Open Access
Memory-based language processing
TLDR
Memory-based language processing as discussed by the authors is based on the idea that the direct re-use of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples.Abstract:
Memory-based language processing--a machine learning and problem solving method for language technology--is based on the idea that the direct re-use of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. This book discusses the theory and practice of memory-based language processing, showing its comparative strengths over alternative methods of language modelling. Language is complex, with few generalizations, many sub-regularities and exceptions, and the advantage of memory-based language processing is that it does not abstract away from this valuable low-frequency information.read more
Citations
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Book
Natural Language Processing with Python
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Journal ArticleDOI
MaltParser: A language-independent system for data-driven dependency parsing
Joakim Nivre,Johan Hall,Jens Nilsson,Atanas Chanev,Gülşen Eryiğit,Sandra Kübler,Svetoslav Marinov,Erwin Marsi +7 more
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Journal ArticleDOI
Probabilistic models of language processing and acquisition
TL;DR: A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with Probabilistic theories of categorization and ambiguity resolution in perception.
References
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Book ChapterDOI
Fast effective rule induction
TL;DR: This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples.
Journal ArticleDOI
Toward memory-based reasoning
Craig Stanfill,David L. Waltz +1 more
TL;DR: The intensive use of memory to recall specific episodes from the past—rather than rules—should be the foundation of machine reasoning.
Posted Content
Text Chunking using Transformation-Based Learning
Lance Ramshaw,Mitchell Marcus +1 more
TL;DR: The authors used transformation-based learning for part-of-speech tagging with fairly high accuracy, achieving recall and precision rates of roughly 92% for base NP chunks and 88% for more complex chunks.
A framework of a mechanical translation between Japanese and English by analogy principle
TL;DR: A model based on a series of human language processing and in particular the use of analogical thinking is defined, based on the ability of analogy finding in human beings.
Book
Beyond grammar : an experience-based theory of language
TL;DR: This work presents a DOP model for tree representations, a formal stochastic language theory, and a model for non-context-free representations for compositional semantic representations.