseness and indirectness, make negative evidence too unreliable as a basis for discovering what scope interpretation is impossible. In order to solve the learnability problems that children's scope productivity raise, I suggest that the impossibility of their non-adult interpretations are acquired by learning some independently observable properties of the language. In other words, the scope constraints are not arbitrary in the sense that their effects are consequences of other properties of the grammar of Japanese.

Current interest in automatic sentiment analysis is motivated by a variety of information requirements. The vast majority of work in sentiment analysis has been specifically targeted at detecting subjective statements and mining opinions. This dissertation focuses on a different but related problem that to date has received relatively little attention in NLP research: detecting implicit sentiment, or spin, in text. This text classification task is distinguished from other sentiment analysis work in that there is no assumption that the documents to be classified with respect to sentiment are necessarily overt expressions of opinion. They rather are documents that might reveal a perspective. This dissertation describes a novel approach to the identification of implicit sentiment, motivated by ideas drawn from the literature on lexical semantics and argument structure, supported and refined through psycholinguistic experimentation. A relationship predictive of sentiment is established for components of meaning that are thought to be drivers of verbal argument selection and linking and to be arbiters of what is foregrounded or backgrounded in discourse. In computational experiments employing targeted lexical selection for verbs and nouns, a set of features reflective of these components of meaning is extracted for the terms. As observable proxies for the underlying semantic components, these features are exploited using machine learning methods for text classification with respect to perspective. After initial experimentation with manually selected lexical resources, the method is generalized to require no manual selection or hand tuning of any kind. The robustness of this linguistically motivated method is demonstrated by successfully applying it to three distinct text domains under a number of different experimental conditions, obtaining the best classification accuracies yet reported for several sentiment classification tasks. A novel graph-based classifier combination method is introduced which further improves classification accuracy by integrating statistical classifiers with models of inter-document relationships.

There has always been interest in the methodology of acceptability judgment collection, as well as the reliability of the results. It seems, though, that the past several years have seen an increase in the number of studies employing formal experimental techniques for the collection of acceptability judgments, so much so that the term experimental syntax has come to be applied to the use of those techniques. The question this dissertation asks is whether the extent of the utility of experimental syntax is to find areas in which informal judgment collection was insufficient, or whether there is a complementary research program for experimental syntax that is more than just a methodological footnote to the informal judgment collection of theoretical syn- tax. This dissertation is a first attempt at a tentative yes: the tools of experimental syntax can be used to explore the relationship between acceptability judgments and the form or nature of grammatical knowledge, not just the content of grammatical knowledge. This dissertation begins by identifying several recent claims about the nature of grammatical knowledge that have been made based upon hypotheses about the nature of acceptability judgments. Each chapter applies the tools of experimental syntax to those hypotheses in an attempt to refine our understanding of the relationship between acceptability and grammatical knowledge. The claims investigated include: that grammatical knowledge is gradient, that grammatical knowledge is sensitive to context effects, that the stability or instability of acceptability reflects underlying differences in grammatical knowledge, that processing effects affect acceptability, and that acceptability judgments have nothing further to contribute to debates over the number and nature of dependency forming operations. Using wh-movement and Island effects as the empirical basis of the research, the results of these studies suggest that the relationship between acceptability and grammatical knowledge is much more complicated than previously thought. The overarching conclusion is that there is a program for experimental syntax that is independent of simple data collection: only through the tools of experimental syntax can we achieve a better understanding of the nature of acceptability, and how it relates to the nature of grammatical knowledge.

Linguistics and psycholinguistics differ not in their topic but in their tools, and our choice of tools should be commensurate to the hypotheses we are testing. A case study of long-distance dependencies serves to illustrate the point.

This dissertation investigates the mechanism of language acquisition given the boundary conditions provided by linguistic representation and the time course of acquisition. Exploration of the mechanism is vital once we consider the complexity of the system to be learned and the non-transparent relationship between the observable data and the underlying system. It is not enough to restrict the potential systems the learner could acquire, which can be done by defining a finite set of parameters the learner must set. Even supposing that the system is defined by n binary parameters, we must still explain how the learner converges on the correct system(s) out of the possible 2^n systems, using data that is often highly ambiguous and exception-filled. The main discovery from the case studies presented here is that learners can in fact succeed provided they are biased to only use a subset of the available input that is perceived as a cleaner representation of the underlying system. The case studies are embedded in a framework that conceptualizes language learning as three separable components, assuming that learning is the process of selecting the best-fit option given the available data. These components are (1) a defined hypothesis space, (2) a definition of the data used for learning (data intake), and (3) an algorithm that updates the learner's belief in the available hypotheses, based on data intake. One benefit of this framework is that components can be investigated individually. Moreover, defining the learning components in this somewhat abstract manner allows us to apply the framework to a range of language learning problems and linguistics domains. In addition, we can combine discrete linguistic representations with probabilistic methods and so account for the gradualness and variation in learning that human children display. The tool of exploration for these case studies is computational modeling, which proves itself very useful in addressing the feasibility, sufficiency, and necessity of data intake filtering since these questions would be very difficult to address with traditional experimental techniques. In addition, the results of computational modeling can generate predictions that can then be tested experimentally.

This dissertation examines Agree, a narrow syntactic, long-distance operation underlying phi-agreement in the grammar. Taking the strong minimalist thesis (cf. Chomsky 2000) as my point of departure, I question Agree on both conceptual and empirical grounds. On the conceptual side, the operation is suspect first for its language-specific character. Second, it also fails to be justified on the grounds of general architectural constraints and legibility requirements. Further, evidences of various long-distance agreement from across languages examined here question the empirical basis for Agree built throughout the previous literature. As far as this is true, I contend that the faculty of language has nothing beyond Merge and Move/Internal Merge, the first being inevitable in any language-like system and the latter necessitated by interface exigencies. My purpose in this dissertation is to show that these two operations suffice to obtain phi-agreement in natural language.