Relations querying

Supported from v4.0

Indexing and searching relations is supported from BlackLab 4.0.

Relations show how (groups of) words are related to one another. One of the most common types of relations is the dependency relation, which shows grammatical dependency between words.

If your corpus contains relations, you can query those as well. One advantage of this style of querying is that it's much easier to find nonadjacent words related to one another, or two related words regardless of what order they occur in.

See Indexing relations.

Querying relations is essentially done by building a partial tree of relations constraints. BlackLab will try and find this structure in the corpus. Relations queries can be combined with "regular" token-level queries as well.

Treebank systems

BlackLab supports limited relations querying, but is not as powerful as a full treebank system, which is primarily designed for this style of search. Links to some treebank systems can be found here. For BlackLab's relations querying limitations, see below.

An example dependency tree

Let's use an example to illustrate the various querying options. Here's a simple dependency tree for the phrase I have a fluffy cat:

      |
     have
    /    \
 (subj)   (obj)
 /          \
I            cat
           /   |
        (det)(amod)
        /      |
       a     fluffy

Finding specific relation types

We might want to find object relations in our data. We can do this as follows:

_ -obj-> _

This will find have a fluffy cat (the span of text covering the two ends of the relation), with a match info group named for the relation type (obj) containing the relation details between have and cat.

The two _ marks in the query simply means we only care about the relation type, not the source or target of the relation. If we specifically want to find instances where cat is the object of the sentence, we can use this instead:

_ -obj-> "cat"

So you can see that the token-based queries described previously are still useful here.

Can I use [] instead of _ ?

As explained above, _ in a relation expression means "any source or target". You might be tempted to use [] instead, especially if you know your relations always have a single-token source and target. This works just fine, but it's a bit slower (it has to double-check that source and target are actually of length 1), so we recommend sticking with _.

(the actual equivalent of _ here is []* (zero or more tokens with no restrictions), but that makes for less readable queries)

A note on terminology

For dependency relations, linguists call the left side the head of the relation and the right side the dependent. However, because dependency relations aren't the only class of relation, and because the term head can be a bit confusing (there's also the "head of an arrow", but that's the other end!), we will use source and target.

When talking about tree structures, we will also use parent and child.

Context	Terms
Dependency relations	head --> dependent
Relations in BlackLab	source --> target
Searching tree structures	parent --> child

Finding relation types using regular expressions

We can specify the relation type as a regular expression as well. To find both subject and object relations, we could use:

_ -subj|obj-> _

or:

_ -.*bj-> _

If you find it clearer, you can use parentheses around the regular expression:

_ -(subj|obj)-> _

With our example tree, the above queries will find all subject relations and all object relations. Each hit will have one relation in the match info. To find multiple relations per hit, read on.

Relation classes

When indexing relations in BlackLab, you assign them a class, a short string indicating what family of relations it belongs to. For example, you could assign the class string dep to dependency relations. An obj relation would become dep::obj.

To simplify things, rel is the default relation class in BlackLab. If you index relations without a class, they will automatically get this class. Similarly, when searching, if you don't specify a class, rel:: will be prepended to the relation type. So if you're not indexing different classes of relations, you can just ignore the classes.

Root relations

A dependency tree has a single root relation. A root relation is special relation that only has a target and no source. Its relation type is usually just called root. In our example, the root points to the word have.

You can find root relations with a special unary operator:

^--> _

This will find all root relations. The details for the root relation will be returned in the match info.

Of course you can place constraints on the target of the root relation as well:

^--> "have"

This will only find root relations pointing to the word have.

Finding two relations with the same source

What if we want to find the subject and object relations of a sentence, both linked to the same source (the verb in the sentence)? We can do that using a semicolon to separate the two target constraints (or child constraints):

_ -subj-> _ ;
  -obj-> _

As you can see, the source or parent is specified only once at the beginning. Then you may specify one or more target constraints (a relation type plus target, e.g. -subj-> _), separated by semicolons.

The above query will find hits covering the words involved in both relations, with details for the two relations in the match info of each hit. In our example, it would find the entire sentence I have a fluffy cat.

Target constraint uniqueness

Note that when matching multiple relations with the same source this way, BlackLab will enforce that they are unique. That is, two target constraints will only match two different relations.

Negative child constraints

You may want to have negative constraints, such as making sure that dog is not the object of the sentence. This can be done by prefixing the relation operator with !:

_  -subj-> _ ;
  !-obj-> "dog"

Note that this is different from :

_  -subj-> _ ;
   -obj-> [word != "dog"]

The second query requires an object relation where the target is a word other than dog; that is, the object relation must exist. By contrast, in the first case, we only require that there exists no object relation with the target dog, so this might match sentences without an object as well as sentences with an object that is not dog.

Searching over multiple levels in the tree

What if we want to query over multiple levels of the tree? For example, we want to find sentences where the target of the subj relation is the source of an amod relation pointing to fluffy, such as in our example tree.

_ -subj-> _ -amod-> "fluffy"

We can combine the techniques as well, for example if we also want to find the object of the sentence like before:

_ -subj-> (_ -amod-> _) ;
  -obj-> _

As you can see, the value of the expression (_ -amod-> _) is actually the source of the amod relation, so we can easily use it as the target of the subj relation.

The -..-> operator is right-associative (as you can see from the first example), but we do need parentheses here, or the parent of the -obj-> relation would be ambiguous.

Limitation: descendant search

One current limitation compared to dedicated treebank systems is the lack of support for finding descendants that are not direct children.

For example, if we want to look for sentences with the verb have and the word fluffy somewhere as an adjectival modifier in that sentence, we can't query something like this:

^--> "have" -->> -amod-> "fluffy"   # DOES NOT WORK

Instead, we have to know how many nodes are between have and fluffy, e.g. this does work:

^--> "have" --> _ -amod-> "fluffy"

Supporting arbitrary descendant search with decent performance is a challenge that we may try to tackle in the future.

For now, you might be able to work around this limitation using a hybrid between token-based and relations querying, e.g.:

(<s/> containing (^--> "have")) containing (_ -amod-> "fluffy")

Advanced relations querying features

Most users won't need this, but they might come in handy in some cases.

Controlling the resulting span

As shown in the previous section, relation expressions return the source of the matching relation by default. But what if you want a different part of the relation?

For example, if we want to find targets of the amod relation, we can do this:

rspan(_ -amod-> _, "target")

If we want the entire span covering both the source and the target (and anything in between):

rspan(_ -amod-> _, "full")

Note that full is the default value for the second parameter, so this will work too:

rspan(_ -amod-> _)

rspan supports another option: all will return a span covering all of the relations matched by your query.

rspan(_ -subj-> (_ -amod-> _) ; -obj-> _, "all")

Because this is pretty useful when searching relations, there's an easy way to apply this rspan operation: just add a parameter adjusthits=true to your BlackLab Server URL. Note that if your query already starts with a call to rspan, adjusthits=true won't do anything.

Capturing all relations in a sentence

If you want to capture all relations in the sentence containing your match, use:

"elephant" within rcapture(<s/>)

What actually happens here is that all relations in the matched clause are returned in the match info.

You can pass a second parameter with the match info name for the list of captured relations (defaults to captured_rels):

"elephant" within rcapture(<s/>, "relations")

If you only want to capture certain relations, you specify a third parameter that is a regular expression filter on the relation type. For example, to only capture relations in the fam class, use:

"elephant" within rcapture(<s/>, "relations", "fam::.*")

Cross-field relations

It is possible to have a corpus with multiple annotated fields, with relations that point from a position or span in one field to another. Annotated fields should be named e.g. contents__original and contents__corrected for this to work, and a relation from the first to the second field could be found like this:

"mistpyed" -->corrected "mistyped"

As you can see, the target version is appended to the relation operator. The source version is determined by the main annotated field searched (the field parameter for BlackLab Server; will default to the main annotated field, which is the first one you defined in your indexing configuration).

Cross-field relations are used to enable parallel corpora, which we'll discuss next.