On the Road to High-Quality POS-Tagging
Identifieur interne : 000971 ( Main/Exploration ); précédent : 000970; suivant : 000972On the Road to High-Quality POS-Tagging
Auteurs : Stefan Klatt [Autriche] ; Karel Oliva [Autriche]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
English descriptors
- Teeft :
- Analysis systems, Annotation, Better precision rate, Better results, Central bank, Computational linguistics, Corpus, Corpus frequency, Correct assignment, Correct reading, Correct readings, Corrua readings, Current tokenizers, Decision window, Entire article, Entity recognizer, Error rate, Error rates, External sector, Foreign material, Foreign words, Inflationary expectations, Input problems, Klatt, Length tokens, Lexical, Lexical analysis, Linguistic point, Linguistic rule, Linguistic rules, Linguistic tagger, Linguistic taggers, Many errors, Module, Module uwrb, Modules uwcb, Modules uwrb, More readings, Morphological analysis, Multiword units, Negra, Negra corpus, Next section, Nite verb, Noun, Oliva, Original sense, Other words, Output problems, Parameterizable threshold, Pipeline architecture, Precision rate, Processing architecture, Proper nouns, Quotation marks, Relative pronoun, Second case, Single quotation mark, Statistical tagger, Statistical taggers, Such tokens, Tagger, Test corpus, Text material, Tokenization, Training material, Ungrammatical readings, Unknown tokens, Unknown word, Unknown words, Uwrb, Verb reading, Whole sentential context, Wieder einmal, Word vergessen.
Abstract
Abstract: In this paper, we present techniques aimed at avoiding typical errors of state-of-the-art POS-taggers and at constructing high-quality POS-taggers with extremely low error rates. Such taggers are very helpful, if not even necessary, for many NLP applications organized in a pipeline architecture. The appropriateness of the suggested solutions is demonstrated in several experiments. Although these experiments were performed only with German data, the proposed modular architecture is applicable for many other languages, too.
Url:
DOI: 10.1007/11551263_31
Affiliations:
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taggers</term><term>Many errors</term><term>Module</term><term>Module uwrb</term><term>Modules uwcb</term><term>Modules uwrb</term><term>More readings</term><term>Morphological analysis</term><term>Multiword units</term><term>Negra</term><term>Negra corpus</term><term>Next section</term><term>Nite verb</term><term>Noun</term><term>Oliva</term><term>Original sense</term><term>Other words</term><term>Output problems</term><term>Parameterizable threshold</term><term>Pipeline architecture</term><term>Precision rate</term><term>Processing architecture</term><term>Proper nouns</term><term>Quotation marks</term><term>Relative pronoun</term><term>Second case</term><term>Single quotation mark</term><term>Statistical tagger</term><term>Statistical taggers</term><term>Such tokens</term><term>Tagger</term><term>Test corpus</term><term>Text material</term><term>Tokenization</term><term>Training material</term><term>Ungrammatical readings</term><term>Unknown tokens</term><term>Unknown word</term><term>Unknown words</term><term>Uwrb</term><term>Verb reading</term><term>Whole sentential context</term><term>Wieder einmal</term><term>Word vergessen</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: In this paper, we present techniques aimed at avoiding typical errors of state-of-the-art POS-taggers and at constructing high-quality POS-taggers with extremely low error rates. Such taggers are very helpful, if not even necessary, for many NLP applications organized in a pipeline architecture. The appropriateness of the suggested solutions is demonstrated in several experiments. Although these experiments were performed only with German data, the proposed modular architecture is applicable for many other languages, too.</div></front></TEI><affiliations><list><country><li>Autriche</li></country><region><li>Vienne (Autriche)</li></region><settlement><li>Vienne (Autriche)</li></settlement></list><tree><country name="Autriche"><region name="Vienne (Autriche)"><name sortKey="Klatt, Stefan" sort="Klatt, Stefan" uniqKey="Klatt S" first="Stefan" last="Klatt">Stefan Klatt</name></region><name sortKey="Klatt, Stefan" sort="Klatt, Stefan" uniqKey="Klatt S" first="Stefan" last="Klatt">Stefan Klatt</name><name sortKey="Oliva, Karel" sort="Oliva, Karel" uniqKey="Oliva K" first="Karel" last="Oliva">Karel Oliva</name><name sortKey="Oliva, Karel" sort="Oliva, Karel" uniqKey="Oliva K" first="Karel" last="Oliva">Karel Oliva</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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