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LapDevelopment_Annotations
This page documents the structure and form of annotations in LAP, i.e. the adaptation of the Linguistic Annotation Framework (LAF) to LAP and how it is stored in the database.
In the following, we will use the following input file toy.txt as a running example:
The cat chased the dog.
Fido barked.
MongoDB is a document database. This means that instead of storing records, as a traditional SQL database, it stores free-form JSON documents. Individual documents are grouped into collections, corresponding roughly to the table of an SQL database, and collections are grouped into databases.
LAP stores output from tools in per-user databases, and the records created from a single run of a tool is stored in a single collection. Thus, there is a one-to-one correspondence between collections and tool invocations.
All documents stored in MongoDB contain an attribute _id, a globally unique identifier used internally by the database.
Metadata related to a sequence of processing steps in LAP is stored in a file called the receipt. This file contains the names of the collections created by previous processing steps and what kinds of annotations are present in the database, and it is stored as a JSON document. Tool scripts take the receipt as an input argument and produce a new, updated receipt reflecting the information added by the tool.
For example, after uploading toy.txt and running it through the NLTK punkt sentence segmenter and the REPP tokenizer, we get this receipt:
{
"annotators": {
"nltk_punkt": "ff7fe1ce-1fe7-11e5-9573-8c705a6aa768",
"repp": "ff9fe12c-1fe7-11e5-90ae-8c705a6aa768"
},
"media": {
"text": "ff55d9d8-1fe7-11e5-9045-8c705a6aa768"
},
"annotations": {
"token": [
"repp"
],
"sentence": [
"nltk_punkt"
]
},
"receipt_origin": "repp"
}
As we can see, there are four top-level fields in the document: receipt_origin, media, annotators, and annotations. The receipt_origin field is name of the tool that produced this receipt, in our case repp. The media field is a dictionary of media types linked to the name of the collection containing that medium. Currently, only text is supported.
The annotators field is a dictionary whose keys are the tools run on the dataset so far and whose values are the names of the collections containing the records created by the tool, and annotations is a dictionary of annotation types (described in further detail later in this document) and the names of the tools that have added annotations of this type to the data.
The LAP API provides functionality to parse the receipt and query the database in lap.store.
LAF considers linguistic annotation to be annotations over a graph comprised of nodes and edges (collectively graph elements), with nodes possibly being linked to one or more regions in a medium. The LAF model is agnostic to media types (text, audio, video, etc.), but LAP currently only supports textual data.
LAP stores individual nodes, edges and regions as individual records in the database.
A region is a delimited subset of the media. For text, this is obviously character offsets into the raw input string. LAP stores these regions as (start, end) pairs, where start is the character offset of the first character in the region and end is the offset of the first character not in the region (that is, the region is the semi-open interval [start, end)). The offsets are zero-indexed.
For example, the region corresponding to the first sentence in our example text is the span 0, 23 and it is stored as the following document:
{
"_id" : ObjectId("5593d512b8c2543703f3cfe6"),
"origin" : "nltk_punkt",
"index" : 0,
"anchors" : [
0,
23
],
"type" : "region",
"id" : "nltk_punkt-r1"
}
A node represents anything that can be annotated with linguistic information (tokens, sentences, PoS tags, etc.), while an edge represents links between these (for example, a node representing a PoS tag is linked by an edge to the node representing the tagged token). Both edges and nodes can have annotations, feature-structures containing the information in the node, but crucially linguistic information shall only be contained in annotations on nodes; annotations on edges can only for non-linguistic information.
All graph elements share the attributes id, a unique identifier, index, a zero-indexed total ordering of the element relative to its sibling elements of the same kind (or -1 if not applicable), rank, the ordering of an element relative to other elements with the same index, and origin, containing the name of the tool that produced it. Finally, a graph element has an attribute annotations containing the feature structure with the annotation actually produced by the tool (for example, the PoS tag output by a tagger).
Nodes may additionally have a list of links, each link being a list of ids of region objects relevant to the node (such as the region corresponding to a token output by a tokenizer), and edges have the additional attributes from and to, being the ids of the pair of nodes linked by the edge.
For example, the first token created by REPP in our toy example is:
{
"_id" : ObjectId("5593d513b8c2543727c6fde6"),
"origin" : "repp",
"index" : 0,
"links" : [
[
"repp-r1"
]
],
"rank" : 0,
"annotations" : {
"repp" : {
"class" : "token",
"label" : "The"
}
},
"type" : "node",
"id" : "repp-n1"
}
This node is then linked to a sentence node (created by a different tool, and stored in a different collection) by the following edge:
{
"_id" : ObjectId("5593d513b8c2543727c6fde7"),
"origin" : "repp",
"index" : 0,
"from" : "repp-n1",
"annotations" : {
"repp" : {
"class" : "token-link"
}
},
"to" : "nltk_punkt-n1",
"type" : "edge",
"id" : "repp-e1"
}
The annotations field is a dictionary, currently with only a single entry with the same name as the origin field, containing a dictionary with the annotations produced. The exact structure of the annotation dictionary depends on the type of annotation, described further in the next section.
LAP supports a number of different kinds of annotation. The type of annotation is determined by the class entry in the annotation dictionary.
The sentence and token annotation classes contains information relevant to sentences and tokens, respectively. At present, these contain only the single attribute label, being the label produced by the tool. This label may or may not be the same as the raw substring in the media, as the tool may normalize whitespace or downcase sentence-initial tokens that are not proper names.
For example the token annotation for the first token in our example text is:
{
"class" : "token",
"label" : "The"
}
The morphology class of annotation contains information relevant to the morphology of a node. Annotations of this type must contain at least one of the attributes pos, the part-of-speech, or lemma, the lemma (the pos field is not required in order to allow for dedicated lemmatizers that only produce lemmata). The annotations may furthermore contain the fields features, tool and language specific morphological features, and derivation, containing information on derivational morphology.
For example, the PoS annotation created by HunPos on the first token of our example is:
{
"class" : "morphology",
"pos" : "DT"
}
Annotations of the dependency class represent (parts of) dependency graphs. These are stored somewhat differently from token-level information. Since LAF prescribes that linguistic information must be attached to nodes and not edges, the intuitive approach of encoding dependencies solely as edges between token-level nodes is not possible. Instead, dependencies are encoded by a node and a pair of edges. The node, annotated with data in the dependency class, contains the linguistic information, while the edges link the head and dependent nodes to the dependency node.
Nodes in the dependency class contain the attribute label, being the dependency label of the arc, and the attribute head, the token index of the head token. This latter attribute is a convenience attribute used internally by the application to simplify conversion to CoNLL-like formats.
Nodes in the token class are linked by edges to nodes in the sentence class, and nodes in the morphology class are linked to nodes in the token class. However, some tools produce both tokens and morphology at once, which will result in two sets of edges being stored in the same collection. To easily let us extract, for example, the edges linking the morphology nodes to token nodes, edges are annotated with the linkage class. Annotations in this class have the attributes domain and range, specifying what kind of node the edge is going from and what it's going to, respectively.
For example, the edge linking the first token of our example text to the first sentence is annotated with:
{
"class" : "linkage",
"domain" : "token",
"range" : "sentence"
}
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