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Posted to commits@stanbol.apache.org by "Rupert Westenthaler (JIRA)" <ji...@apache.org> on 2012/09/10 11:42:08 UTC
[jira] [Created] (STANBOL-734) ContentPart for NLP data -
AnalyzedText
Rupert Westenthaler created STANBOL-734:
-------------------------------------------
Summary: ContentPart for NLP data - AnalyzedText
Key: STANBOL-734
URL: https://issues.apache.org/jira/browse/STANBOL-734
Project: Stanbol
Issue Type: Sub-task
Reporter: Rupert Westenthaler
Assignee: Rupert Westenthaler
Because the management of NLP metadata - that is usually available on a word granularity - is not feasible using the RDF metadata this describes the addition of a special ContentPart Stanbol. This ContentPart will have the name AnalysedText.
AnalysedText
=====
* It wraps the text/plain ContentPart of a ContentItem
* It allows the definition of Spans (type, start, end, spanText). Type
is an Enum: Text, TextSection, Sentence, Chunk, Span
* Spans are sorted naturally by type, start and end. This allows to
use a NavigateableSet (e.g. TreeSet) and the #subSet() functionality
to work with contained Tokens. The #higher and #lower methods of
NavigateableSet even allow to build Iterators that allow concurrent
modifications (e.g adding Chunks while iterating over the Tokens of a
Sentence).
* One can attach Annotations to Spans. Basically a multi-valued Map
with Object keys and Value<valueType> value(s) that support a type
save view by using generically typed Annotation<key,valueType>
* The Value<valueType> object natively supports confidence. This
allows (e.g. for POS tags) to use the same instance ( e.g. of the POS
tag for Noun) to be used for all noun annotations.
* Note that the AnalysedText does NOT use RDF as representing those
kind of data as RDF is not scaleable enough. This also means that the
data of the AnalysedText are NOT available in the Enhancement Metadata
of the ContentItem. However EnhancementEngines are free to write
all/some results to the AnalysedText AND the RDF metadata of the
ContentItem.
Here is a sample code
AnalysedText at; //the contentPart
Iterator<Sentence> sentences = at.getSentences;
while(sentences.hasNext){
Sentence sentence = sentences.next();
String sentText = sentence.getSpan();
Iterator<SentenceToken> tokens = sentence.getTokens();
while(tokens.hasNext()){
Token token = tokens.next();
String tokenText = token.getSpan();
Value<PosTag> pos = token.getAnnotation(
NlpAnnotations.posAnnotation);
String tag = pos.value().getTag();
double confidence = pos.probability();
}
}
NLP annotations
=====
* TagSet and Tag<tagType>: A TagSet can be used for 1..n languages and
contains Tags of a specific generic type. The Tag only defines a
String "tag" property
* Currently Tags for POS (PosTag) and Chunking (PhraseTag) are
defined. Both define also an optional LexicalCategory. This is a enum
with the 12 top level concepts defined by the
[Olia](http://nlp2rdf.lod2.eu/olia/) ontology (e.g. Noun, Verb,
Adjective, Adposition, Adverb ...)
* TagSets (including mapped LexicalCategories) are defined for all
languages where POS taggers are available for OpenNLP. This includes
also the "penn.owl", "stts.owl" and "parole_es_cat.owl" provided by
OLIA. The other TagSets used by OpenNLP are currently not available by
Olia.
* Note that the LexicalCategory can be used to process POS annotations
of different languages
TagSet:
https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/TagSet.java
POS:
https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/pos
A code sample:
TagSet<PosTag> tagSet; //the used TagSet
Map<String,PosTag> unknown; //missing tags in the TagSet
Token token; //the token
String tag; //the detected tag
double prob; //the probability
PosTag pos = tagset.getTag(tag);
if(pos == null){ //unkonw tag
pos = unknown.get(tag);
}
if(pos == null) {
pos = new PosTag(tag);
//this tag will not have a LexicalCategory
unknown.add(pos); //only one instance
}
token.addAnnotation(
NlpAnnotations.POSAnnotation,
new Value<PosTag>(pos, prob));
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[jira] [Resolved] (STANBOL-734) ContentPart for NLP data -
AnalyzedText
Posted by "Rupert Westenthaler (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/STANBOL-734?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Rupert Westenthaler resolved STANBOL-734.
-----------------------------------------
Resolution: Fixed
considered to be implemented with http://svn.apache.org/viewvc?rev=1412121&view=rev. Further changes adaptions should be implemented in own (more focused) issues
> ContentPart for NLP data - AnalyzedText
> ---------------------------------------
>
> Key: STANBOL-734
> URL: https://issues.apache.org/jira/browse/STANBOL-734
> Project: Stanbol
> Issue Type: Sub-task
> Reporter: Rupert Westenthaler
> Assignee: Rupert Westenthaler
>
> Because the management of NLP metadata - that is usually available on a word granularity - is not feasible using the RDF metadata this describes the addition of a special ContentPart Stanbol. This ContentPart will have the name AnalysedText.
> AnalysedText
> =====
> * It wraps the text/plain ContentPart of a ContentItem
> * It allows the definition of Spans (type, start, end, spanText). Type
> is an Enum: Text, TextSection, Sentence, Chunk, Span
> * Spans are sorted naturally by type, start and end. This allows to
> use a NavigateableSet (e.g. TreeSet) and the #subSet() functionality
> to work with contained Tokens. The #higher and #lower methods of
> NavigateableSet even allow to build Iterators that allow concurrent
> modifications (e.g adding Chunks while iterating over the Tokens of a
> Sentence).
> * One can attach Annotations to Spans. Basically a multi-valued Map
> with Object keys and Value<valueType> value(s) that support a type
> save view by using generically typed Annotation<key,valueType>
> * The Value<valueType> object natively supports confidence. This
> allows (e.g. for POS tags) to use the same instance ( e.g. of the POS
> tag for Noun) to be used for all noun annotations.
> * Note that the AnalysedText does NOT use RDF as representing those
> kind of data as RDF is not scaleable enough. This also means that the
> data of the AnalysedText are NOT available in the Enhancement Metadata
> of the ContentItem. However EnhancementEngines are free to write
> all/some results to the AnalysedText AND the RDF metadata of the
> ContentItem.
> Here is a sample code
> AnalysedText at; //the contentPart
> Iterator<Sentence> sentences = at.getSentences;
> while(sentences.hasNext){
> Sentence sentence = sentences.next();
> String sentText = sentence.getSpan();
> Iterator<SentenceToken> tokens = sentence.getTokens();
> while(tokens.hasNext()){
> Token token = tokens.next();
> String tokenText = token.getSpan();
> Value<PosTag> pos = token.getAnnotation(
> NlpAnnotations.posAnnotation);
> String tag = pos.value().getTag();
> double confidence = pos.probability();
> }
> }
> NLP annotations
> =====
> * TagSet and Tag<tagType>: A TagSet can be used for 1..n languages and
> contains Tags of a specific generic type. The Tag only defines a
> String "tag" property
> * Currently Tags for POS (PosTag) and Chunking (PhraseTag) are
> defined. Both define also an optional LexicalCategory. This is a enum
> with the 12 top level concepts defined by the
> [Olia](http://nlp2rdf.lod2.eu/olia/) ontology (e.g. Noun, Verb,
> Adjective, Adposition, Adverb ...)
> * TagSets (including mapped LexicalCategories) are defined for all
> languages where POS taggers are available for OpenNLP. This includes
> also the "penn.owl", "stts.owl" and "parole_es_cat.owl" provided by
> OLIA. The other TagSets used by OpenNLP are currently not available by
> Olia.
> * Note that the LexicalCategory can be used to process POS annotations
> of different languages
> TagSet:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/TagSet.java
> POS:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/pos
> A code sample:
> TagSet<PosTag> tagSet; //the used TagSet
> Map<String,PosTag> unknown; //missing tags in the TagSet
> Token token; //the token
> String tag; //the detected tag
> double prob; //the probability
> PosTag pos = tagset.getTag(tag);
> if(pos == null){ //unkonw tag
> pos = unknown.get(tag);
> }
> if(pos == null) {
> pos = new PosTag(tag);
> //this tag will not have a LexicalCategory
> unknown.add(pos); //only one instance
> }
> token.addAnnotation(
> NlpAnnotations.POSAnnotation,
> new Value<PosTag>(pos, prob));
--
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[jira] [Commented] (STANBOL-734) ContentPart for NLP data -
AnalyzedText
Posted by "Rupert Westenthaler (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/STANBOL-734?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13500293#comment-13500293 ]
Rupert Westenthaler commented on STANBOL-734:
---------------------------------------------
Documentation for Analyzed Text
AnalysedText
=====
The AnalysedText is a Java domain model designed to describe NLP processing results. It describes of two major parts:
1. Structure of the Text such as text-sections, sentences, chunks and tokens
2. Annotations for the detected parts of the text.
## AnalysetText as ContentPart
Within the Stanbol Enhancer the AnalysedText is used as [ContentPart](../contentitem#content-parts) registered with the URI <code>urn:stanbol.enhancer:nlp.analysedText</code>
Because of that it can be retrieved by using the following code
:::java
AnalysedText at;
ci.getLock().readLock().lock();
try {
at = ci.getPart(AnalysedText.ANALYSED_TEXT_URI, AnalysedText.class);
} catch (NoSuchPartException e) {
//not present
at = null;
} finally {
ci.getLock().readLock().unlock();
}
Components that need to create an AnalysedText instance can do so by using the _AnalysedTextFactory_
:::java
@Reference
AnalysedTextFactory atf;
ContentItem ci; //the contentItem
AnalysedText at;
Entry<String,Blob> plainTextBlob = ContentItemHelper.getBlob(
ci, Collections.singelton("text/plain"));
if(plainTextBlob != null){
//creates and adds the AnalysedText ContentPart to the ContentItem
ci.getLock().writeLock().lock();
try {
at = atf.createAnalysedText(ci,plainTextBlob.value());
} finally {
ci.getLock().writeLock().unlock();
}
} else { //no NLP processing possible
at = null;
}
If used outside of OSGI users can also use the AnalysedTextFactory#getDefaultInstance() to obtain the AnalysedTextFactory instance of the in-memory implementation.
## Structure of the Text
The basic building block of the AnalysedText is the Span. A Span defines type, [start,end) as well as the spanText. For the type an enumeration (_SpanTypeEnum_) with the members Text, TextSection, Sentence, Chunk and Text. [start,end) define the character positions of the Span within the Text where the start position is inclusive and the end position is exclusive.
Analog to the type of the Span there are also Java interfaces representing those types and providing additional convenience methods. An additional _Section_ interface was introduced as common parent for all types that may have enclosed Spans. The AnalyzedText is the interface representing SpanTypeEnum#Text. The main intension of those Java classes are to have convenience methods that ease the use of the API.
### Uniqueness of Spans
A Span is considered equals to an other Span if [start, end) and type are the same. The natural oder of Spans is defined by
* smaller start index first
* bigger end index first
* higher ordinal number of the SpanTypeEnum first
This order is used by all Iterators returned by the AnalyzedText API
### 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.
### Code Samples:
The following Code Snippet shows some typical usages of the API:
:::java
AnalysedText at; //typically retrieved from the contentPart
Iterator<Sentence> sentences = at.getSentences;
while(sentences.hasNext){
Sentence sentence = sentences.next();
String sentText = sentence.getSpan();
Iterator<SentenceToken> tokens = sentence.getTokens();
while(tokens.hasNext()){
Token token = tokens.next();
String tokenText = token.getSpan();
Value<PosTag> pos = token.getAnnotation(
NlpAnnotations.posAnnotation);
String tag = pos.value().getTag();
double confidence = pos.probability();
}
}
Code that adds new Spans looks like follows
:::java
//Tokenize an Text
Iterator<Sentence> sentences = at.getSentences();
Iterator<? extends Section> sections;
if(sentences.hasNext()){ //sentence Annotations presnet
sections = sentences;
} else { //if no sentences tokenize the text at once
sections = Collections.singelton(at).iterator();
}
//Tokenize the sections
for(Section section : sentenceList){
//assuming the Tokenizer returns tokens as 2dim int array
int[][] tokenSpans = tokenizer.tokenize(section.getSpan());
for(int ti = 0; ti < tokenSpans.length; ti++){
Token token = section.addToken(
tokenSpans[ti][0],tokenSpans[ti][1]);
}
}
For all #add**(start,end) methods in the API the parsed start and end indexes are relative to the parent (the one the #add**(..) method is called). The [start,end) indexes returned by Spans are absolute values. If an #add**(..) method is called for a Span '[start,end):type' that already exists than instead of an new instance the already existing one is returned.
## Annotation Support
Annotation support is provided by two interfaces _Annotated_ and _Annotation_ and the _Value_ class. _Annotated_ provides an API for adding information the the annotated object. Those annotations are represented by key value mappings where Object is used as key and the _Value_ class for values. The _Value_ class provides the generically typed value as well as a double probability in the range [0..1] or -1 if not known. Finally the _Annotation_ class is used to ensure type safety.
The following example shows the intended usage of the API
1. One needs to define the _Annotations_ one would like to use. Annotations are typically defined as public static members of interfaces or classes. The following example uses the definition of the Part of Speech annotation.
:::java
public interface NlpAnnotations {
//an Part of Speech Annotation using a String key
//and the PosTag class as value
Annotation<String,PosTag> POS_ANNOTATION = new Annotation<String,PosTag>(
"stanbol.enhancer.nlp.pos", PosTag.class);
...
}
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.
:::java
PosTag tag = new PosTag("N"); //a simple POS tag
Token token; //The Token we want to add the tag
token.addAnnotations(POS_ANNOTATION,Value.value(tag),0.95);
3. For consuming annotations there are two options. First the possibility to use the _Annotation_ object and second by directly using the key. While the 2nd option is not as nicely to use (as it does not provide type safety) it allows consuming annotations without the need to have the used _Annotation_ in the classpath. The following examples show both options
:::java
Iterator<Token> tokens = sentence.getTokens();
while(tokens.hasNext){
Token token = tokens.next();
//use the POS_ANNOTATION to get the PosTag
PosTag tag = token.getAnnotation(POS_ANNOTATION);
if(tag != null){
log.info("{} has PosTag {}",token,tag.value());
} else {
log.infor("{} has no PosTag",token);
}
//(2) use the key to retrieve values
String key = "urn:test-dummy";
Value<?> value = token.getValue(key);
//the programmer needs to know the type!
if(v.probability() > 0.5){
log.info("{}={}",key,value.value());
}
}
The _Annotated_ interface supports multi valued annotations. For that it defines methods for adding/setting and getting multiple values. Values are sorted first by the probability (unknown probability last) and secondly by the insert order (first in first out). So calling the single value getAnnotation() method on a multi valued field will return the first item (highest probability and first added in case of multiple items with the same/no probabilities)
> ContentPart for NLP data - AnalyzedText
> ---------------------------------------
>
> Key: STANBOL-734
> URL: https://issues.apache.org/jira/browse/STANBOL-734
> Project: Stanbol
> Issue Type: Sub-task
> Reporter: Rupert Westenthaler
> Assignee: Rupert Westenthaler
>
> Because the management of NLP metadata - that is usually available on a word granularity - is not feasible using the RDF metadata this describes the addition of a special ContentPart Stanbol. This ContentPart will have the name AnalysedText.
> AnalysedText
> =====
> * It wraps the text/plain ContentPart of a ContentItem
> * It allows the definition of Spans (type, start, end, spanText). Type
> is an Enum: Text, TextSection, Sentence, Chunk, Span
> * Spans are sorted naturally by type, start and end. This allows to
> use a NavigateableSet (e.g. TreeSet) and the #subSet() functionality
> to work with contained Tokens. The #higher and #lower methods of
> NavigateableSet even allow to build Iterators that allow concurrent
> modifications (e.g adding Chunks while iterating over the Tokens of a
> Sentence).
> * One can attach Annotations to Spans. Basically a multi-valued Map
> with Object keys and Value<valueType> value(s) that support a type
> save view by using generically typed Annotation<key,valueType>
> * The Value<valueType> object natively supports confidence. This
> allows (e.g. for POS tags) to use the same instance ( e.g. of the POS
> tag for Noun) to be used for all noun annotations.
> * Note that the AnalysedText does NOT use RDF as representing those
> kind of data as RDF is not scaleable enough. This also means that the
> data of the AnalysedText are NOT available in the Enhancement Metadata
> of the ContentItem. However EnhancementEngines are free to write
> all/some results to the AnalysedText AND the RDF metadata of the
> ContentItem.
> Here is a sample code
> AnalysedText at; //the contentPart
> Iterator<Sentence> sentences = at.getSentences;
> while(sentences.hasNext){
> Sentence sentence = sentences.next();
> String sentText = sentence.getSpan();
> Iterator<SentenceToken> tokens = sentence.getTokens();
> while(tokens.hasNext()){
> Token token = tokens.next();
> String tokenText = token.getSpan();
> Value<PosTag> pos = token.getAnnotation(
> NlpAnnotations.posAnnotation);
> String tag = pos.value().getTag();
> double confidence = pos.probability();
> }
> }
> NLP annotations
> =====
> * TagSet and Tag<tagType>: A TagSet can be used for 1..n languages and
> contains Tags of a specific generic type. The Tag only defines a
> String "tag" property
> * Currently Tags for POS (PosTag) and Chunking (PhraseTag) are
> defined. Both define also an optional LexicalCategory. This is a enum
> with the 12 top level concepts defined by the
> [Olia](http://nlp2rdf.lod2.eu/olia/) ontology (e.g. Noun, Verb,
> Adjective, Adposition, Adverb ...)
> * TagSets (including mapped LexicalCategories) are defined for all
> languages where POS taggers are available for OpenNLP. This includes
> also the "penn.owl", "stts.owl" and "parole_es_cat.owl" provided by
> OLIA. The other TagSets used by OpenNLP are currently not available by
> Olia.
> * Note that the LexicalCategory can be used to process POS annotations
> of different languages
> TagSet:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/TagSet.java
> POS:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/pos
> A code sample:
> TagSet<PosTag> tagSet; //the used TagSet
> Map<String,PosTag> unknown; //missing tags in the TagSet
> Token token; //the token
> String tag; //the detected tag
> double prob; //the probability
> PosTag pos = tagset.getTag(tag);
> if(pos == null){ //unkonw tag
> pos = unknown.get(tag);
> }
> if(pos == null) {
> pos = new PosTag(tag);
> //this tag will not have a LexicalCategory
> unknown.add(pos); //only one instance
> }
> token.addAnnotation(
> NlpAnnotations.POSAnnotation,
> new Value<PosTag>(pos, prob));
--
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[jira] [Commented] (STANBOL-734) ContentPart for NLP data -
AnalyzedText
Posted by "Rupert Westenthaler (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/STANBOL-734?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13500846#comment-13500846 ]
Rupert Westenthaler commented on STANBOL-734:
---------------------------------------------
Documentation for the in-memory implementation of the AnalyzedText
In-Memory AnalyzedText and Annotation implementation
================
This describes the implementation of the [Analyzed Text](analysedtext) used by default by the Stanbol NLP processing module. This implementation is directly contained within the org.apache.stanbol.enhancer.nlp module.
## AnalyzedTextFactory
The AnalyzedTextFactory of the in-memory implementation registers itself as OSGI service with an "service.ranking" of Integer.MIN_VALUE. That means that any other registered AnalyzedTextFactory will override this one (unless it does not use Integer.MIN_VALUE itself).
The implementation uses the ContentItemHelper#getText(Blob blob) method to retrieve the text from the parsed blob. The text is than used to create an AnalyzedText instance.
## AnalyzedText Implementation
The in-memory implementation is based on a NavigableMap that uses the same span as both key and value. TreeMap is currently used as implementation. The compareTo(..) method of the Span implementation ensures the correct ordering of Spans as specified by the [Analyzed Text](analyzedtext) interface. All add**(..) methods first check if a span with the added type, [start,end) is already contained. If this is the case the current span is returned otherwise an new instance is created.
The Iterator implementation is not based on the Iterators provided by the NavigableMap as those would throw ConcurrentModificationExceptions - what is prohibited by the specification. Instead in implementation that is based on the #higherKey() method is used. Filtered Iterators are implemented using Apache Commons Collections FilteredIterator utility with an Predicate based on the SpanTypeEnum.
## Annotation Implementation
The implementation of the _Annotated_ interface is similar to that of the SolrInputDocument. Internally it uses a Map<Object,Object> to store data. When a single value is added it is directly store in the map. In case of multiple values data are stored in Arrays. Arrays are sorted by an comparator that ensures that the value with the highest probability is at index '0'.
Type safety is not checked so creating multiple Annotations with different value types that share the same key will cause ClassCastExceptions at runtime.
> ContentPart for NLP data - AnalyzedText
> ---------------------------------------
>
> Key: STANBOL-734
> URL: https://issues.apache.org/jira/browse/STANBOL-734
> Project: Stanbol
> Issue Type: Sub-task
> Reporter: Rupert Westenthaler
> Assignee: Rupert Westenthaler
>
> Because the management of NLP metadata - that is usually available on a word granularity - is not feasible using the RDF metadata this describes the addition of a special ContentPart Stanbol. This ContentPart will have the name AnalysedText.
> AnalysedText
> =====
> * It wraps the text/plain ContentPart of a ContentItem
> * It allows the definition of Spans (type, start, end, spanText). Type
> is an Enum: Text, TextSection, Sentence, Chunk, Span
> * Spans are sorted naturally by type, start and end. This allows to
> use a NavigateableSet (e.g. TreeSet) and the #subSet() functionality
> to work with contained Tokens. The #higher and #lower methods of
> NavigateableSet even allow to build Iterators that allow concurrent
> modifications (e.g adding Chunks while iterating over the Tokens of a
> Sentence).
> * One can attach Annotations to Spans. Basically a multi-valued Map
> with Object keys and Value<valueType> value(s) that support a type
> save view by using generically typed Annotation<key,valueType>
> * The Value<valueType> object natively supports confidence. This
> allows (e.g. for POS tags) to use the same instance ( e.g. of the POS
> tag for Noun) to be used for all noun annotations.
> * Note that the AnalysedText does NOT use RDF as representing those
> kind of data as RDF is not scaleable enough. This also means that the
> data of the AnalysedText are NOT available in the Enhancement Metadata
> of the ContentItem. However EnhancementEngines are free to write
> all/some results to the AnalysedText AND the RDF metadata of the
> ContentItem.
> Here is a sample code
> AnalysedText at; //the contentPart
> Iterator<Sentence> sentences = at.getSentences;
> while(sentences.hasNext){
> Sentence sentence = sentences.next();
> String sentText = sentence.getSpan();
> Iterator<SentenceToken> tokens = sentence.getTokens();
> while(tokens.hasNext()){
> Token token = tokens.next();
> String tokenText = token.getSpan();
> Value<PosTag> pos = token.getAnnotation(
> NlpAnnotations.posAnnotation);
> String tag = pos.value().getTag();
> double confidence = pos.probability();
> }
> }
> NLP annotations
> =====
> * TagSet and Tag<tagType>: A TagSet can be used for 1..n languages and
> contains Tags of a specific generic type. The Tag only defines a
> String "tag" property
> * Currently Tags for POS (PosTag) and Chunking (PhraseTag) are
> defined. Both define also an optional LexicalCategory. This is a enum
> with the 12 top level concepts defined by the
> [Olia](http://nlp2rdf.lod2.eu/olia/) ontology (e.g. Noun, Verb,
> Adjective, Adposition, Adverb ...)
> * TagSets (including mapped LexicalCategories) are defined for all
> languages where POS taggers are available for OpenNLP. This includes
> also the "penn.owl", "stts.owl" and "parole_es_cat.owl" provided by
> OLIA. The other TagSets used by OpenNLP are currently not available by
> Olia.
> * Note that the LexicalCategory can be used to process POS annotations
> of different languages
> TagSet:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/TagSet.java
> POS:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/pos
> A code sample:
> TagSet<PosTag> tagSet; //the used TagSet
> Map<String,PosTag> unknown; //missing tags in the TagSet
> Token token; //the token
> String tag; //the detected tag
> double prob; //the probability
> PosTag pos = tagset.getTag(tag);
> if(pos == null){ //unkonw tag
> pos = unknown.get(tag);
> }
> if(pos == null) {
> pos = new PosTag(tag);
> //this tag will not have a LexicalCategory
> unknown.add(pos); //only one instance
> }
> token.addAnnotation(
> NlpAnnotations.POSAnnotation,
> new Value<PosTag>(pos, prob));
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[jira] [Commented] (STANBOL-734) ContentPart for NLP data -
AnalyzedText
Posted by "Rupert Westenthaler (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/STANBOL-734?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13501777#comment-13501777 ]
Rupert Westenthaler commented on STANBOL-734:
---------------------------------------------
Documentation for the NLP Annotations
NLP Annotations
===========
While the The [Analyzed Text](analyzedtext) interface allows to define Sentences, Chunks and Tokens within the text and also to attach annotations to those this part of the Stanbol NLP processing module provides the Java domain model for the annotations section this part of the Stanbol NLP processing module defines the Java domain model used for those annotations. This includes annotation models for Part of Speech (POS) tags, Chunks , recognized Named Entities (NER) as well as morphological analysis.
### Part of Speech (POS) annotations
Part of Speech (POS) tagging represents an token level annotation. It assigns tokens with categories like noun, verb, adjectives, punctuation ... This annotations are typically provided by an POS tagger that consumes Tokens and provides tag(s) with confidence(s) as output. Tags are usually string values that are member of a TagSet - a fixed list of tags used to annotate tokens. Those Tag sets are typically language and often even trainings corpus specific. This makes it really hard to consume POS tags created by different POS tagger for different languages as the consumer would need to know about the meanings of all the different POS tags for the different languages.
The POS annotation model defined by the Stanbol NLP module tries to solve this issue by providing means to align POS tag sets with formal categories defined by the [OLiA Ontology](http://nlp2rdf.lod2.eu/olia/). The following sub-section will provide details and usage examples.
#### OLiA MorphosyntacticCategories
The '[OLiA](http://nlp2rdf.lod2.eu/olia/) Reference Model for Morphology and Morphosyntax, with experimental extension to Syntax' defines a set of ~150 formally defined and multi-lingual POS tags. Those types are defined as a non-cyclic multi-hierarchy with 'oilia:MorphosyntacticCategory' as common root.
To give an example the POS 'olia:Gerund' is defined as a 'olia:NonFiniteVerb' what itself is a 'olia:Verb'. An example for a multi-hierarchy is 'olia:NominalQuantifier' that is both a 'olia:Noun' and a 'olia:Quantifier'.
To allow support a nice integration of the formal definitions by the OLiA ontology within the Stanbol NLP annotations there are two Java enumerations:
* __LexicalCategories__: This enumeration covers the 12 top level categories as defined by OLiA. This includes Noun, Verb, Adjective, Adposition, Adverb, Conjuction, Interjection, PronounOrDeterminer, Punctuation, Quantifier, Residual and Unique.
* __Pos__: This enumeration covers all OLiA MorphosyntacticCategories from the 2+ level. So by using the _Pos_ enum one can e.g. distinguish between ProperNoun's and CommonNoun's or FiniteVerb's and NonFiniteVerb's ... The _Pos_ enumeration has full support for the multi-hierarchy as defined by OLiA. The Pos#categories() methods allows to get the 1st level parents of _Pos_. The Pos#hierarchy() returns all 2+ level parents of a _Pos_ member.
#### PosTag and TagSet
The PosTag represents a POS tag as used by an POS tagger. PosTags do support the following features:
* __tag__ [1..1]::Stirng - This is the string tag as used by the POS tagger.
* __category__ [0..*]::LexicalCategory - The assigned LexicalCategory enumeration members.
* __pos__ [0..*]::Pos - The assigned Pos enumeration members.
An Example for a PosTag representing a 'olia:ProperNoun' looks like follows
:::java
PosTag tag = new PosTag("NP", Pos.ProperNoun);
The first parameter is the String POS tag used by the POS tagger and the second parameter represents the mapping to the OLiA MorphosyntacticCategories for this tag. The next example shows an sofisticated mapping for the "PWAV" (Pronominaladverb) as used by the STTS tag set for the German language
:::java
new PosTag("PWAV", LexicalCategory.Adverb, Pos.RelativePronoun, Pos.InterrogativePronoun);
_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
:::java
//Tagset is generically typed. We need a TagSet for PosTag's
public static final TagSet<PosTag> STTS = new TagSet<PosTag>(
"STTS", "de"); //define a name and the languages it supports
static {
//you can set properties to a TagSet. While supported this
//feature is currently not used by Stanbol
STTS.getProperties().put("olia.annotationModel",
new UriRef("http://purl.org/olia/stts.owl"));
STTS.getProperties().put("olia.linkingModel",
new UriRef("http://purl.org/olia/stts-link.rdf"));
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 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.
:::java
TagSet<PosTag> tagSet; //the used TagSet
//holds PosTags for tags returned by the POS tagger that
//are missing in the TagSet
Map<String,PosTag> adhocTags = new HashMap<String,PosTag>():
List<Span> token = new ArrayList<Span>(64);
Iterator<Section> sentences; //Iterator over the sentences
while(sentences.hasNext()){
Section sentence = sentences.next();
//get the tokens of the current sentence
token.clean();
AnalysedTextUtils.appandToList(
sentence.getEnclosed(SpanTypeEnum.Token),
tokenList);
//typically one needs also to get the Strings
//of the tokens for the pos tagger
String[] tokenText = new String[tokenList.size()];
for(int i=0;i<tokens.size();i++){
tokenText[i] = tokens.get(i).getSpan();
}
//now POS tag the sentence
String[] posTags = posTagger.tag(tokens);
//finally apply the PosTags and save the annotation
for(int i=0;i<tokens.size();i++){
PosTag tag = tagSet.get(posTags[i]);
if(tag == null) { //unmapped tag
tag = adhocTags.get(posTags[i]);
}
if(tag == null) { //unknown tag
tag = new PosTag(posTags[i]);
adhocTags.put(posTags[i],tag);
}
//add the annotation to the Token
token.addAnnotation(
NlpAnnotations.POS_ANNOTATION,
Value.value(tag));
}
}
### Phrase annotations
Phrase annotations can be used to define the type of a _Chunk_. The _PhraseTag_ class is used for phrase annotations. It defines first a string tag and secondly the Phrase category. The _LexicalCategory_ enumeration is used as valued for the category. As the _PhraseTag_ is a subclass of _Tag_ it can be also used in combination with the _TagSet_ class as described in the [PosTag and TagSet] section.
The following code snippets show how to create a PhraseTag for noun phrases
:::java
PhraseTag tag = new PhraseTag("NP", LexicalCategory.Noun);
### Name Entity (NER) annotations
Named Entity annotations are created by NER modules. Before the Stanbol NLP chain they where represented in Stanbol by using '[fise:TextAnnotation](../enhancementstructure#fisetextannotation)'s and any Enhancement Engine that does NER should still support this. With the Stanbol NLP processing module it is now also possible to represent detected Named Entities as _Chunk_ with an PhraseTag added as Annotation.
A Named Entity represented as 'fise:TextAnnotation' includes the following information:
urn:namedEntity:1
rdf:type fise:TextAnnotation, fise:Enhancement
fise:selected-text {named-entity-text}
fise:start {start-char-pos}
fise:end {end-char-pos}
dc:type {named-entity-type}
where:
* {named-entity-text} is the text recognized as Named Entity. This is the same as returned by _Chunk#getSpan()_
* {start-char-pos} is the start character position of the Named Entity relative to the start of the text. This is the same as _Chunk#getStart()_
* {end-char-pos} is the end position and the same as _Chunk#getEnd()_
* {named-enttiy-type} is the type of the recognized Named Entity as URI. The _PhraseTag allows to define both the string tag as used by the NER component as well as the URI this type is mapped to. In Stanbol it is preferred to use 'dbpedia:Person', 'dbpedia:Organisation' and 'dbpedia:Place' for the according entity types.
The _NerTag_ class extends _Tag_ and can therefore be also used with the _TagSet_ class. This means that users of the API can use _TagSet_ to manage the string tag to URI mappings for the supported Named Entity types.
The following Code Snippets shows how to add NER annotations to the AnalysedText:
:::java
AnalysedText at; //The AnalysedText
TagSet<NerTag> nerTags; //registered NER tags
Iterator<Section> sections; //sections to iterate over
List<String> tokenTexts = new ArrayList<Span>(64);
while(sections.hasNext()){
Section section = sections.next();
//NER tagger typically need String[] as input
token.clean();
Iterator<Token> tokens = section.getTokens;
while(tokens.hasNext()){
tokenTexts.add(tokens.next().getSpan());
}
//Span -> #start #end #type #probability
Span[] nerSpans = nerTagger.tag(
tokenTexts.toArray(new String[tokenTexts.size()]);
for(int i=0; i < nerSpans.length; i++){
Chunk namedEntity = at.addChunk(
nerSpans[i].start,nerSpans[i].start);
NerTag tag = nerTags.get(nerSpans[i].type)
if(tag == null){ //unmapped NER
tag = new NerTag(nerSpans[i].type);
}
namedEntity.addAnnotation(
NlpAnnotations.NER_ANNOTATION,
Value.value(tag, nerSpans[i]. probability));
}
}
Note that the above Code Snippet only shows how to add the Named Entity to the AnalyzedText ContentPart. A actual NER engine Implementation needs also to add those information to the metadata of the [ContentItem](../contentitem).
:::java
ContentItem ci; //The processed ContentItem
Language lang; //The Language of the processed Text
MGraph metadata = ci.getMetadata();
Section section; //the current Section
Chunk namedEntity //the currently processed Named Entity
Value<NerTag> nerAnnotation = namedEntity.getAnnotation(
NlpAnnotations.NER_ANNOTATION);
UriRef textAnnotation = EnhancementEngineHelper.createTextEnhancement(ci, this);
metadata.add(new TripleImpl(textAnnotation, ENHANCER_SELECTED_TEXT,
new PlainLiteralImpl(namedEntity.getSpan(), language)));
metadata.add.add(new TripleImpl(textAnnotation, ENHANCER_SELECTION_CONTEXT,
new PlainLiteralImpl(section.getSpan(), language)));
if(tag.getType() != null){
metadata.add(new TripleImpl(textAnnotation, DC_TYPE,
nerAnnotation.value().getType));
} //else do not add an dc:type for unmapped NamedEntities
g.add(new TripleImpl(textAnnotation, ENHANCER_CONFIDENCE,
literalFactory.createTypedLiteral(nerAnnotation.probability())));
g.add(new TripleImpl(textAnnotation, ENHANCER_START,
literalFactory.createTypedLiteral(namedEntity.getStart()));
g.add(new TripleImpl(textAnnotation, ENHANCER_END,
literalFactory.createTypedLiteral(namedEntity.getEnd())));
### Morphological Analyses
__NOTE:__ _This part of the Stanbol NLP annotations is still work in progress. So this part of the API might undergo heavy changes even in minor releases._
The results of a Morphological Analyses are represented by the _MorphoFeatures_ class and can be added to the analyzed word (_Token_) by using the _NlpAnnotations.MORPHO_ANNOTATION_. The _MorphoFeatures_ class provides the following features:
* __Lemma__: A String value representing the lemmatization of the annotated Token.
* __Case__: The _Case_ enumeration contains around 70 members defined based on concepts of the [OLiA Ontology](http://nlp2rdf.lod2.eu/olia/). The _CaseTag_ allows to define cases and optionally map them to the cases defined by the enumeration.
* __Definitness__: The _Definitness_ enumeration has the members Definite and Indefinite also defined by Concepts in the [OLiA Ontology](http://nlp2rdf.lod2.eu/olia/).
* __Gender__: The _Gender_ enumeration contains the six gender defined by the [OLiA Ontology](http://nlp2rdf.lod2.eu/olia/). The _GenderTag_ allows to define Genders and optionally map them to the gender defined by the enumeration.
* __Number__: The _NumberFeature_ enumeration defines the eight number features defined by [OLiA](http://nlp2rdf.lod2.eu/olia/). The _NumberTag_ can be used to define number features and map them to the members of the enumeration
* __Person__: the _Person_ enumeration has the definitions for 'first', 'second' and 'third' with mappings to the according concepts of the [OLiA Ontology](http://nlp2rdf.lod2.eu/olia/).
* __Tense__: The _Tense_ enumeration represents the tense hierarchy as defined by the [OLiA Ontology](http://nlp2rdf.lod2.eu/olia/). the _Tense#getParent()_ allows access to the direct parent of a _Tense_ while the _Tense#getTenses()_ method can be used to obtain the transitive closure (including the _Tens_ object itself). _TenseTag_ is used for Tense annotations. It allows both to parse a string tag representing the tense as well as defining a mapping to the tenses defined by the _Tense_ enumeration.
* __Mood__: The _VerbMood_ enumeration currently defines members from different part of the [OLiA Ontology](http://nlp2rdf.lod2.eu/olia/). While OLiA does define the 'ilia:MoodFeature' class but those members had not a good match with verb moods as used by the CELI/linguagrid.org service. For now the decision was to define the _VerbMood_ enumeration more closely to the usage of CELI, but this needs clearly to be validated as soon as implementations for other NLP frameworks are added. Their is also a _VerbMoodTag_ that allows to define verb moods by a string tag and an mapping to the _VerbMood_ enumeration.
The _MorphoFeatures_ supports multi valued annotations for all the above features. Getter for a single value will always return the first added value.
> ContentPart for NLP data - AnalyzedText
> ---------------------------------------
>
> Key: STANBOL-734
> URL: https://issues.apache.org/jira/browse/STANBOL-734
> Project: Stanbol
> Issue Type: Sub-task
> Reporter: Rupert Westenthaler
> Assignee: Rupert Westenthaler
>
> Because the management of NLP metadata - that is usually available on a word granularity - is not feasible using the RDF metadata this describes the addition of a special ContentPart Stanbol. This ContentPart will have the name AnalysedText.
> AnalysedText
> =====
> * It wraps the text/plain ContentPart of a ContentItem
> * It allows the definition of Spans (type, start, end, spanText). Type
> is an Enum: Text, TextSection, Sentence, Chunk, Span
> * Spans are sorted naturally by type, start and end. This allows to
> use a NavigateableSet (e.g. TreeSet) and the #subSet() functionality
> to work with contained Tokens. The #higher and #lower methods of
> NavigateableSet even allow to build Iterators that allow concurrent
> modifications (e.g adding Chunks while iterating over the Tokens of a
> Sentence).
> * One can attach Annotations to Spans. Basically a multi-valued Map
> with Object keys and Value<valueType> value(s) that support a type
> save view by using generically typed Annotation<key,valueType>
> * The Value<valueType> object natively supports confidence. This
> allows (e.g. for POS tags) to use the same instance ( e.g. of the POS
> tag for Noun) to be used for all noun annotations.
> * Note that the AnalysedText does NOT use RDF as representing those
> kind of data as RDF is not scaleable enough. This also means that the
> data of the AnalysedText are NOT available in the Enhancement Metadata
> of the ContentItem. However EnhancementEngines are free to write
> all/some results to the AnalysedText AND the RDF metadata of the
> ContentItem.
> Here is a sample code
> AnalysedText at; //the contentPart
> Iterator<Sentence> sentences = at.getSentences;
> while(sentences.hasNext){
> Sentence sentence = sentences.next();
> String sentText = sentence.getSpan();
> Iterator<SentenceToken> tokens = sentence.getTokens();
> while(tokens.hasNext()){
> Token token = tokens.next();
> String tokenText = token.getSpan();
> Value<PosTag> pos = token.getAnnotation(
> NlpAnnotations.posAnnotation);
> String tag = pos.value().getTag();
> double confidence = pos.probability();
> }
> }
> NLP annotations
> =====
> * TagSet and Tag<tagType>: A TagSet can be used for 1..n languages and
> contains Tags of a specific generic type. The Tag only defines a
> String "tag" property
> * Currently Tags for POS (PosTag) and Chunking (PhraseTag) are
> defined. Both define also an optional LexicalCategory. This is a enum
> with the 12 top level concepts defined by the
> [Olia](http://nlp2rdf.lod2.eu/olia/) ontology (e.g. Noun, Verb,
> Adjective, Adposition, Adverb ...)
> * TagSets (including mapped LexicalCategories) are defined for all
> languages where POS taggers are available for OpenNLP. This includes
> also the "penn.owl", "stts.owl" and "parole_es_cat.owl" provided by
> OLIA. The other TagSets used by OpenNLP are currently not available by
> Olia.
> * Note that the LexicalCategory can be used to process POS annotations
> of different languages
> TagSet:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/TagSet.java
> POS:
> https://bitbucket.org/srfgkmt/stanbol-nlp/src/b064095a1b56/stanbol-enhancer-nlp/src/main/java/org/apache/stanbol/enhancer/nlp/pos
> A code sample:
> TagSet<PosTag> tagSet; //the used TagSet
> Map<String,PosTag> unknown; //missing tags in the TagSet
> Token token; //the token
> String tag; //the detected tag
> double prob; //the probability
> PosTag pos = tagset.getTag(tag);
> if(pos == null){ //unkonw tag
> pos = unknown.get(tag);
> }
> if(pos == null) {
> pos = new PosTag(tag);
> //this tag will not have a LexicalCategory
> unknown.add(pos); //only one instance
> }
> token.addAnnotation(
> NlpAnnotations.POSAnnotation,
> new Value<PosTag>(pos, prob));
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