Child Language: The Parametric Approach (Oxford Linguistics)
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The sentences. The meaning and thereby the truth value of the attitudinal sentence 3. The meaning of 3. And fake beard in 3. A Montagovian analysis certainly would deal handily with such sentences. But again, we may ask how much of the expressive richness of Montague's type theory is really essential for computational linguistics.
To begin with, sentences such as 3. On the other hand 3. A modest concession to Montague, sufficient to handle 3. We can then treat look as a predicate modifier, so that look happy is a new predicate derived from the meaning of happy. And finally, fake is quite naturally viewed as a predicate modifier, though unlike most nominal modifiers, it is not intersective John wore something that was a beard and was fake or even subsective John wore a particular kind of beard.
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Note that this form of intensionality does not commit us to a higher-order logic—we are not quantifying over predicate extensions or intensions so far, only over individuals aside from the need to allow for plural entities, as noted. The rather compelling case for intensional predicate modifiers in our semantic vocabulary reinforces the case made above on the basis on extensional examples for allowing predicate modification.
Reification , like the phenomena already enumerated, is also pervasive in natural languages. Examples are seen in the following sentences. Humankind in 3. The name-like character of the term is apparent from the fact that it cannot readily be premodified by an adjective. The subjects in 3.
Here - ness is a predicate modifier that transforms the predicate polite , which applies to ordinary usually human individuals, into a predicate over quantities of the abstract stuff, politeness. This allows for modification of the nominal predicate before reification, in phrases such as fluffy snow or excessive politeness. The subject of 3. Finally 3. Here we can posit a reification operator Ke that maps sentence intensions into kinds of situations. This type of sentential reification needs to be distinguished from that -clause reification, such as appears to be involved in 3.
We mentioned the possibility of a modal-logic analysis of 3. The use of reification operators is a departure from a strict Montgovian approach, but is plausible if we seek to limit the expressiveness of our semantic representation by taking predicates to be true or false of individuals, rather than of objects of arbitrarily high types, and likewise take quantification to be over individuals in all cases, i. Some computational linguists and AI researchers wish to go much further in avoiding expressive devices outside those of standard first-order logic. One strategy that can be used to deal with intensionality within FOL is to functionalize all predicates, save one or two.
Here loves is regarded as a function that yields a reified property, while Holds or in some proposals, True , and perhaps equality, are the only predicates in the representation language.
Then we can formalize 3. The main practical impetus behind such approaches is to be able to exploit existing FOL inference techniques and technology. Another important issue has been canonicalization or normalization : What transformations should be applied to initial logical forms in order to minimize difficulties in making use of linguistically derived information?
The uses that should be facilitated by the choice of canonical representation include the interpretation of further texts in the context of previously interpreted text and general knowledge , as well as inferential question answering and other inference tasks. We can distinguish two types of canonicalization: logical normalization and conceptual canonicalization. An example of logical normalization in sentential logic and FOL is the conversion to clause form Skolemized, quantifier-free conjunctive normal form. The rationale is that reducing multiple logically equivalent formulas to a single form reduces the combinatorial complexity of inference.
For example, in a geographic domain, we might replace the relations between countries is next to, is adjacent to, borders on, is a neighbor of, shares a border with, etc. In the domain of physical, communicative, and mental events, we might go further and decompose predicates into configurations of primitive predicates.
As in the case of logical normalization, conceptual canonicalization is intended to simplify inference, and to minimize the need for the axioms on which inference is based. A question raised by canonicalization, especially by the stronger versions involving reduction to primitives, is whether significant meaning is lost in this process.
For example, the concept of being neighboring countries, unlike mere adjacency, suggests the idea of side-by-side existence of the populations of the countries, in a way that resembles the side-by-side existence of neighbors in a local community. More starkly, reducing the notion of walking to transporting oneself by moving one's feet fails to distinguish walking from running, hopping, skating, and perhaps even bicycling.
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Therefore it may be preferable to regard conceptual canonicalization as inference of important entailments, rather than as replacement of superficial logical forms by equivalent ones in a more restricted vocabulary. We will comment further on primitives in the context of the following subsection. While many AI researchers have been interested in semantic representation and inference as practical means for achieving linguistic and inferential competence in machines, others have approached these issues from the perspective of modeling human cognition. Prior to the s, computational modeling of NLP and cognition more broadly were pursued almost exclusively within a representationalist paradigm, i.
In the s, connectionist or neural models enjoyed a resurgence, and came to be seen by many as rivalling representationalist approaches. We briefly summarize these developments under two subheadings below. Some of the cognitively motivated researchers working within a representationalist paradigm have been particularly concerned with cognitive architecture , including the associative linkages between concepts, distinctions between types of memories and types of representations e. Others have been more concerned with uncovering the actual internal conceptual vocabulary and inference rules that seem to underlie language and thought.
Ross Quillian's semantic memory model, and models developed by Rumelhart, Norman and Lindsay Rumelhart et al. A common thread in cognitively motivated theorizing about semantic representation has been the use of graphical semantic memory models, intended to capture direct relations as well as more indirect associations between concepts, as illustrated in Figure This particular example is loosely based on Quillian Quillian suggested that one of the functions of semantic memory, conceived in this graphical way, was to enable word sense disambiguation through spreading activation.
In particular, the activation signals propagating from sense 1 the living-plant sense of plant would reach the concept for the stuff, water , in four steps along the pathways corresponding to the information that plants may get food from water , and the same concept would be reached in two steps from the term water , used as a verb, whose semantic representation would express the idea of supplying water to some target object. Such conceptual representations have tended to differ from logical ones in several respects.
One, as already discussed, has been the emphasis by Schank and various other researchers e. However, this involves a questionable assumption that subtle distinctions between, say, walking to the park, ambling to the park, or traipsing to the park are simply ignored in the interpretive process, and as noted earlier it neglects the possibility that seemingly insignificant semantic details are pruned from memory after a short time, while major entailments are retained for a longer time. Another common strain in much of the theorizing about conceptual representation has been a certain diffidence concerning logical representations and denotational semantics.
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The relevant semantics of language is said to be the transduction from linguistic utterances to internal representations, and the relevant semantics of the internal representations is said to be the way they are deployed in understanding and thought. For both the external language and the internal mentalese representation, it is said to be irrelevant whether or not the semantic framework provides formal truth conditions for them. The rejection of logical semantics has sometimes been summarized in the dictum that one cannot compute with possible worlds.
However, it seems that any perceived conflict between conceptual semantics and logical semantics can be resolved by noting that these two brands of semantics are quite different enterprises with quite different purposes. Certainly it is entirely appropriate for conceptual semantics to focus on the mapping from language to symbolic structures in the head, realized ultimately in terms of neural assemblies or circuits of some sort , and on the functioning of these structures in understanding and thought. But logical semantics, as well, has a legitimate role to play, both in considering how words and larger linguistic expressions relate to the world and how the symbols and expressions of the internal semantic representation relate to the world.
This role is metatheoretic in that the goal is not to posit cognitive entities that can be computationally manipulated, but rather to provide a framework for theorizing about the relationship between the symbols people use, externally in language and internally in their thinking, and the world in which they live. It is surely undeniable that utterances are at least sometimes intended to be understood as claims about things, properties, and relationships in the world, and as such are at least sometimes true or false.
It would be hard to understand how language and thought could have evolved as useful means for coping with the world, if they were incapable of capturing truths about it. Moreover, logical semantics shows how certain syntactic manipulations lead from truths to truths regardless of the specific meanings of the symbols involved in these manipulations and these notions can be extended to uncertain inference, though this remains only very partially understood.
Thus, logical semantics provides a basis for assessing the soundness or otherwise of inference rules. While human reasoning as well as reasoning in practical AI systems often needs to resort to unsound methods abduction, default reasoning, Bayesian inference, analogy, etc. A strong indication that cognitively motivated conceptual representations of language are reconcilable with logically motivated ones is the fact that all proposed conceptual representations have either borrowed deliberately from logic in the first place in their use of predication, connectives, set-theoretic notions, and sometimes quantifiers or can be transformed to logical representations without much difficulty, despite being cognitively motivated.
As noted earlier, the s saw the re-emergence of connectionist computational models within mainstream cognitive science theory e. We have already briefly characterized connectionist models in our discussion of connectionist parsing.
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But the connectionist paradigm was viewed as applicable not only to specialized functions, but to a broad range of cognitive tasks including recognizing objects in an image, recognizing speech, understanding language, making inferences, and guiding physical behavior. The emphasis was on learning, realized by adjusting the weights of the unit-to-unit connections in a layered neural network, typically by a back-propagation process that distributes credit or blame for a successful or unsuccessful output to the units involved in producing the output Rumelhart and McClelland From one perspective, the renewal of interest in connectionism and neural modeling was a natural step in the endeavor to elaborate abstract notions of cognitive content and functioning to the point where they can make testable contact with brain theory and neuroscience.
But it can also be seen as a paradigm shift, to the extent that the focus on subsymbolic processing began to be linked to a growing skepticism concerning higher-level symbolic processing as models of mind, of the sort associated with earlier semantic network-based and rule-based architectures.