In our earlier discussion in section 3. There we saw sample interpretive rules for a small number of phrase structure rules and vocabulary. The interpretive rules are repeated at the tree nodes from section 3. As can be seen, the Montagovian treatment of NPs as second-order predicates leads to some complications, and these are exacerbated when we try to take account of quantifier scope ambiguity.
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We mentioned Montague's use of multiple parses, the Cooper-storage approach, and the unscoped-quantifier approach to this issue in section 3. It is easy to see that multiple unscoped quantifiers will give rise to multiple permutations of quantifier order when the quantifiers are brought to the sentence level.
At this point we should pause to consider some interpretive methods that do not conform with the above very common but not universally employed syntax-driven approach. First, Schank and his collaborators emphasized the role of lexical knowledge, especially primitive actions used in verb decomposition, and knowledge about stereotyped patterns of behavior in the interpretive process, nearly to the exclusion of syntax.
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These ideas had considerable appeal, and led to unprecedented successes in machine understanding of some paragraph-length stories. Another approach to interpretation that subordinates syntax to semantics is one that employs domain-specific semantic grammars Brown and Burton While these resemble context-free syntactic grammars perhaps procedurally implemented in ATN-like manner , their constituents are chosen to be meaningful in the chosen application domain.
For example, an electronics tutoring system might employ categories such as measurement, hypothesis , or transistor instead of NP, and fault-specification or voltage-specification instead of VP. The importance of these approaches lay in their recognition of the fact that knowledge powerfully shapes our ultimate interpretation of text and dialogue, enabling understanding even in the presence of noisy, flawed, and partial linguistic input.
Statistical NLP has only recently begun to be concerned with deriving interpretations usable for inference and question answering and as pointed out in the previous subsection, some of the literature in this area assumes that the NL text itself can and should be used as the basis for inference.
We will mention examples of this type of work, and comment on its prospects, in section 8. We noted earlier that language is potentially ambiguous at all levels of syntactic structure, and the same is true of semantic content, even for syntactically unambiguous words, phrases and sentences. For example, words like bank , recover , and cool have multiple meanings even as members of the same lexical category; nominal compounds such as ice bucket, ice sculpture, olive oil, or baby oil leave unspecified the underlying relation between the nominals such as constituency or purpose.
Many techniques have been proposed for dealing with the various sorts of semantic ambiguities, ranging from psychologically motivated principles, to knowledge-based methods, heuristics, and statistical approaches.
Psychologically motivated principles are exemplified by Quillian's spreading activation model described earlier and the use of selectional preferences in word sense disambiguation. Examples of knowledge-based disambiguation would be the disambiguation of ice sculpture to a constitutive relation based on the knowledge that sculptures may be carved or constructed from solid materials, or the disambiguation of a man with a hat to a wearing -relation based on the knowledge that a hat is normally worn on the head. The possible meanings may first be narrowed down using heuristics concerning the limited types of relations typically indicated by nominal compounding or by with -modification.
Heuristic principles used in scope disambiguation include island constraints quantifiers such as every and most cannot expand their scope beyond their local clause and differing wide-scoping tendencies for different quantifiers e. Statistical approaches typically extract various features in the vicinity of an ambiguous word or phrase that are thought to influence the choice to be made, and then make that choice with a classifier that has been trained on an annotated text corpus. The features used might be particular nearby words or their parts of speech or semantic categories, syntactic dependency relations, morphological features, etc..
Such techniques have the advantage of learnability and robustness, but ultimately will require supplementation with knowledge-based techniques. For example, the correct scoping of quantifiers in contrasting sentence pairs such as. For example,. Thus in general appears to be the implicit default adverbial. But when the quantifying adverb is present, the sentence admits both an atemporal reading according to which many purebred racehorses are characteristically skittish, as well as a temporal reading to the effect that purebred racehorses in general are subject to frequent episodes of skittishness.
If we replace purebred by at the starting gate , then only the episodic reading of skittish remains available, while often may quantify over racehorses, implying that many are habitually skittish at the starting gate, or it may quantify over starting-gate situations, implying that racehorses in general are often skittish in such situations; furthermore, making formal sense of the phrase at the starting gate evidently depends on knowledge about horse racing scenarios.
The interpretive challenges presented by such sentences are or should be of great concern in computational linguistics, since much of people's general knowledge about the world is most naturally expressed in the form of generic and habitual sentences. Systematic ways of interpreting and disambiguating such sentences would immediately provide a way of funneling large amounts of knowledge into formal knowledge bases from sources such as lexicons, encyclopedias, and crowd-sourced collections of generic claims such as those in Open Mind Common Sense e.
Many theorists assume that the logical forms of such sentences should be tripartite structures with a quantifier that quantifies over objects or situations, a restrictor that limits the quantificational domain, and a nuclear scope main clause that makes an assertion about the elements of the domain e. The challenge lies in specifying a mapping from surface structure to such a logical form. While many of the principles underlying the ambiguities illustrated above are reasonably well understood, general interpretive algorithms are still lacking. The dividing line between semantic interpretation computing and disambiguating logical forms and discourse understanding—making sense of text—is a rather arbitrary one.
Language has evolved to convey information as efficiently as possible, and as a result avoids lengthy identifying descriptions and other lengthy phrasings where shorter ones will do. The reverse sequencing, cataphora , is seen occasionally as well. Determining the co referents of anaphors can be approached in a variety of ways, as in the case of semantic disambiguation.
Linguistic and psycholinguistic principles that have been proposed include gender and number agreement of coreferential terms, C-command principles e. An early heuristic algorithm that employed several features of this type to interpret anaphors was that of Hobbs But selectional preferences are important as well. Another complication concerns reference to collections of entities, related entities such as parts , propositions, and events that can become referents of pronouns such as they, this, and that or of definite NPs such as this situation or the door of the house without having appeared explicitly as a noun phrase.
Like other sorts of ambiguity, coreference ambiguity has been tackled with statistical techniques. These typically take into account factors like those mentioned, along with additional features such as antecedent animacy and prior frequency of occurrence, and use these as probabilistic evidence in making a choice of antecedent e. Parameters of the model are learned from a corpus annotated with coreference relations and the requisite syntactic analyses. Coming back briefly to nominal compounds of form N N, note that unlike conventional compounds such as ice bucket or ice sculpture —ones approachable using an enriched lexicon, heuristic rules, or statistical techniques—some compounds can acquire a variety of meanings as a function of context.
For example, rabbit guy could refer to entirely different things in a story about a fellow wearing a rabbit suit, or one about a rabbit breeder, or one about large intelligent leporids from outer space.
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Such examples reveal certain parallels between compound nominal interpretation and anaphora resolution: At least in the more difficult cases, N N interpretation depends on previously seen material, and on having understood crucial aspects of that previous material in the current example, the concepts of wearing a rabbit suit, being a breeder of rabbits, or being a rabbit-like creature.
In other words N N interpretation, like anaphora resolution, is ultimately knowledge-dependent, whether that knowledge comes from prior text, or from a preexisting store of background knowledge. A strong version of this view is seen in the work of Fan et al. For example, in a chemistry context, HCL solution is assumed to require elaboration into something like: solution whose base is a chemical whose basic structural constituents are HCL molecules.
Algorithms are provided and tested empirically that search for a relational path subject to certain general constraints from the modified N to the modifying N, selecting such a relational path as the meaning of the N N compound. As the authors note, this is essentially a spreading-activation algorithm, and they suggest more general application of this method see section 5. One pervasive phenomenon of this type is of course ellipsis , as illustrated earlier in sentences 2.
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Interpreting ellipsis requires filling in of missing material; this can often be found at the surface level as a sequence of consecutive words as in the gapping and bare ellipsis examples 2. Further complications arise when the imported material contains referring expressions, as in the following variant of 5. Here the missing material may refer either to Felix's boss or my boss called the strict and sloppy reading respectively , a distinction that can be captured by regarding the logical form of the antecedent VP as containing only one, or two, occurrences of the lambda-abstracted subject variable, i.
The two readings can be thought of as resulting respectively from scoping his boss first, then filling in the elided material, and the reverse ordering of these operations Dalrymple et al.
Other challenging forms of ellipsis are event ellipsis, as in 5. In applications these and some other forms of ellipsis are handled, where possible, by a making strong use of domain-dependent expectations about the types of information and speech acts that are likely to occur in the discourse, such as requests for flight information in an air travel adviser; and b interpreting utterances as providing augmentations or modifications of domain-specific knowledge representations built up so far.
Corpus-based approaches to ellipsis have so far focused mainly on identifying instances of VP ellipsis in text, and finding the corresponding antecedent material, as problems separate from that of computing correct logical forms e. Another refractory missing-material phenomenon is that of implicit arguments. For example, in the sentence.
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However, not all of the fillers for those slots are made explicitly available by the text—the carbon monoxide referred to provides one of the fillers, but the air in the interior of the car, and potential occupants of the car and that they rather than, say, the upholstery would be at risk are a matter of inference from world knowledge.
Finally, another form of shorthand that is common in certain contexts is metonymy , where a term saliently related to an intended referent stands for that referent. For example, in an airport context,. Like other types of underspecification, metonymy has been approached both from knowledge-based and corpus-based perspectives. Knowledge that can be brought to bear includes selectional preferences e. Lakoff and Johnson , rules for when to conjecture such relations e.
Corpus-based methods e. As for other facets of the interpretive process including parsing , use of deep domain knowledge for metonym processing can be quite effective in sufficiently narrow domains, while corpus-based, shallow methods scale better to broader domains, but are apt to reach a performance plateau falling well short of human standards. Text and spoken language do not consist of isolated sentences, but of connected, interrelated utterances, forming a coherent whole—typically, a temporally and causally structured narrative, a systematic description or explanation, a sequence of instructions, or a structured argument for a conclusion or in dialogue, as discussed later, question-answer exchanges, requests followed by acknowledgments, mixed-initiative planning, etc.