Loading a document¶
Any script that uses FoLiA starts with the import:
import folia.main as folia
At the basis of any FoLiA processing lies the following class:
||This is the FoLiA Document and holds all its data in memory.|
To read a document from file, instantiate a document as follows:
doc = folia.Document(file="/path/to/document.xml")
Document instance holds the entire document in
memory. Note that for large FoLiA documents this may consume quite some memory!
If you happened to already have the document content in a string, you can load
doc = folia.Document(string="<FoLiA ...")
Once you have loaded a document, all data is available for you to read and manipulate as you see fit. We will first illustrate some simple use cases:
To save a document back to the file it was loaded from, we do:
Or we can specify a specific filename:
Any content that is in a different XML namespace than the FoLiA namespaces or other supported namespaces (XML, Xlink), will be ignored upon loading and lost when saving.
You may want to simply print all (plain) text contained in the document, which is as easy as:
Obtaining the text as a string is done by invoking the document’s
text = doc.text()
Or alternatively as follows:
text = str(doc)
For any subelement of the document, you can obtain its text in the same fashion
as well, by calling its
AbstractElement.text() method or by using
str(), the only difference is that the former allows for extensive fine
tuning using various extra parameters (See
A document instance has an index which you can use to grab any of its elements by ID. Querying using the index proceeds similar to using a python dictionary:
word = doc['example.p.3.s.5.w.1'] print(word)
IDs are unique in the entire document, and preferably even beyond.
All FoLiA elements are derived from
AbstractElement and offer an
identical interface. To quickly check whether you are dealing with a FoLiA
element you can therefore always do the following:
This abstract base element is never instantiated directly. The FoLiA paradigm derives several more abstract base classes which may implement some additional methods or overload some of the original ones:
||Abstract base class from which all FoLiA elements are derived.|
||Abstract element, all structure elements inherit from this class.|
||Abstract element, all span annotation elements are derived from this class|
||Annotation layers for Span Annotation are derived from this abstract base class|
||Abstract class for text markup elements, elements that appear with the
Obtaining list of elements¶
The aforementioned index is useful only if you know the ID of the element. This if often not the case, and you will want to iterate through the hierarchy of elements through different means.
If you want to iterate over all of the child elements of a certain element, regardless of what type they are, you can simply do so as follows:
for subelement in element: if isinstance(subelement, folia.Sentence): print("this is a sentence") else: print("this is something else")
If applied recursively this allows you to traverse the entire element tree, there are however specialised methods available that do this for you.
There is a generic method
AbstractElement.select() available on all
elements to select child elements of any desired class. This method is by
default applied recursively for most element types:
sentence = doc['example.p.3.s.5.w.1'] words = sentence.select(folia.Word) for word in words: print(word)
word = sentence.count(folia.Word)
select() method and similar high-level methods derived from it, are
generators. This implies that the results of the selection are returned one by
one in the iteration, as opposed to all stored in memory. This also implies
that you can only iterate over it once, we can not do another iteration over
words variable in the above example, unless we reinvoke the
select() method to get a new generator. Likewise, we can not do
len(words), but have to use the
count() method instead.
If you want to have all results in memory in a list, you can simply do the following:
words = list(sentence.select(folia.Word))
The select method is by default recursive, set the third argument to
make it non-recursive. The second argument can be used for restricting matches
to a specific set, a tuple of classes. The recursion will not go into any
non-authoritative elements such as alternatives, originals of corrections.
There are various shortcut methods for
for word in doc.words(): print(word)
That however gives you one big iteration of words without boundaries. You may
more likely want to seek words within sentences, provided the document
distinguishes sentences. So we first iterate over all sentences using
Document.sentences() and then over the
words therein using
for sentence in doc.sentences(): for word in sentence.words(): print(word)
Or including paragraphs, assuming the document has them:
for paragraph in doc.paragraphs(): for sentence in paragraph.sentences(): for word in sentence.words(): print(word)
Do be aware that such constructions make presumptions about the structure of the FoLiA document that may not always apply!
All of these shortcut methods also take an
index parameter to quickly
select a specific item in the sequence:
word = sentence.words(3) #retrieves the fourth word
Structure Annotation Types¶
The FoLiA library discerns various Python classes for structure
annotation, all are subclasses of
AbstractStructureElement, which in
turn is a subclass of
AbstractElement. We list the classes
for structure anntoation along with the FoLiA XML tag. Sets and classes can
be associated with most of these elements to make them more specific, these are
never prescribed by FoLiA. The list of classes is as follows:
||A cell in a
||Element used in
||Structure element representing some kind of division.|
||Represents an entry in a glossary/lexicon/dictionary.|
||Structural element representing events, often used in new media contexts for things such as tweets,chat messages and forum posts.|
||Element that provides an example.|
||Element for the representation of a graphical figure.|
||Gap element, represents skipped portions of the text.|
||Head element; a structure element that acts as the header/title of a
||Line break element, signals a line break.|
||Element for enumeration/itemisation.|
||Single element in a List.|
||Element used for notes, such as footnotes or warnings or notice blocks.|
||Generic structure element used to mark a part inside another block.|
||Quote: a structure element.|
||A structural element that denotes a reference, internal or external.|
||A row in a
||A table consisting of
||A term, often used in contect of
||Encapsulated the header of a table, contains
||A full text.|
||Whitespace element, signals a vertical whitespace|
||Word (aka token) element.|
The FoLiA documentation explains the exact semantics and use of these in detail. Make sure to consult it to familiarize yourself with how the elements should be used.
FoLiA and this library enforce explicit rules about what elements are allowed in what others. Exceptions will be raised when this is about to be violated.
The FoLiA paradigm features sets and classes as primary means to represent the actual value (class) of an annotation. A set often corresponds to a tagset, such as a set of part-of-speech tags, and a class is one selected value in such a set.
The paradigm furthermore introduces other common attributes to set on annotation elements, such as an identifier, information on the annotator and provenance, and more. A full list is provided below:
element.id(str) - The unique identifier of the element
element.set(str) - The set the element pertains to.
element.cls(str) - The assigned class, i.e. the actual value of the annotation, defined in the set. Classes correspond with tagsets in this case of many annotation types. Note that since class is already a reserved keyword in python, the library consistently uses
element.processor(str) - The ID of the processor who last added/modified this element. The processor is an instance of
Processorand is part of the provenance data. It contains information regarding who or what performed the annotation, such as (not exhaustive):
element.processor.id(str) - the ID of the processor, has to be unique
element.processor.name(str) - the name of the processor, e.g. the name of a certain software tool or human annotator, needs not be unique
element.processor.type- the type of processor (e.g.
element.annotator(str) - The name or ID of the annotator who last added/modified this element, this is a less extensive mechanism used only if processor is not used.
element.annotatortype- Only if processor is not used: the type of annotator, can be either
element.confidence(float) - A confidence value expressing the confidence the annotator has in this annotation.
element.datetime(datetime.datetime) - The date and time when the element was added/modified.
element.n(str) - An ordinal label, used for instance in enumerated list contexts, numbered sections, etc..
The following attributes are specific to a speech context:
element.src(str) - A URL or filename referring the an audio or video file containing the speech. Access this attribute using the
element.speaker_src()method, as it is inheritable from ancestors.
element.speaker(str) - The name of ID of the speaker. Access this attribute using the
element.speech_speaker()method, as it is inheritable from ancestors.
element.begintime(4-tuple) - The time in the above source fragment when the phonetic content of this element starts, this is a
element.endtime(4-tuple) - The time in the above source fragment when the phonetic content of this element ends, this is a
Attributes that are not available for certain elements, or not set, default to
As FoLiA is a format for linguistic annotation, accessing annotation is one of
the primary functions of this library. This can be done using the methods
that are available on many FoLiA elements. These methods are similar to the
AbstractElement.select() method except they will raise a
NoSuchAnnotation exception when no such annotation is found. The
annotations() is that the former
will grab only one and raise an exception if there are more between which it
can’t disambiguate, whereas the second is a generator, but will still raise an
exception if none is found:
for word in doc.words(): try: pos = word.annotation(folia.PosAnnotation, 'http://somewhere/CGN') lemma = word.annotation(folia.LemmaAnnotation) print("Word: ", word) print("ID: ", word.id) print("PoS-tag: " , pos.cls) print("PoS Annotator: ", pos.annotator) print("Lemma-tag: " , lemma.cls) except folia.NoSuchAnnotation: print("No PoS or Lemma annotation")
Note that the second argument of
AbstractElement.select() can be used to restrict your selection to a certain set. In the
above example we restrict ourselves to Part-of-Speech tags in the CGN set.
Inline Annotation Types¶
The following inline annotation elements are available in FoLiA, they are embedded under a structural element (not necessarily a token, despite the name).
||Domain annotation: an extended token annotation element|
||Part-of-Speech annotation: a token annotation element|
||Language annotation: an extended token annotation element|
||Lemma annotation: a token annotation element|
||Sense annotation: a token annotation element|
||Subjectivity annotation/Sentiment analysis: a token annotation element|
Text and phonetic annotation¶
The actual text of an element, or a phonetic textual representation, are also considered annotations themselves.
||Text content element (
||Phonetic content element (
These are the only elements for which FoLiA prescribes a default set and a default class (
This will only be relevant if you work with multiple text layers (current
text vs OCRed text for instance) or with corrections of
orthography or phonetics.
FoLiA distinguishes inline annotation and span annotation, inline annotation is
embedded in-line within a structural element, and the annotation therefore
pertains to that structural element, whereas span annotation is stored in a
stand-off annotation layer outside the element and refers back to it. Span
annotation elements typically span over multiple structural elements, they
are all subclasses of
We will discuss three ways of accessing span annotation. As stated, span
annotation is contained within an annotation layer (a subclass of
AbstractAnnotationLayer) of a certain structure element, often a
sentence. In the first way of accessing span annotation, we do everything
explicitly: We first obtain the layer, then iterate over the span annotation
elements within that layer, and finally iterate over the words to which the
span applies. Assume we have a
sentence and we want to print all the named
entities in it, assuming the entities layer is embedded at sentence level as is
for layer in sentence.select(folia.EntitiesLayer): for entity in layer.select(folia.Entity): print(" Entity class=", entity.cls, " words=") for word in entity.wrefs(): print(word, end="") #print without newline print() #print newline
AbstractSpanAnnotation.wrefs() method, available on all span annotation elements, will return
a list of all words (as well as morphemes and phonemes) over which a span
annotation element spans.
This first way is rather verbose. The second way of accessing span annotation
takes another approach, using the
Word.findspans() method available on
Here we start from a word and seek span annotations in which that word occurs.
Assume we have a
word and want to find chunks it occurs in:
for chunk in word.findspans(folia.Chunk): print(" Chunk class=", chunk.cls, " words=") for word2 in chunk.wrefs(): #print all words in the chunk (of which the word is a part) print(word2, end="") print()
The third way allows us to look for span elements given an annotation layer and
words. In other words, it checks if one or more words form a span. This is an
exact match and not a sub-part match as in the previously described method. To
do this, we use use the
available on all annotation layers:
for span in annotationlayer.findspan(word1,word2): print("Class: ", span.cls) print("Text: ", span.text()) #same for every span here
Span Annotation Types¶
This section lists the available Span annotation elements, the layer that contains them is explicitly mentioned as well.
Some of the span annotation elements are complex and take span role elements as children, these are normal span annotation elements that occur on a within another span annotation (of a particular type) and can not be used standalone.
FoLiA distinguishes the following span annotation elements:
||Chunk element, span annotation element to be used in
||Span annotation element to encode dependency relations|
||Entity element, for entities such as named entities, multi-word expressions, temporal entities.|
||Predicate, used within
||Syntactic Unit, span annotation element to be used in
||A time segment|
These are placed in the following annotation layers:
||Chunking Layer: Annotation layer for
||Syntax Layer: Annotation layer for
||Dependencies Layer: Annotation layer for
||Entities Layer: Annotation layer for
||Observation Layer: Annotation layer for
||Sentiment Layer: Annotation layer for
||Statement Layer: Annotation layer for
||Syntax Layer: Annotation layer for
||Syntax Layer: Annotation layer for
||Timing layer: Annotation layer for
Some span annotation elements take span roles, depending on their type:
||Span role element that marks the dependent in a dependency relation.|
||The headspan role is used to mark the head of a span annotation.|
Creating a new document¶
Creating a new FoliA document, rather than loading an existing one from file,
is done by explicitly providing the ID for the new document in the
doc = folia.Document(id='example')
Whenever you add a new type of annotation, no matter whether linguistic, structural or otherwise, you need to declare it. Now this FoLiA library is capable of automatically declaring annotations as you go along as long as there is no ambiguity, so you won’t always need to do this explicitly, but it is still important to understand what is going on as you will run into it eventually.
Declarations are made using the
Document.declare() method. For example, do you want to use paragraphs in your
document? Declare it. The simplest form of declaration looks as follows:
In your declaration you can associate a set with the annotation type, we do this in the second parameter to
Document.declare() (for various annotation types this is mandatory even because without this there can be no
classes for the annotations). The set defines the vocabulary that is used, the declaration points to a URL where this
set is hosted, but don’t worry about this too much yet:
At this point, you may also include information about who or what performed this type of annotation. For instance, your
program or script. We call this provenance information, and each annotator is added through a processor, an instance
Processor, passed as argument to the
doc.declare(folia.PosAnnotation, 'http://some/path/brown-tag-set', Processor(name="mytagger") )
If you want to add another processor, simply call the declare method again, it will still result in only one declaration, but this will be tied to multiple processors:
othertagger = doc.declare(folia.PosAnnotation, 'http://some/path/brown-tag-set', Processor(name="othertagger") )
As shown in the above example, the
Document.declare() method will actually return the
Processor instance, which is useful if you have multiple, as each processor will automatically (unless you specificy it explicitly) get assigned an ID, which you can pass to individual annotations to associate your annotation with a particular processor. This will be illustrated later.
You’re not limited to just using one set, simply call declare with another set to add another declaration:
doc.declare(folia.PosAnnotation, 'http://some/path/cgn-tag-set' )
To check if a particular annotation type and set is declared, use the
Assuming we begin with an empty document, we should first add a Text element.
Then we can add paragraphs, sentences, or other structural elements. The
AbstractElement.add() method adds new children to an element:
text = doc.add(folia.Text) paragraph = text.add(folia.Paragraph) sentence = paragraph.add(folia.Sentence) sentence.add(folia.Word, 'This') sentence.add(folia.Word, 'is') sentence.add(folia.Word, 'a') sentence.add(folia.Word, 'test') sentence.add(folia.Word, '.')
Adding annotations, or any elements for that matter, is done using the
AbstractElement.add() method on the intended parent element. We assume that the annotations
we add have already been properly declared, otherwise an exception will be
raised as soon as
add() is called. Let’s build on the previous example:
#First we grab the fourth word, 'test', from the sentence word = sentence.words(3) #Add Part-of-Speech tag word.add(folia.PosAnnotation, set='brown-tagset',cls='n') #Add lemma lemma.add(folia.LemmaAnnotation, cls='test')
Note that in the above examples, the
add() method takes a class as first
argument, and subsequently takes keyword arguments that will be passed to the
A second way of using
AbstractElement.add() is by simply passing a fully instantiated child
element, thus constructing it prior to adding. The following is equivalent to the
above example, as the previous method is merely a shortcut for convenience:
#First we grab the fourth word, 'test', from the sentence word = sentence.words(3) #Add Part-of-Speech tag word.add( folia.PosAnnotation(doc, set='brown-tagset',cls='n') ) #Add lemma lemma.add( folia.LemmaAnnotation(doc , cls='test') )
AbstractElement.add() method always returns that which was added, allowing it to be chained.
In the above example we first explicitly instantiate a
LemmaAnnotation. Instantiation of any FoLiA element (always
Python class subclassed off
AbstractElement) follows the following
Class(document, *children, **kwargs)
AbstractElement.__init__() for all details on construction
Note that the document has to be passed explicitly as first argument to the constructor.
The common attributes are set using equally named keyword arguments:
Not all attributes are allowed for all elements, and certain attributes are
required for certain elements.
ValueError exceptions will be raised when these
constraints are not met.
Instead of setting
id. you can also set the keyword argument
generate_id_in and pass it another element, an ID will be automatically
generated, based on the ID of the element passed. When you use the first method
of adding elements, instantiation with
generate_id_in will take place automatically
behind the scenes when applicable and when
id is not explicitly set.
Any extra non-keyword arguments should be FoLiA elements and will be appended
as the contents of the element, i.e. the children or subelements. Instead of
using non-keyword arguments, you can also use the keyword argument
and pass a list. This is a shortcut made merely for convenience, as Python
obliges all non-keyword arguments to come before the keyword-arguments, which
if often aesthetically unpleasing for our purposes. Example of this use case
will be shown in the next section.
We already introduced the concept of provenance in the section on Declarations, provenance data clarifies what
the origin of an annotation is, i.e. who or what annotated it. If you declared an annotation type with a single
processor, then it will automatically act as the default for annotations of that type (and set). If, however, you have
multiple processors for a given annotation type and set (and it’s good practise to always assume this), you should make
this explicit when adding the annotation, using the
#First we declare the annotation type with a processor posprocessor = doc.declare(folia.PosAnnotation, set='brown-tagset', processor=Processor(name="mypostagger")) #Then we add an annotation to our word word.add( folia.PosAnnotation, set='brown-tagset', cls='n', processor=posprocessor)
The processor attribute takes an instance of
Processor, or the ID (not the name!) of an existing processor. If
the processor has not been declared yet, the library will do that for you automatically.
You can iterate over the entire provenance chain of a document
doc by iterating over
Provenance). To get a a specific processor by ID:
Instead of explicitly assigning a processor with invididual annotations, you can do so implicitly by associating a
processor with the
Document, it will then be automatically be used for any subsequent annotations you add. You
can associate a processor immediately upon document instantation:
doc = folia.Document(file="/tmp/example.folia.xml", processor=Processor(name="myscript", version="0.1"))
Instead of using
Processor(), you instantiate one using
Processor.create() which will autodetect a lot of
information regarding your processor for you, such as the system you’re running on, command that was executed, date & time, etc…:
doc = folia.Document(file="/tmp/example.folia.xml", processor=Processor.create(name="myscript", version="0.1"))
You can also associate a processor after instantiation (useful in case you want to use different processors at differents points in your script), but in that case you need to make sure to append it to the provenance chain yourself:
doc.processor = Processor.create(name="myscript", version="0.1") doc.provenance.append(doc.processor)
Unsetting a main processor is done using a simple
doc.processor = None.
Adding span annotation¶
Adding span annotation is easy with the FoLiA library. As you know, span
annotation uses a stand-off annotation embedded in annotation layers. These
layers are in turn embedded in structural elements such as sentences. However,
AbstractElement.add() method abstracts over this. Consider the following example of a named entity:
doc.declare(folia.Entity, "https://raw.githubusercontent.com/proycon/folia/master/setdefinitions/namedentities.foliaset.xml") sentence = text.add(folia.Sentence) sentence.add(folia.Word, 'I',id='example.s.1.w.1') sentence.add(folia.Word, 'saw',id='example.s.1.w.2') sentence.add(folia.Word, 'the',id='example.s.1.w.3') word = sentence.add(folia.Word, 'Dalai',id='example.s.1.w.4') word2 =sentence.add(folia.Word, 'Lama',id='example.s.1.w.5') sentence.add(folia.Word, '.', id='example.s.1.w.6') word.add(folia.Entity, word, word2, cls="per")
To make references to the words, we simply pass the word instances and use the
document’s index to obtain them. Note also that passing a list using the
contents is wholly equivalent to passing the non-keyword
word.add(folia.Entity, cls="per", contents=[word,word2])
In the next example we do things more explicitly. We first create a sentence and then add a syntax parse, consisting of nested elements:
doc.declare(folia.SyntaxLayer, 'some-syntax-set') sentence = text.add(folia.Sentence) sentence.add(folia.Word, 'The',id='example.s.1.w.1') sentence.add(folia.Word, 'boy',id='example.s.1.w.2') sentence.add(folia.Word, 'pets',id='example.s.1.w.3') sentence.add(folia.Word, 'the',id='example.s.1.w.4') sentence.add(folia.Word, 'cat',id='example.s.1.w.5') sentence.add(folia.Word, '.', id='example.s.1.w.6') #Adding Syntax Layer layer = sentence.add(folia.SyntaxLayer) #Adding Syntactic Units layer.add( folia.SyntacticUnit(self.doc, cls='s', contents=[ folia.SyntacticUnit(self.doc, cls='np', contents=[ folia.SyntacticUnit(self.doc, self.doc['example.s.1.w.1'], cls='det'), folia.SyntacticUnit(self.doc, self.doc['example.s.1.w.2'], cls='n'), ]), folia.SyntacticUnit(self.doc, cls='vp', contents=[ folia.SyntacticUnit(self.doc, self.doc['example.s.1.w.3'], cls='v') folia.SyntacticUnit(self.doc, cls='np', contents=[ folia.SyntacticUnit(self.doc, self.doc['example.s.1.w.4'], cls='det'), folia.SyntacticUnit(self.doc, self.doc['example.s.1.w.5'], cls='n'), ]), ]), folia.SyntacticUnit(self.doc, self.doc['example.s.1.w.6'], cls='fin') ]) )
AbstractElement.append() method would have had the same effect in the above syntax tree sample.
Any element can be deleted by calling the
AbstractElement.remove() method on its parent. Suppose we want to delete
A deep copy can be made of any element by calling its
word2 = word.copy()
The copy will be without parent and document. If you intend to associate a copy with a new document, then copy as follows instead:
word2 = word.copy(newdoc)
If you intend to attach the copy somewhere in the same document, you may want to add a suffix for any identifiers in its scope, since duplicate identifiers are not allowed and would raise an exception. This can be specified as the second argument:
word2 = word.copy(doc, ".copy")
Searching in a FoLiA document¶
If you have loaded a FoLiA document into memory, you may want to search for a
particular annotations. You can of course loop over all structural and
annotation elements using
AbstractAnnotationLayer.findspan() are useful methods of finding span
annotations covering particular words, whereas
AbstractSpanAnnotation.wrefs() does the reverse and finds the words for a
given span annotation element. In addition to these main methods of navigation
and selection, there is higher-level function available for searching, this
uses the FoLiA Query Language (FQL) or the Corpus Query Language (CQL).
These two languages are part of separate libraries that need to be imported:
from folia import fql from pynlpl.formats import cql
Corpus Query Language (CQL)¶
CQL is the easier-language of the two and most suitable for corpus searching. It is, however, less flexible than FQL, which is designed specifically for FoLiA and can not just query, but also manipulate FoLiA documents in great detail.
CQL has to be converted to FQL first, which is then executed on the given document. This is a simple example querying for the word “house”:
doc = folia.Document(file="/path/to/some/document.folia.xml") query = fql.Query(cql.cql2fql('"house"')) for word in query(doc): print(word) #these will be folia.Word instances (all matching house)
Multiple words can be queried:
query = fql.Query(cql.cql2fql('"the" "big" "house"')) for word1,word2,word3 in query(doc): print(word1, word2,word3)
Queries may contain wildcard expressions to match multiple text patterns. Gaps can be specified using . The following will match any three word combination starting with the and ending with something that starts with house. It will thus match things like “the big house” or “the small household”:
query = fql.Query(cql.cql2fql('"the"  "house.*"')) for word1,word2,word3 in query(doc): ...
We can make the gap optional with a question mark, it can be lenghtened with + or * , like regular expressions:
query = fql.Query(cql.cql2fql('"the" ? "house.*"')) for match in query(doc): print("We matched ", len(match), " words")
Querying is not limited to text, but all of FoLiA’s annotations can be used. To force our gap consist of one or more adjectives, we do:
query = fql.Query(cql.cql2fql('"the" [ pos = "a" ]+ "house.*"')) for match in query(doc): ...
The original CQL attribute here is
tag rather than
pos, this can be used too. In addition, all FoLiA element types can be used! Just use their FoLiA tagname.
Consult the CQL documentation for more. Do note that CQL is very word/token centered, for searching other types of elements, use FQL instead.
FoLiA Query Language (FQL)¶
FQL is documented here, a full overview is beyond the scope of this documentation. We will just introduce some basic selection queries so you can develop an initial impression of the language’s abilities.
All FQL processing is done via the following class, as already seen in the previous section:
||This class represents an FQL query.|
Selecting a word with a particular text is done as follows:
query = fql.Query('SELECT w WHERE text = "house"') for word in query(doc): print(word) #this will be an instance of folia.Word
Regular expression matching can be done using the
query = fql.Query('SELECT w WHERE text MATCHES "^house.*$"') for word in query(doc): print(word)
The classes of other annotation types can be easily queried as follows:
query = fql.Query('SELECT w WHERE :pos = "v"' AND :lemma = "be"') for word in query(doc): print(word)
You can constrain your queries to a particular target selection using the
query = fql.Query('SELECT w WHERE text MATCHES "^house.*$" FOR s WHERE text CONTAINS "sell"') for word in query(doc): print(word)
This construction also allows you to select the actual annotations. To select all people (a named entity) for words that are not John:
query = fql.Query('SELECT entity WHERE class = "person" FOR w WHERE text != "John"') for entity in query(doc): print(entity) #this will be an instance of folia.Entity
FOR statement may be chained, and Explicit IDs can be passed using the
query = fql.Query('SELECT entity WHERE class = "person" FOR w WHERE text != "John" FOR div ID "section.21"') for entity in query(doc): print(entity)
Sets are specified using the OF keyword, it can be omitted if there is only one for the annotation type, but will be required otherwise:
query = fql.Query('SELECT su OF "http://some/syntax/set" WHERE class = "np"') for su in query(doc): print(su) #this will be an instance of folia.SyntacticUnit
We have just covered the SELECT keyword, FQL has other keywords for manipulating documents, such as EDIT, ADD, APPEND and PREPEND.
Consult the FQL documentation at https://github.com/proycon/foliadocserve/blob/master/README.rst for further documentation on the language.
Throughout this tutorial you have seen the
Document class as a means
of reading FoLiA documents. This class always loads the entire document in
memory, which can be a considerable resource demand. The following class
provides an alternative to loading FoLiA documents:
It does not load the entire document in memory but merely returns the elements you are interested in. This results in far less memory usage and also provides a speed-up.
A reader is constructed as follows, the second argument is the class of the element you want:
reader = folia.Reader("my.folia.xml", folia.Word) for word in reader: print(word.id)
FoLiA has a number of text markup elements, these appear within the
t) element, iterating over the element of a
TextContent element will first and foremost produce strings, but also
uncover these markup elements when present. The following markup types exists:
Features allow a second-order annotation by adding the ability to assign
properties and values to any of the existing annotation elements. They follow
the set/class paradigm by adding the notion of a subset and class relative to
this subset. The
AbstractElement.feat() method provides a shortcut that can be used on any
annotation element to obtain the class of the feature, given a subset. To
illustrate the concept, take a look at part of speech annotation with some
pos = word.annotation(folia.PosAnnotation) if pos.cls = "n": if pos.feat('number') == 'plural': print("We have a plural noun!") elif pos.feat('number') == 'singular': print("We have a singular noun!")
AbstractElement.feat() method will return an exception when the feature does not exist.
Note that the actual subset and class values are defined by the set and not
FoLiA itself! They are therefore fictitious in the above example.
The Python class for features is
Feature, in the following example we
add a feature:
pos.add(folia.Feature, subset="gender", cls="f")
Although FoLiA does not define any sets nor subsets. Some annotation types do
come with some associated subsets, their use is never mandatory. The advantage
is that these associated subsets can be directly used as an XML attribute in
the FoLiA document. The FoLiA library provides extra classes, all subclassed
Feature for these:
A key feature of FoLiA is its ability to make explicit alternative annotations,
for inline annotations, the
alt) class is used to
this end. Alternative annotations are embedded in this structure. This implies
the annotation is not authoritative, but is merely an alternative to the actual
annotation (if any). Alternatives may typically occur in larger numbers,
representing a distribution each with a confidence value (not mandatory). Each
alternative is wrapped in its own
Alternative element, as multiple
elements inside a single alternative are considered dependent and part of the
same alternative. Combining multiple annotation in one alternative makes sense
for mixed annotation types, where for instance a pos tag alternative is tied to
a particular lemma:
alt = word.add(folia.Alternative) alt.add(folia.PosAnnotation, set='brown-tagset',cls='n',confidence=0.5) alt = word.add(folia.Alternative) #note that we reassign the variable! alt.add(folia.PosAnnotation, set='brown-tagset',cls='a',confidence=0.3) alt = word.add(folia.Alternative) alt.add(folia.PosAnnotation, set='brown-tagset',cls='v',confidence=0.2)
Span annotation elements have a different mechanism for alternatives, for those
the entire annotation layer is embedded in a
element. This element should be repeated for every type, unless the layers it
describeds are dependent on it eachother:
alt = sentence.add(folia.AlternativeLayers) layer = alt.add(folia.Entities) entity = layer.add(folia.Entity, word1,word2,cls="person", confidence=0.3)
Because the alternative annotations are non-authoritative, normal selection
methods such as
annotations() will never yield them,
unless explicitly told to do so. For this reason, there is an
alternatives() method on structure elements, for the first category of alternatives.
In summary, a list of the two relevant classes for alternatives:
Corrections are one of the most complex annotation types in FoLiA. Corrections can be applied not just over text, but over any type of structure annotation, inline annotation or span annotation. Corrections explicitly preserve the original, and recursively so if corrections are done over other corrections.
Despite their complexity, the library treats correction transparently. Whenever you query for a particular element, and it is part of a correction, you get the corrected version rather than the original. The original is always non-authoritative and normal selection methods will ignore it.
If you want to deal with correction, you have to explicitly handle the
Correction element. If an element is part of a correction, its
AbstractElement.incorrection() method will give the correction element, if not, it will
pos = word.annotation(folia.PosAnnotation) correction = pos.incorrection() if correction: if correction.hasoriginal(): originalpos = correction.original(0) #assuming it's the only element as is customary #originalpos will be an instance of folia.PosAnnotation print("The original pos was", originalpos.cls)
Corrections themselves carry a class too, indicating the type of correction (defined by the set used and not by FoLiA).
Correction.original(), corrections distinguish three other types,
Correction.new() (the corrected version),
Correction.current() (the current uncorrected version) and
Correction.suggestions() (a suggestion for correction), the former two and latter two usually form pairs,
new() can never be used together. Of
suggestions(index) there may be multiple, hence the index argument. These return, respectively, instances of
Adding a correction can be done explicitly:
wrongpos = word.annotation(folia.PosAnnotation) word.add(folia.Correction, folia.New(doc, folia.PosAnnotation(doc, cls="n")) , folia.Original(doc, wrongpos), cls="misclassified")
Let’s settle for a suggestion rather than an actual correction:
wrongpos = word.annotation(folia.PosAnnotation) word.add(folia.Correction, folia.Suggestion(doc, folia.PosAnnotation(doc, cls="n")), cls="misclassified")
In some instances, when correcting text or structural elements,
New may be
empty, which would correspond to an deletion. Similarly,
Original may be
empty, corresponding to an insertion.
The use of
Current is reserved for use with structure elements, such as words, in combination with suggestions. The structure elements then have to be embedded in
Current. This situation arises for instance when making suggestions for a merge or split.
Here is a list of all relevant classes for corrections:
Relations are used to make reference to external documents. It concerns
references as annotation rather than references which are explicitly part of
the text, such as hyperlinks and
The following elements are relevant for alignments:
FoLiA allows arbitrary descriptions to be assigned with any element. It also allows assigning metrics to any annotation, which consist of a key/value pair that often express a quantivative or qualitative measure. This is accomplished, respectively, with the following element classes:
FoLiA can be used with a variety of more advanced metadata schemes (e.g. Dublin Core,
CMDI). If this is too much, you can use its own simple native metadata
facility, a simple key value store . After instantiation of a
Document, the metadata can be
accessed through the
metadata attribute, which behaves like a Python
doc = folia.Document(file="/path/to/document.xml") doc.metadata['language'] = "en"