Introduction: The Humanities and the Sciences.

The humanities and the sciences have a strongly connected history, yet their histories continue to be written separately. Although the scope of the history of science has undergone a tremendous broadening during the past few decades, scholars of the history of the humanities and the history of science still seem to belong to two separate cultures that have endured through the past century. This Focus section explores what common ground would enable a study of the histories of the humanities and the sciences to investigate their shared epistemic objects, virtues, values, methods, and practices.

A Comparative Framework for Studying the Histories of the Humanities and Science.

While the humanities and the sciences have a closely connected history, there are no general histories that bring the two fields together on an equal footing. This paper argues that there is a level at which some humanistic and scientific disciplines can be brought under a common denominator and compared. This is at the level of underlying methods, especially at the level of formalisms and rule systems used by different disciplines. The essay formally compares linguistics and computer science by noting that the same grammar formalism was used in the 1950s for describing both human and. programming languages. Additionally, it examines the influence of philology on molecular biology, and vice versa, by recognizing that the tree-formalism and rule system used for text reconstruction was also employed in DNA genetics. It also shows that rule systems for source criticism in history are used in forensic science, evidence-based medicine, and jurisprudence. This paper thus opens up a new comparative approach within which the histories of the humanities and the sciences can be examined on a common level.

Three design principles of language: the search for parsimony in redundancy.

In this paper we present three design principles of language - experience, heterogeneity and redundancy--and present recent developments in a family of models incorporating them, namely Data-Oriented Parsing/Unsupervised Data-Oriented Parsing. Although the idea of some form of redundant storage has become part and parcel of parsing technologies and usage-based linguistic approaches alike, the question how much of it is cognitively realistic and/or computationally optimally efficient is an open one. We argue that a segmentation-based approach (Bayesian Model Merging) combined with an all-subtrees approach reduces the number of rules needed to achieve an optimal performance, thus making the parser more efficient. At the same time, starting from unsegmented wholes comes closer to the acquisitional situation of a language learner, and thus adds to the cognitive plausibility of the model.

How hierarchical is language use?

It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science.

Structured cognition and neural systems: from rats to language.

Much of animal and human cognition is compositional in nature: higher order, complex representations are formed by (rule-governed) combination of more primitive representations. We review here some of the evidence for compositionality in perception and memory, motivating an approach that takes ideas and techniques from computational linguistics to model aspects of structural representation in cognition. We summarize some recent developments in our work that, on the one hand, use algorithms from computational linguistics to model memory consolidation and the formation of semantic memory, and on the other hand use insights from the neurobiology of memory to develop a neurally inspired model of syntactic parsing that improves over existing (not cognitively motivated) models in computational linguistics. These two theoretical studies highlight interesting analogies between language acquisition, semantic memory and memory consolidation, and suggest possible neural mechanisms, implemented in computational algorithms that may underlie memory consolidation.

Insensitivity of the human sentence-processing system to hierarchical structure.

Although it is generally accepted that hierarchical phrase structures are instrumental in describing human language, their role in cognitive processing is still debated. We investigated the role of hierarchical structure in sentence processing by implementing a range of probabilistic language models, some of which depended on hierarchical structure, and others of which relied on sequential structure only. All models estimated the occurrence probabilities of syntactic categories in sentences for which reading-time data were available. Relating the models' probability estimates to the data showed that the hierarchical-structure models did not account for variance in reading times over and above the amount of variance accounted for by all of the sequential-structure models. This suggests that a sentence's hierarchical structure, unlike many other sources of information, does not noticeably affect the generation of expectations about upcoming words.

Children's grammars grow more abstract with age--evidence from an automatic procedure for identifying the productive units of language.

We develop an approach to automatically identify the most probable multiword constructions used in children's utterances, given syntactically annotated utterances from the Brown corpus of CHILDES. The found constructions cover many interesting linguistic phenomena from the language acquisition literature and show a progression from very concrete toward abstract constructions. We show quantitatively that for all children of the Brown corpus grammatical abstraction, defined as the relative number of variable slots in the productive units of their grammar, increases globally with age.

From exemplar to grammar: a probabilistic analogy-based model of language learning.

While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase-structure trees should be assigned to initial sentences, s/he allows (implicitly) for all possible trees and lets linguistic experience decide which is the "best" tree for each sentence. The best tree is obtained by maximizing "structural analogy" between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rule-based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children's language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions.