Author: rwesten
Date: Fri Nov 23 13:21:32 2012
New Revision: 1412877
URL: http://svn.apache.org/viewvc?rev=1412877&view=rev
Log:
STANBOL-733 - minor formatting related changes
Modified:
stanbol/site/trunk/content/docs/trunk/components/enhancer/engines/entitylinking.mdtext
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/analyzedtext.mdtext
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/nlpannotations.mdtext
Modified:
stanbol/site/trunk/content/docs/trunk/components/enhancer/engines/entitylinking.mdtext
URL:
http://svn.apache.org/viewvc/stanbol/site/trunk/content/docs/trunk/components/enhancer/engines/entitylinking.mdtext?rev=1412877&r1=1412876&r2=1412877&view=diff
==============================================================================
---
stanbol/site/trunk/content/docs/trunk/components/enhancer/engines/entitylinking.mdtext
(original)
+++
stanbol/site/trunk/content/docs/trunk/components/enhancer/engines/entitylinking.mdtext
Fri Nov 23 13:21:32 2012
@@ -102,7 +102,7 @@ When activating "Proper Noun Linking" us
1. the POS tagging for given languages do support _Pos#ProperNoun_. If this is
not the case for some languages than language specific configurations need to
be used to manually adjust configurations for such languages. The next section
provides examples for that.
2. the Entities in the Vocabulary linked against need typically be mentioned
as Proper Nouns in the Text. Users that need to link Vocabularies with Entities
that use common nouns as their labels (e.g. House, Mountain, Summer, ...) can
typically not use "Proper Noun Linking" with the following exceptions:
- * Entities with labels comprised of multiple common nouns (e.g. White
House) can be detected in cases where _Chunk_s are supported and the _Link
Multiple Matchable Tokens in Phrases_ option is enabled (see the next
sub-section for details).
+ * Entities with labels comprised of multiple common nouns (e.g. White
House) can be detected in cases where _Chunks_ are supported and the _Link
Multiple Matchable Tokens in Phrases_ option is enabled (see the next
sub-section for details).
* In case Entities mentioned in the text are written as upper case tokens
that the _Upper Case Token Mode_ can be set to "LINK" (see the next sub-section
for details)
If suitable it is strongly recommended to activate "Proper Noun Linking" as it
highly increases the performance because in typical text only around 1/10 of
the Nouns are marked as Proper Nouns and therefore the amount of vocabulary
lookups also decreases by this amount.
@@ -196,7 +196,7 @@ The following properties define how Link
* __Default Matching Language__
_(enhancer.engines.linking.defaultMatchingLanguage)_: Linking is always done in
the language of the processed text and in the _Default Matching Language_. By
default the default language are labels without an language tag, but this
parameter allows to override this to a specific language. This is e.g. useful
for [DBpedia](http://dbpedia.org) where all labels are marked with the language
of the source Wikipedia data. So it makes sense to configure the default
matching language to this value.
* __Max Search Token Distance__
_(enhancer.engines.linking.maxSearchTokenDistance)_: The maximum number of
Tokens searched around a linked token to search for additional matchable tokens
to be included for searches for Entities. The default value is '3'. As an
Example in the text section "at the University of Munich a new procedure to"
only "Munich" would be marked as linkable token if _Proper Noun Linking_ is
activated. However for searching Entities it makes sense to also use the
matchable term 'University', because otherwise a search would potentially
return an huge number of candidates of Entities mentioning 'Munich' in their
labels. This parameter allows to configure the maximum distance of tokens so
that the EntityLinkingEngine may include them as additional optional
constraints for queries via the EntitySearcher interface. _NOTE_ that this
parameter will not allow to include tokens outside of a _processable chunk_ if
the _linked token_ is within an such.
* __Max Search Tokens__ _(enhancer.engines.linking.maxSearchTokens)_: The
maximum number of Tokens used for searches via the _EntitySearcher_ interface.
The default value is '2'. In case more _matchable tokens_ are within the
configured _Max Search Token Distance_ than those closer & trailing the
_linkable token_ are preferred. E.g. the text "president Barack Obama" where
'Barack' is the currently active _linkable token_ will result in a query with
the tokens 'Barack' OR 'Obama' if _Max Search Tokens_=2 and _Max Search Token
Distance_>=1 because both 'president' and 'Obama' do have a distance of 1 but
trailing Tokens are preferred.
-* __Lemma based Matching__ _(enhancer.engines.linking.lemmaMatching)_: If this
feature in enabled than the _MorphoFeatures#getLemma()_ values are used instead
of the _Token#getSpan()_s if present.
+* __Lemma based Matching__ _(enhancer.engines.linking.lemmaMatching)_: If this
feature in enabled than the _MorphoFeatures#getLemma()_ values are used instead
of the _Token#getSpan()s_ if present.
* __Min Search Token Length__
_(enhancer.engines.linking.minSearchTokenLength)_: This is used as fallback if
the _Tokens_ in the _[AnalyzedText](../nlp/analyzedtext)_ do not contain Part
of Speech annotations or if the confidence of those annotations is to low. The
default value is '3' meaning that in such cases all tokens with more than '3'
characters are linked with the vocabulary. _NOTE_ that this configuration might
move to the _Text Processing Configuration_ in future versions.
The parameters below are used to configure the matching process.
@@ -263,7 +263,7 @@ This method is called with the 'id' of a
* __Entity Search__ _lookup(String field, Set<String> includeFields,
List<String> search, String[] languages,Integer
limit)::Collection<Representation>_
-This method is used for searching entities in the controlled vocabulary. The
configured _Label Field_ is parsed in the 'field' parameter. The
'includedFileds' contain all fields required for the linking process.
_Representation_s returned as result need to include values for those fields.
The 'search' parameter includes the tokens used for the search. Values should
be considered optional however Results are considered to rank Entities that
match more search tokens first. The array of 'languages' is used to parse the
languages that need to be considered for the search. If 'languages' contains
NULL or '' it means that also labels without an language tag need to be
included in the search (NOTE that this DOES NOT mean to include labels of any
language!). Finally the 'limit' parameter is used to specify the maximum number
of results. If NULL than the implementation can choose an meaningful default.
+This method is used for searching entities in the controlled vocabulary. The
configured _Label Field_ is parsed in the 'field' parameter. The
'includedFileds' contain all fields required for the linking process.
_Representations_ returned as result need to include values for those fields.
The 'search' parameter includes the tokens used for the search. Values should
be considered optional however Results are considered to rank Entities that
match more search tokens first. The array of 'languages' is used to parse the
languages that need to be considered for the search. If 'languages' contains
NULL or '' it means that also labels without an language tag need to be
included in the search (NOTE that this DOES NOT mean to include labels of any
language!). Finally the 'limit' parameter is used to specify the maximum number
of results. If NULL than the implementation can choose an meaningful default.
* __Offline Mode__ _supportsOfflineMode()::boolean_ : indicates if the
EntitySearcher implementation needs to connect an remote service. This is
needed to deactivate the EntityLinkingEngine in cases where Apache Stanbol is
started in OfflineMode
* __Serach Result Limit__ _getLimit()::Integer_ : The maximum number of search
results supported by the EntitySearcher implementation. Can return NULL if not
applicable or unknown.
@@ -281,9 +281,9 @@ The _LabelTokenizer_ interface defines o
#### MainLabelTokenizer
-As it might very likely be the case that users will want to use multiple
LabelTokenizer for different languages the EntityLinkingEngine comes with an
MainLabelTokenizer implementation. It registers itself as LabelTokenizer with
highest possible OSGI 'service.ranking' and tracks all other registered
_LabelTokenizer_s.
+As it might very likely be the case that users will want to use multiple
LabelTokenizer for different languages the EntityLinkingEngine comes with an
MainLabelTokenizer implementation. It registers itself as LabelTokenizer with
highest possible OSGI 'service.ranking' and tracks all other registered
_LabelTokenizers_.
-So if custom _LabelTokenizer_s register themselves as OSGI service than the
MainLabelTokenizer can forward requests to them. It will do so in the order of
the '<code>service.ranking</code>'s. in addition _LabelTokenizer_ can use the
'<code>enhancer.engines.keywordextraction.labeltokenizer.languages</code>'
property to formally specify the languages they are supporting. This property
does use the language configuration syntax (e.g. "en,de" would include English
and German; "!it,!fr,*" would specify all languages expect Italian and French).
If no configuration is provided than "*" (all languages) is assumed - what is
fine as default as long as _LabelTokenizer_ correctly return NULL for languages
they do not support.
+So if custom _LabelTokenizers_ register themselves as OSGI service than the
MainLabelTokenizer can forward requests to them. It will do so in the order of
the '<code>service.ranking</code>'s. in addition _LabelTokenizer_ can use the
'<code>enhancer.engines.keywordextraction.labeltokenizer.languages</code>'
property to formally specify the languages they are supporting. This property
does use the language configuration syntax (e.g. "en,de" would include English
and German; "!it,!fr,*" would specify all languages expect Italian and French).
If no configuration is provided than "*" (all languages) is assumed - what is
fine as default as long as _LabelTokenizer_ correctly return NULL for languages
they do not support.
The MainLabelTokenizer forwards tokenize requests to all available
LabelTokenizer implementations that support a specific language sorted by their
'<code>service.ranking</code>' until the first one does NOT return NULL. If no
LabelTokenizer was found or all returned NULL it will also return NULL.
@@ -306,7 +306,7 @@ This will inject the MainLabelTokenizer
textProcessingConfig, linkerConfig, //config
labelTokenizer); //the MainLabelTokenizer
-Configuring the NamedEntityLinkingEngine like this ensures that all registered
_LabelTokenizer_s are considered for tokenizing.
+Configuring the NamedEntityLinkingEngine like this ensures that all registered
_LabelTokenizers_ are considered for tokenizing.s_
#### OpenNLP LabelTokenizer
Modified:
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/analyzedtext.mdtext
URL:
http://svn.apache.org/viewvc/stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/analyzedtext.mdtext?rev=1412877&r1=1412876&r2=1412877&view=diff
==============================================================================
---
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/analyzedtext.mdtext
(original)
+++
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/analyzedtext.mdtext
Fri Nov 23 13:21:32 2012
@@ -67,7 +67,7 @@ This order is used by all Iterators retu
### Concurrent Modifications and Iterators
-Iterators returned by the AnalyzedText API MUST throw
_ConcurrentModificationException_s but rather reflect changes to the
underlaying model. While this is not constant with the default behavior of
Iterators in Java this is central for the effective usage of the AnalyzedText
API - e.g. when Iterating over Sentences while adding Tokens.
+Iterators returned by the AnalyzedText API MUST throw
_ConcurrentModificationExceptions_ but rather reflect changes to the
underlaying model. While this is not constant with the default behavior of
Iterators in Java this is central for the effective usage of the AnalyzedText
API - e.g. when Iterating over Sentences while adding Tokens.
### Code Samples:
@@ -131,7 +131,7 @@ The following example shows the intended
...
}
-2. Defined _Annotation_ are used to add information to an _Annotated_ instance
(like a Span). For adding annotations the use of _Annotation_s is required to
ensure type safety. The following code snippet shows how to add an PosTag with
the probability 0.95.
+2. Defined _Annotation_ are used to add information to an _Annotated_ instance
(like a Span). For adding annotations the use of _Annotations_ is required to
ensure type safety. The following code snippet shows how to add an PosTag with
the probability 0.95.
:::java
PosTag tag = new PosTag("N"); //a simple POS tag
Modified:
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/nlpannotations.mdtext
URL:
http://svn.apache.org/viewvc/stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/nlpannotations.mdtext?rev=1412877&r1=1412876&r2=1412877&view=diff
==============================================================================
---
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/nlpannotations.mdtext
(original)
+++
stanbol/site/trunk/content/docs/trunk/components/enhancer/nlp/nlpannotations.mdtext
Fri Nov 23 13:21:32 2012
@@ -39,7 +39,7 @@ The first parameter is the String POS ta
_TagSet_ is the other important class as it allows to manage the set of PosTag
instances. _TagSet_ has two main functions: First it allows an integrator of an
POS tagger with Stanbol to define the mappings from the string POS tags used by
the Pos Tagger to the LexicalCategory and Pos enumeration members as preferable
used by the Stanbol NLP chain. Second it ensures that there is only a single
instance of PosTag used to annotate all Tokens with the same type.
-_TagSet_s are typically specified as static members of utility classes. The
following code snippet shows an example
+TagSets are typically specified as static members of utility classes. The
following code snippet shows an example
:::java
//Tagset is generically typed. We need a TagSet for PosTag's
@@ -56,10 +56,10 @@ _TagSet_s are typically specified as sta
STTS.addTag(new PosTag("ADJA", Pos.AttributiveAdjective));
STTS.addTag(new PosTag("ADJD", Pos.PredicativeAdjective));
STTS.addTag(new PosTag("ADV", LexicalCategory.Adverb));
-//[...]
+ //[...]
}
-The string tag (first parameter) of the _PosTag_ is used as unique key by the
_TagSet_. Adding an 2nd _PasTag_ with the same tag will override the first one.
_PosTag_s that are added to a _TagSet_ have the _Tag#getAnnotationModel()_
property set to that model.
+The string tag (first parameter) of the _PosTag_ is used as unique key by the
_TagSet_. Adding an 2nd _PasTag_ with the same tag will override the first one.
_PosTags_ that are added to a _TagSet_ have the _Tag#getAnnotationModel()_
property set to that model.
The final example shows a code snippet shows the core part of an POS tagging
engine using the both the [AnalyzedText](analyzedtext) and the _PosTag_ and
_TagSet_ APIs.
