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https://doi.org/10.1007/354063...
Part of book or chapter of book . 1997 . Peer-reviewed
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DBLP
Conference object . 2025
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Part-of-speech tagging using Progol

Authors: James Cussens;

Part-of-speech tagging using Progol

Abstract

A system for ‘tagging’ words with their part-of-speech (POS) tags is constructed. The system has two components: a lexicon containing the set of possible POS tags for a given word, and rules which use a word's context to eliminate possible tags for a word. The Inductive Logic Programming (ILP) system Progol is used to induce these rules in the form of definite clauses. The final theory contained 885 clauses. For background knowledge, Progol uses a simple grammar, where the tags are terminals and predicates such as nounp (noun phrase) are non-terminals. Progol was altered to allow the caching of information about clauses generated during the induction process which greatly increased efficiency. The system achieved a per-word accuracy of 96.4% on known words drawn from sentences without quotation marks. This is on a par with other tagging systems induced from the same data [5, 2, 4] which all have accuracies in the range 96–97%. The per-sentence accuracy was 4 49.5%.

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United Kingdom
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Keywords

004, 400

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
37
Average
Top 1%
Top 10%
Green
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