The Probabilistic Representation of Linguistic Knowledge

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In the past twenty-five years work in natural language technology has made impressive progress across a wide range of tasks, which include, among others, information retrieval and extraction, text interpretation and summarization, speech recognition, morphological analysis, syntactic parsing, word sense identification, and machine translation. Much of this progress has been due to the successful application of powerful techniques for probabilistic modeling and statistical analysis to large corpora of linguistic data. These methods have given rise to a set of engineering tools that are rapidly shaping the digital environment in which we access and process most of the information that we use. In recent work (Lappin and Shieber (2007), Clark and Lappin (2011a), Clark and Lappin (2011b)) my co-authors and I have argued that the machine learning methods that are driving the expansion of natural language technology are also directly relevant to understanding central features of human language acquisition. When these methods are used to construct carefully specified formal models and implementations of the grammar induction task, they yield striking insights into the limits and possibility of human learning on the basis of the primary linguistic data to which children are exposed. These models indicate that language learning can be achieved without the sorts of strong innate learning biases that have been posited by traditional theories of universal grammar. Weak biases, some derivable from non-linguistic cognitive domains, and domain general learning procedures are sufficient to support efficient data driven learning of plausible systems of grammatical representation.In the current research I am focussing on the problem of how to specify the class of representations that encode human knowledge of the syntax of natural languages. I am pursuing the hypothesis that a representation in this class is best expressed as an enriched statistical language model that assigns probability values to the sentences of a language. A central part of the enrichment of the model consists of a procedure for determining the acceptability (grammaticality) of a sentence as a graded value, relative to the properties of that sentence and the language of which it is a part. This procedure avoids the simple reduction of the grammaticality of a string to its estimated probability of occurrence, while still characterizing grammaticality in probabilistic terms. An enriched model of this kind will provide a straightforward explanation for the fact that individual native speakers generally judge the well formedness of sentences along a continuum, rather than through the imposition of a sharp boundary between acceptable and unacceptable sentences. The pervasiveness of gradedness in the linguistic knowledge of individual speakers poses a serious problem for classical theories of syntax, which partition strings of words into the grammatical sentences of a language and ill formed strings of words. This research holds out the prospect of important impact in two areas. First, it can shed light on the relationship between the representation and acquisition of linguistic knowledge on one hand, and learning and the encoding of knowledge in other cognitive domains. This work can, in turn, help to clarify the respective roles of biologically conditioned learning biases and data driven learning in human cognition. Second, this work can contribute to the development of more effective language technology by providing insight, from a computational perspective, into the way in which humans represent the syntactic properties of sentences in their language. To the extent that natural language processing systems take account of this class of representations they will provide more efficient tools for parsing and interpreting text and speech.