On machine intelligence (2nd revised ed.)
On machine intelligence (2nd revised ed.)
Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
C4.5: programs for machine learning
C4.5: programs for machine learning
Laddering: technique and tool use in knowledge acquisition
Knowledge Acquisition
Knowledge acquisition from databases
Knowledge acquisition from databases
Data Mining and Knowledge Discovery
Machine Learning
Syntactic Parsing as a Knowledge Acquisition Problem
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
Knowledge Discovery in Databases: Exploiting Knowledge-Level Redescription
EKAW '96 Proceedings of the 9th European Knowledge Acquisition Workshop on Advances in Knowledge Acquisition
Inductive Logic Programming for Natural Language Processing
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Support Vector Learning for Semantic Argument Classification
Machine Learning
Language pattern analysis for automotive natural language speech applications
Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Topics as contextual indicators for word choice in SMS conversations
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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Collections of grammatically annotated texts (corpora), and in particular, iparsed corpora, present a challenge to current methods of analysis. Such corpora are large and highly structured heterogeneous data sources. In this paper we briefly describe the parsed one-million word ICE-GB corpus, and the ICECUP query system. We then consider the application of iknowledge discovery in databases (KDD) to text corpora. Following Cupit and Shadbolt (Proceedings 9th European Knowledge Acquisition Workshop, EKAW '96; Berlin: Springer Verlag, pp. 245–261, 1996), we argue that effective linguistic knowledge discovery must be based on a process of iredescription or, more precisely, iabstraction, based on the research question to be investigated. Abstraction maps relevant elements from the corpus to an abstract model of the research topic. This mapping may be implemented using a grammatical query representation such as ICECUP's iFuzzy Tree Fragments (FTFs). Since this abstractive process must be both experimental and expert-guided, ultimately a workbench is necessary to maintain, evaluate and refine the abstract model. We conclude with a pilot study, employing our approach, into aspects of noun phrase postmodifying clause structure. The data is analysed using the UNIT machine learning algorithm to search for significant interactions between domain variables. We show that our results are commensurable with those published in the linguistics literature, and discuss how the methodology may be improved.