Type Raising and the Cooperative Construction of Meaning

Posted on December 11, 2012

One of the goals of computational semantics is to find an algorithmic way of building representations of natural language utterances that would enable us to perform sound inference on them. Since first-order predicate logic is a well-studied and expressive formal language, we will consider the mapping of natural language utterances onto first-order predicate logic formulas.

In order to build these formulas, we will rely on Frege’s principle of compositionality. We will assume that our input, the syntactic representation of the utterance, will be in the form of a binary constituency tree. We associate a lambda term to every word of the sentence (a leaf of the constituency tree) using a lexicon. The semantics of an inner node are then obtained by function application, with one of the children acting as functor and the other as argument. We will remark which subconstituent is to be the functor and which the argument, even though this can be easily inferred from the types of the lambda terms. The semantic representation of the sentence is defined as the lambda term associated with the root node.

Simple Things First

Let us consider the simplest possible cases first. We will look at proper nouns and intransitive verbs.

In the above lexicon, I use the following typographic conventions: “double quotes” for natural language expressions, bold for the first-order predicate logic formulas (or their parts) and the unadorned typeface for the lambda calculus specific matter. Later on, I will use expressions in double quotes to refer not only to the expressions themselves, but also to their semantic representations, either computed through composition or obtained from the lexicon. Note that the bold parentheses do not correspond to lambda calculus function applications, they are part of the produced FOPL formulas. I will always mark lambda applications explicitly with the @ symbol. I have also taken the liberty of assigning types to the lambda terms. The two atomic types that come naturally in the domain of FOPL formulas are terms (t) and formulas (f).

The lexicon I show here is very straightforward. We map the proper noun “Vincent” onto a constant and the intransitive verb “growls” onto a lambda abstraction which builds an atomic formula by filling the gap in the unary relation with the supplied argument (we will call functions that take terms and produce formulas predicates).

Let us take the simple sentence “Vincent growls” and its syntactic structure.

(S (NP "Vincent")
   (VP "growls"))

We obtain the semantic representation of the sentence by applying the “growls” lambda term to the “Vincent” one and β-reducing, growl(vincent) : f.

Let us now consider transitive verbs, as in the sentence “Vincent likes Mia”, with the following lexicon additions

and syntactic structure.

(S (NP "Vincent")
   (VP (VT "likes")
       (NP "Mia")))

Unsurprisingly, “Mia” maps to a constant and “likes” maps to a binary predicate (a function that builds a formula using two terms as arguments). In the sentence above, “likes” applies to “Mia” first, yielding λy. like(y, mia) : t → f after β-reduction. Applying this lambda term to that of “Vincent” yields the final representation, like(vincent, mia) : f.

So far so good. Some things to note about our system so far. First off, syntactic categories always correspond to a single semantic type (see table below). Second, the head constituent ends up always being the functor during composition.

Quantifiable Complications

We will now generalize our notion of a noun phrase and admit phrases with determiners such as “a” and “every”. The sentence we would like to be able to analyze is “Every boxer growls”. The desired semantic representation would look something like this ∀x. (boxer(x)growl(x)) : f. It is obvious that the relations boxer and growl correspond to the words “boxer” and “growls”. What is the semantics of “every” then? Well, as the coloring hints, it is the quantifier, the implication and the variables. However, our old way of composing VPs with NPs will not work here, as one does not simply put a quantifier in the argument position of a relation.

Here are the new elements of our lexicon.

We represent common nouns as unary predicates (sets of entities are after all a natural denotation for common nouns). As for the quantifier, we map it to a function that builds a universal quantification (see generalized quantifiers). The first argument is the restricting predicate which defines the range of the variable, the second argument is the predicate which is to be true in the domain of quantification.

Calculemus! *

(S (NP (Det "Every")
       (N "boxer"))
   (VP "growls"))

“Every” applies to “boxer” yielding λQ. ∀x. (boxer(x) (Q@x)) : (t → f) → f. The resulting NP then applies to “growls” to produce ∀x. (boxer(x)growl(x)) : f. Hurray!

OK, what we have here is not as great as it looks. Contrary to the previous situations, it is now the subject NP which acts as the functor, not the VP. However, in our new theory, this is true only for the case when the NP turns out to be a quantified NP. In the case of a proper noun, we still make the VP the functor. This slightly annoying discrepancy has more disturbing implications. The semantic type of one NP (“every boxer” : (t → f) → f)) can be different from that of another NP (“Vincent” : t). This loss of consistency between syntactic and semantic types would make writing the rest of our lexicon difficult as we would always have to consider both cases whenever dealing with NPs.

Luckily, we can solve this problem by raising the types of the proper nouns from t to (t → f) → f. Whenever we have lambda terms F : α → β and G : α, we can construct G’ = λP. P@G : (α → β) → β, which satisfies that F@G = G’@F (proof via simple β-reduction). What it means is that we can take a functor F and an argument G, do some trivial change to G, and then use the new G as a functor with F as the argument while still getting the same result as before. If we apply this technique to our proper nouns, we get the following updated lexicon entries.

An intuitive way to look at the current NP might be as a thing that ranges over some entities and when given a predicate, produces a formula that is true when the entities being ranged over satisfy the predicate. Therefore, in the above implementations of “Vincent”, the way to produce a formula which is true whenever vincent satisfies a predicate is to simply use the constant vincent as the term for the predicate being built.

Stuff Gets Difficult

Let’s try and do a recap of the types we have so far.

Oops, we seem to have a problem. By changing what it means to be an NP we have broken our transitive verbs which relied on NPs being terms.

If we were to consider a sentence where the transitive verb has a proper noun as the object (e.g. “Every boxer likes Mia”), things would still work out (we can type our proper nouns as (t → (t → f)) → (t → f)). However, when we try to work with a quantified object, things break.

Let’s use the sentence “Every boxer likes a woman” as an example.

(S (NP (Det "Every")
       (N "boxer"))
   (VP (TV "likes")
       (NP (Det "a")
           (N "woman"))))

The semantics of “woman” are straightforward. As for “a”, we construct another quantifier, this time using an existential quantification. We can construct the meaning of “a woman” as λQ. ∃y. (woman(y) (Q@y)) : (t → f) → f. Now, if we try to apply this lambda term to the one of “likes” or vice versa, we get nonsense. In the first case, we end up with a lambda abstraction inside of our FOPL formula, and in the second case, we place (an abstraction of) a quantification as an argument of a relation. Neither of these can ever resolve to a proper formula.

How do we fix this? Well, let us imagine how we would like the meaning of “likes a woman” to look like.

The above is one reasonable answer (Beware of the two ys! The orange bold one belongs to the constructed FOPL formula and was contributed by “a”. The red plain one is part of the lambda calculus and should belong to “like”.). These semantics for “likes a woman” look good. If we apply them to a term, they yield a formula stating that the entity named by the term does indeed “like a woman”, i.e. what we have is a working predicate.

Now that we know what we want, it is not so difficult to work out the correct version of the “likes” semantics on paper.

OK, that looks good. Let’s check if the whole thing works.


We have a consistent theory of the syntax-semantics interface now, here is our syntax-to-semantics type mapping.

The transitive verb is still expressed as a function taking an NP and producing a VP, which seems somewhat linguistically plausible.

The Part Where I Get Confused Too

OK, so what did I do to my lexicon entry for “likes” that it magically started doing exactly what I needed instead of breaking horribly?

If we look at the type signature of our new “likes” representation and compare it with the old one, we can see that we have swapped out the old type of NP (t) with its new raised type ((t → f) → f). In the body of the abstraction, we are calling our argument O and passing it something which looks like our original value. Isn’t this just like type raising? Why did the meaning of our sentence change then?

Well, it’s not really like type raising if you look at it closely.

<td><span style="color:red">λx. λy.
<td> : t → t → f</td>
<td><span style="color:red">λO. O@(λx. λy.
<td> : ((t → t → f) → (t → f)) → (t → f)</td>
<td><span style="color:red">λO. λy. O@(λx.
<td> : ((t → f) → f) → t → f</td>
this is the original:
this would be the type raised version:
this is what we used:

Now we can see that the position of λy is what is different in the two versions. So what does our version actually do? It’s not that hard to decipher. After a transitive verb is applied to an object, it is supposed to return a predicate, that is a function which takes a term and returns a formula stating something about the term.

Some examples of predicates we have seen:

Let’s dissect our “likes” term then.

First off, we accept the object as an argument and bind its name to the variable O. Then we define the predicate we will return. The name of the predicate’s argument will be y, this is the variable that the subject will be replacing with its term. Finally, how does the body of the predicate look like? To find out, we ask the object to build it, capturing any term it wants to use to represent itself in the relation using the variable x. When O is the meaning of “Mia”, the object sends mia as the term to represent it and returns the relation. When O is a quantified phrase like “a woman”, it sends the logical variable y as its representative x and it wraps the resulting relation in a quantifier providing a range for y.


OK, so things got pretty crazy pretty fast. How come we ended up having to repeatedly juggle higher-order functions and raise types? We can find an answer to this by inspecting the complexity (intertwinedness) of the expressions we set out to generate.

To produce growl(vincent) : f, we just had to apply the second-order function “growls” to the first-order constant “Vincent”. This was a fairly trivial thing as the representation of “Vincent” is just plugged into a hole in the represenation of “growls”. Similarly goes for plugging “Vincent” and “Mia” into the binary like relation.

Things get more interesting when we try to generate something like ∀x. (boxer(x)growl(x)) : f. When we want to combine “every” and “boxer”, we ask “every” to start. “Every” lays down the quantifiers and asks “boxer” to build the restricting formula, which in turn asks “every” for the term to be filled in the predicate (the variable x). In order to handle this case, we raised the NPs over VPs and ended up with third-order items for the NPs in the lexicon.

Finally, to get quantified noun phrases working as objects of transitive verbs, we had to fix our definition of “likes”, which we raised over the NPs and which made “likes” a fourth-order item in the lexicon. Let’s take a look at applying “likes” to an object such as “a woman”, λy. ∃y. (woman(y) like(y, y)) : t → f. Here we ask “likes” to build a term, which begins by putting down the lambda abstraction (λy.), then passing control to the object, in this case “a woman”. The object lays down any necessary quantifiers (∃y. woman(y)) and asks “likes” to build the consequent of the quantifier. “Likes” will build the formula (like(y, …)), but not without taking a term to place in the relation from the object (y). In other words, the function assigned by the lexicon to “likes” is a fourth-order function, which calls the third-order function corresponding to “a woman”, which calls the second-order lambda function defined within “likes” which uses the first-order argument x supplied to it by the object “a woman”.

This kind of there-and-back-again exchange of control is enforced by the interleaved syntactic structure of the FOPL formulas we want to produce (the colors corresponding to the individual lexical items do not form sovereign subtrees) and the principle of compositionality. Each of the two constituents to be combined contains only the information strictly contained in its subtree. To combine these two pieces of information into an elaborate structure, the two lambda terms have to cooperate with each other, exchanging continuation-like lambda functions.

One Last Jump

OK, we have gotten our heads around the recursions in the compositions and our pyramid of types, but there is one more thing I’d like to do.

If we look at our theory, there are still some nasty spots left. First off, the type of NP is VP → S, i.e. NPs consume VPs to produce sentences. This sounds quite unintuitive from a linguistic standpoint. We would much rather have a VP which satisfies its valency by consuming an NP as its subject and forms a sentence. Also, there is something off about how the lexical entry for “likes” treats its object fundamentally differently from its subject. Finally, the notion of who gets to be the functor seems kind of arbitrary. Within VPs, it is the verb itself, the head of the phrase. Within NPs, it is the determiner (which is also the head of its phrase, if you subscribe to the theory of generative grammar). However, within the top-level sentence, it is not VP, the head of the sentence, which is the functor, but the subject NP.

All of these problems are linked and we can solve them in one stroke using a technique we have used several times before. Can you guess which? Yep, it’s type raising. Here are the new lexical entries for our verbs.

In the case of the intransitive verb, we simply type raise the VP using the general type raising rule we have used on proper nouns before. For “likes”, it is a little bit more complicated. “Likes” is a transitive verb. That is, it is a function from an NP to a VP. So first, we have to strip away the outer lambda abstraction to get the VP body contained inside and then we mechanically apply the type raising rule on that VP term, changing λy. … into λS. S@(λy. …).

Let’s have look at our new type mapping.

Much better! The VP type now actually makes sense linguistically, the functors line up with head constituents, the arguments of transitive verbs are now handled uniformly and we have even made the transitive and intransitive verbs look similar to each other.

Minor Caveats

If you try to switch the order of S@(λy. and O@(λx. in the lexical entry for “likes” (or any other transitive verb for that matter), you will generate a different representation for sentences which have quantifiers in both the subject and object. For example:

These different formulas actually correspond to two different readings of the original sentence “Every boxer likes a woman” (scope ambiguity). The top interpretation is the original one and corresponds to the reading “For every boxer there is a specific woman that he likes”, while the bottom interpretation corresponds to the reading “There is a (one) woman and every boxer likes her”.

This ambiguity can be represented in our theory (maybe somewhat crudely) by providing two different lexical entries for transitive verbs, one for each permissible order of argument qualifiers.

Finally, we should also do something about the FOPL variables we generate. Hard-coding x into the lexical entry for “every” and y into the one for “a” doesn’t seem like a good solution, if only because it precludes us from using the same quantifier in the arguments of one verb, e.g. as in “Every boxer likes every woman”.

Further Reading

  1. Montague, Richard: English as a Formal Language
  2. Montague, Richard: The Proper Treatment of Quantification in Ordinary English
  3. Blackburn, Patrick and Bos, Johan: Representation and Inference in Natural Language

I haven’t read any of these, which might explain my state of mild confusion, though I plan to read all of them soon. 1. and 2. are foundational papers in computational semantics which I expect will cover most of what I showed here. 3. is one of the few textbooks in the field.

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