LTTagger Sample Clauses

LTTagger. This component does shallow syntactic analysis, by assigning a single part of speech to each token in the sentence, this translation. way enabling the system to perform preprocessing for machine Language Resources The main resources are tagging rules: Rules consist of condition–action patterns, conditions being configurations of linguistic categories and actions performing some disambiguation operations. Modus Operandi After initial preprocessing and pruning of ‘impossible’ readings, the tool performs from left to right in a cyclic way, applying disambiguation rules to the input until all ambiguous constructions are disambiguated, or no rules can fire any more. A final cleanup step takes care of remaining constructions. The tagger is still under construction. WSDL will be: xxxx://00.000.000.000:0000/xxxxxxxX0/xxxxxxxx/XXXxxxxx?xx dl Table 17 WS Details for LT Tagger
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LTTagger. This component does shallow syntactic analysis, by assigning a single part of speech to each token in the sentence, using the Standard tagset of about 90 tags. The tagger operates on a sentence lattice, containing a list of tokens, each token being a list of readings. The task of the tagger is to reduce the lattice to a list, i.e. remove readings which are incorrect in the given context, and keep just one reading per token. Removing readings is done by rules; the tagger is neither probabilistic (TreeTagger, TnT) nor transformational (Xxxxx). The main language resources used are tagging rules. They consist of condition–action patterns, conditions being configurations of tags, and actions performing disambiguation operations (mainly: KEEP this tag and remove all others, or REMOVE this tag and keep the others). The current version supports German only, and has about 400 tagging rules. Modus Operandi: After initial preprocessing and pruning of ‘impossible’ readings (mainly for missing multiword parts), the tagger analyses the sentence lattice from left to right, firing all disambiguation rules starting at the current node; these rules simplify the sentence lattice to the right. Remaining ambiguities are resolved using the local probabilities of the parts-of-speech involved. A final cleanup step builds proper lemmata (e.g. for German split verbs). Although tests are still ongoing it seems that the current version matches probabilistic taggers for German (TreeTagger, TnT) in quality (~96% accuracy). xxxx://00.000.000.000:0000/xxxxxxxX0/xxxxxxxx/XXXxxxxx?xxxx WSDL will be: Table 23 WS Details for LT Tagger
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