A Competitve Attachment Model for Resolving Syntactic Ambiguities in Natural Language Parsing

Linguistic ambiguity is the greatest obstacle to achieving practical computational systems for natural language understanding. By contrast, people experience surprisingly little difficulty in interpreting ambiguous linguistic input. This dissertation explores distributed computational techniques for mimicking the human ability to resolve syntactic ambiguities efficiently and effectively. The competitive attachment theory of parsing formulates the processing of an ambiguity as a competition for activation within a hybrid connectionist network. Determining the grammaticality of an input relies on a new approach to distributed communication that integrates numeric and symbolic constraints on passing features through the parsing network. The method establishes syntactic relations both incrementally and efficiently, and underlies the ability of the model to establish long-distance syntactic relations using only local communication within a network. The competitive distribution of numeric evidence focuses the activation of the network onto a particular structural interpretation of the input, resolving ambiguities. In contrast to previous approaches to ambiguity resolution, the model makes no use of explicit preference heuristics or revision strategies. Crucially, the structural decisions of the model conform with human preferences, without those preferences having been incorporated explicitly into the parser. Furthermore, the competitive dynamics of the parsing network account for additional on-line processing data that other models of syntactic preferences have left unaddressed. (Also cross-referenced as UMIACS-TR-95-55

Adaptive Eye Movement Control in a Simple Linguistic Task.

This dissertation pursues a computationally rational analysis of eye movements in a simple list-reading task. The strength of the computationally rational approach is in the ability to explain why certain phenomena may emerge under the assumption that behavior is an approximately optimal adaptation to the joint constraints of an organism's intrinsic computational constraints and task demands. The provided theory and model integrates a framework of lexical processing as active perception (Norris, 2006) with oculomotor constraints derived from a broad-coverage model of eye movement control in reading (Reichle, Warren & McConnell 2009). The first portion of the thesis provides experimental evidence of adaptation of fixation durations to quantitatively-expressed payoffs in a simple reading task, and adaptation in the model on the same dimension. The second portion explores spillover lexical frequency effects in the same framework and how they may emerge from a model that can adaptively allocate processing resources to information drawn from perception (foveal or parafoveal), or memory. In addition to implications for eye movement control in reading, these findings can be interpreted to bear on task adaptation in reading, as well as the adaptive use of perception and memory in a sequential sampling framework.PhDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110380/1/mshvarts_1.pd

Vector Semantics

This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have ‘linguistics’ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Vector Semantics

This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have ‘linguistics’ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges