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The new splitter comes with an initial set of patterns that try to emulate the behaviour of the original splitter apart from the situations where the original one was obviously wrong, like not allowing sentences to start with a number. Here is a full list of the parameters used by the RegEx Sentence Splitter: Init-time parameters encoding The character encoding to be used while reading the pattern lists.

The list of tags used is given in Appendix G. The tagger uses a default lexicon and ruleset the result of training on a large corpus taken from the Wall Street Journal. To use these, the default lexicon should be replaced with the appropriate lexicon at load time. The default ruleset should still be used in this case. This is an optional parameter. If user does not provide any value, new annotations are created under the default annotation set.

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It contains rules which act on annotations assigned in earlier phases, in order to produce outputs of annotated entities. The Unknown annotation type is used by the Orthomatcher module see 6. The Orthomatcher module adds identity relations between named entities found by the semantic tagger, in order to perform coreference. The matching rules are only invoked if the names being compared are both of the same type, i.

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Output — matches attributes added to the existing entity annotations. There is also a table of spurious matches, i.

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The list of tables to be used is a load time parameter of the orthomatcher: a default list is set but can be changed as necessary. They generate temporary annotations which are used by the pronominal submodule such temporary annotations are removed later. The main coreference module can operate successfully only if all ANNIE modules were already executed.

The FSM is intended to match the quoted fragments and generate appropriate annotations that will be used later by the pronominal module. These rules then try to identify fragments enclosed by such punctuation. Similar to the quoted speech submodule, it is a JAPE transducer operating with a grammar containing patterns that match the most commonly observed pleonastic it constructs. This uses the result from the execution of the quoted speech and pleonastic it submodules.

The module works according to the following algorithm: Preprocess the current document. This step locates the annotations that the submodule need such as Sentence, Token, Person, etc. Preprocessing The preprocessing task includes the following subtasks: Identifying the sentences in the document being processed. For each sentence a data structure is prepared that contains three lists.

It is possible that the gender information is missing for some entities - for example if only the person family name is observed then the Named Entity transducer will be unable to deduce the gender.

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In such cases the list with the matching entities generated by the OrhtoMatcher is inspected and if some of the orthographic matches contains gender information it is assigned to the entity being processed. Pronoun Resolution This task includes the following subtasks: Retrieving all the pronouns in the document. If it is, then no further attempt for resolution is made. The proper context is determined.

The context size is expressed in the number of sentences it will contain. Smothered with melted American style Cheddar cheese. This one should come with a warning sign! Topped off with a whole grilled Scotch Bonnet. Inspired by the King.

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