| { | |
| "paper_id": "C90-2007", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T12:37:07.240566Z" | |
| }, | |
| "title": "Lexical Ambiguity and The Role of Knowledge Representation in Lexicon Design", | |
| "authors": [ | |
| { | |
| "first": "Branimir", | |
| "middle": [], | |
| "last": "Boguraev", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Lexical Systems Group IBM T.J. Watson Research Center Yorktown Heights", | |
| "institution": "", | |
| "location": { | |
| "postCode": "10598", | |
| "region": "New York" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "James", | |
| "middle": [], | |
| "last": "Pustejovsky", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "The traditional framework ['or ambiguity resolution employs only 'static' knowledge, expressed generally as selectional restrictions or domain specific constraints, and makes uo use of any specific knowledge manipulation mechanisms apart from the simple ability to match valences of structurally related words. In contraust, this paper suggests how a theory of lexical semantics making use of a knowledge representation framework offers a richer, more expressive vocabulary for lexical information. In particular, by performing specialized inference over the ways in which aspects of knowledge structures of words in context c~Ln be composed, mutually compatible and contextully relevant lexieal components of words and phrases are highlighted. In the view presented here, lexical ambiguity resolution is an integral part of the same procedure that creates the semantic interpretation of a sentence itself.", | |
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| "paper_id": "C90-2007", | |
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| "abstract": [ | |
| { | |
| "text": "The traditional framework ['or ambiguity resolution employs only 'static' knowledge, expressed generally as selectional restrictions or domain specific constraints, and makes uo use of any specific knowledge manipulation mechanisms apart from the simple ability to match valences of structurally related words. In contraust, this paper suggests how a theory of lexical semantics making use of a knowledge representation framework offers a richer, more expressive vocabulary for lexical information. In particular, by performing specialized inference over the ways in which aspects of knowledge structures of words in context c~Ln be composed, mutually compatible and contextully relevant lexieal components of words and phrases are highlighted. In the view presented here, lexical ambiguity resolution is an integral part of the same procedure that creates the semantic interpretation of a sentence itself.", | |
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| "section": "Abstract", | |
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| "text": "Our thesis below is that a theory of lexical semantics making use of a knowledge representation (KR) framework offers a richer, more expressive vocabulary ['or lexical information. Ultimately the goal of this research is to explain the creative use of language and highlight the role of a constantly evolving lexicon, while obviating the current prevalent views of 'static' lexicon design. A side effect of adopting such a theory as the basis of semantic interpretation is that some classically difftcult problems in ambiguity --. in particular, lexical --are resolved by viewing them from a different perspective. In an implementation such as that proposed here, lexical ambiguity resolution is an integral part of the same procedure that produces the semantic interpretation of a sentence. There are several methodological motivations for wanting to import tools developed for computational representation and manipulation of knowledge into the si, udy of word meaning, or lexical semantics. In particular, we believe that the goals of computational linguistics are the same as those of linguistics: to provide useful, testable and explanatory theories of the nature of language and its relation to human cognition a.s a whole. It follows that computational linguistics is linguistics as it should now be done, and that the computational tools developed and available in the larger context of Artificial Intelligence should not be ignored by linguists, l\"nrthermore, 'shifting' the application area of KR formalisms fi:om their traditional domain (general world knowledge) to a level below words (lexical knowledge), allows us to abstract the notion of lexical meaning away from world knowledge, as well as from other semantic influences (e.g. discourse and pragmatic factors); such a process of abstraction is a crucial prerequisite of any theory of lexieal meaning.", | |
| "cite_spans": [], | |
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| "section": "Importing Knowledge Representation into the Lexicon", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Bringing in KR tools provides us with several benefits, which are inst, rumental to enriching the semantics of lexical expressions. Firstly, it is now possible to systematically iucorpora.te world knowledge into the lexical entry, while still maintaining an awareness of the boundary between lexical and common sense knowledge. Secondly, it is also possible to reason over that knowledge, facilitating the construction of richer semantic interpretations. I\"inally, having developed a theory incorporating generalizations about the systematic patterning of words, we have a formal language for expressing these generalizations. The interplay of these capabilities results in a generative language for expressing the meanings of words, while providing a different way of capturing multiple word senses through richer composition. Together with a set of principles for lexical decomposition, whose central tenet is that semantic expressions for word meaning (in context) are constructed by a fixed number of generative devices (cf. Pustejovsky [1989b] ), this language becomes a tool for expressing lexical knowledge, while not presupposing finite enumeration of word senses.", | |
| "cite_spans": [ | |
| { | |
| "start": 1024, | |
| "end": 1048, | |
| "text": "(cf. Pustejovsky [1989b]", | |
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| "section": "Importing Knowledge Representation into the Lexicon", | |
| "sec_num": "1" | |
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| "text": "Current dictionaries reflect, through their organization, the traditional view of word senses; in particular, they assume that the space of possible/allowable uses of a word is exhaustively carved out by an enumerable set of senses ['or that word. Computational lexicons, to date, generally tend to follow this orga-nization. As a result, the natural language interl)retation tasks these lexica support acquire (or inherit) similar view to lexical ambiguity, which then necessitates a particular approach to disambiguation. Furthermore, dictionaries and lexicons currently are of a distinctly static nature: the division into separate word senses not only precludes permeability; it also fails to account for the creative use of words in novel contexts. In contrast, rather than taking a 'snapshot' of language at any moment of time and freezing it into lists of word sense specifications, the model of the lexicon proposed here does not preclude extendability: it is open-ended in nature and accounts for the novel, creative, uses of words in a variety of contexts by positing procedures for generating semantic expressions for words on the basis of particular contexts.", | |
| "cite_spans": [], | |
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| "section": "Importing Knowledge Representation into the Lexicon", | |
| "sec_num": "1" | |
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| "text": "In tbe remainder of this paper we will illustrate a particular theory of lexical semantics, following Pustejovsky [forthcoming] which promotes the notion of a generative lexicon. In particular, we brietly discuss certain types of lexical ambiguity, demonstrate how traditional methods of mnbiguity resolution fail to scale up for these (and other) cases, and then outline an approach to sernantie interpretation embodying richer methods of compositionality.", | |
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| "section": "Importing Knowledge Representation into the Lexicon", | |
| "sec_num": "1" | |
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| "text": "As we also show below, the lexical model we pro> pose has the effect of greatly reducing the size of the lexicon. Moreover, it bears directly on issues of o> ganization and content of computational lexicons, as the model now embodies strong assumptions about the kinds of lexical aspects of words essential for natural language processing. The generative theory of lexical semantics, then, imposes a strong focus on current efforts to extract lexical data from large online text resources (dictionaries and corpora): it not only offers a uniform representational framework for expressing the data extracted by the tools and methods of computational lexicography (cf. Boguraev and Briscoe, [1989] ), but also offers guidance on tl, e kinds of lexical data --or distinctions in ttle lexical behavior of words ---which should be sought in such resources (cf. Boguracv et al., [1990] ).", | |
| "cite_spans": [ | |
| { | |
| "start": 680, | |
| "end": 695, | |
| "text": "Briscoe, [1989]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 851, | |
| "end": 879, | |
| "text": "(cf. Boguracv et al., [1990]", | |
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| "section": "Importing Knowledge Representation into the Lexicon", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "One of the most pervasive phenomena in natural language, and one which every realistic language proccssing application faces, is that of ambiguity. Consequently, resolution of lexical ambiguity becomes an essential task, without which deeper (or perhaps any) language understanding and interpretation is impossible.", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "The Nature of Lexical Ambiguity", | |
| "sec_num": "2" | |
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| { | |
| "text": "As we pointed out earlier, all current computational lexicons lit into a particular processing framework for dealing with this problem. Assuming a partition.ing of the space of possible uses of a word into word senses --as postulated and defined by the entry for that word --the problem becomes that of selecting, on the basis of various contextual factors (typically subsumed by, but not necessarily limited to, the notion of selectional restrictions), the word sense closest to the use of the word in tile given text.. Computationally, this reduces to a search within a finite space of possibilities.", | |
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| "section": "Enumeration", | |
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| "text": "Realistically, however, this approach fails on several accounts --both in terms of what information is made available in a lexicon to drive the disambiguation process and how an autonlated sense selection procedure might make use of this information. Typically, external contextual factors alone are not sufficient for precise selection of a word sense; additionally, often the lexical entry does not provide enough reliable pointers to critically discriminate between word senses. Secondly, the search process becomes computationally expensive, if not in effect intractable, when it has to account for longer phrases made up of individually ambiguous words. Finally, the a~ssumption that an exhaustive listing can be assigned to the different uses of a word lacks the explanatory power necessary for making generalizations and/or predictions about how words used in a novel way can be reconciled with their currently existing lexical definitions.", | |
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| "text": "To illustrate this last. point, below we present some examples of problematic nature for current ambiguity resolution frameworks.", | |
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| "text": "Creative Use of Words Consider the ambiguity and context-dependence of adjectives such as fast and slow, where tile meaning of the predicate varies depending on the head being modified. Typically, a lexicon requires an enumeration of di/ferent senses for such words, in order to account for the ambiguity illustrated below: h a fast car: Ambiguous: a car driven quickly / one that is inherently fast. 2: a fast typist: the person performs the act of typing quickly, 3: a fast waltz: the motion of the dance is quick. 4: a fast book: one that calJ be read in ashort time, 5: a fast reader: one who reads quickly.", | |
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| "text": "These examples involve at least three distinct word senses for the word fasl:", | |
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| "sec_num": null | |
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| { | |
| "text": "fast(l):", | |
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| { | |
| "text": "to move quickly; fast(2) : to pertbrm some act quickly; :fast(3) : to do something that takes little time.", | |
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| "sec_num": null | |
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| "text": "(Note that in a real lexicon, word senses would be further annotated with selectional restrictions; these are omitted here for brevity.) Upon closer analysis, each occurrence of'fast abow., predicates in a slightly different way. In fact, any finite ennmeration of word senses will not account for creative applications of this adjective in the language. For example, fast in the phrase a fast motorway refers to the ability of vehicles on the motorway to sustain high speed. As a novel use of fast, we are clearly looking at a new sense that is not covered by the enumeration above.", | |
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| "text": "Permeability of Word Senses Part of our argument for a different organization of the lexicon is based on a claim that the boundaries between the word senses in the analysis of fast above are too rigid.", | |
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| "text": "Still, even if we assume that ennmeration is adequate as a descriptive mechanism, it is not always obvious how to select the correct word sense in any given context: conAder the systematic ambiguity of verbs like bake (discussed by Atkins el el., [1988] ), which require discrimination with respect to change-of-state versus create readings: 6: John baked the potato. 7: John baked the cake.", | |
| "cite_spans": [ | |
| { | |
| "start": 247, | |
| "end": 253, | |
| "text": "[1988]", | |
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| "text": "The problem here is that there is too much overlap in the 'core' semantic components of the different readings; hence, it is not possible to guarantee correct word sense selection on the basis of selectional restrictions alone. Furthermore, as language evolve~s, partial overlaps of core and peripheral components of different word meanings make the traditional notion of word ,~ense, as implemented in current dictionaries, inadequate (see Atkins [1990] for a critique of the tlat, linear enumeration-based organization of dictionary entries). The only feasible approach would be to employ considerably more refined distinctions in the semantic content of the complement than is conventionally provided by e.g. the mechanism of selectional restrictions.", | |
| "cite_spans": [ | |
| { | |
| "start": 441, | |
| "end": 454, | |
| "text": "Atkins [1990]", | |
| "ref_id": "BIBREF0" | |
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| "text": "Difference in Syntactic Forms It is equally arbitrary to create separate word senses for a lexical item just because it can participate in distinct syntactic realizations --and yet this has been the only approach open to computational lexicons which assume the ambiguity resolution framework outlined above. A striking example of this is provided by verbs such as believe and forget. Observe in (8--11) below that the syntactic realization of the complement determines both the factivity of the proposition in the complement or whether an NP is interpreted as a concealed question (see, for example, Grimshaw [1979] ).", | |
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| "text": "Grimshaw [1979]", | |
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| "text": "8: Mary forgot that she locked the door. 9: Mary forgot to lock the door. 10: Mary forgot the answer. 11: Mary forgot her wallet.", | |
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| "text": "Sensitivity to factivity would affect, for instance, the interpretation by a question-answering system, when asked Did Mary lock the door? Since sentence (8) is factive and (9) is nonfactive, the answers should be Yes and No respectively. Such a distinction could be easily accounted for by simply positing separate word senses for each syntactic type, but this misses the obvious relatedness between the two instances of forget. If it were possible to make the use of forget in (8) and (9) sensitive to the syntactic type of its complement, then we could also explain the parallel cases in (10) and (11). This would allow us to have essentially one definition for forget which could, by suitable composition with the different complement types, ~eneratc all the allowable readings (cf. Pustejovsky [ 1989a] ). 1", | |
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| "text": "[ 1989a]", | |
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| "text": "1 Note that such a treatment is different, in effect, from proposals to confiate more than one syntactic realization of the same word sense through the mechanism of type 2.2 Towards a Dynamic Model of the Lexicon The major thrust of our analysis has attempted to show that the ambiguities shown above cannot be adequately handled by exhaustive enumeration of what are regarded as different word senses. It follows that the conventional computational framework for lexical ambiguity resolution, and in particular, the format for lexical entries in current computational lexicons, fails in at lea,st two respects. It is unable to describe all the 'senses' of a word through finite enumeration; and it is also unable to capture interesting generalizations between 'senses' of the same word.", | |
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| "text": "Such failures are partially due to limited (lexical) knowledge made available to natural language processing systems, as well as to an impoverished notion of (lexical) inference. Thus, the traditional framework for ambiguity resolution only employs 'static' knowledge, expressed as e.g. selectional restrictions, and no specific knowledge manipulation mechanisms apart from the simple ability to match valences of connected words. In contrast, we show below how a lexical entry can be assigned a richer knowledge structure and how, by performing specialized inferencc over the ways in which aspects of knowledge structures of words in context can be composed, mutually compatible and relevant lexical components of words and phrases are highlighted. This process, licensed by constraints operating through the inference mechanislns, in fact, results in generating a semantic interpretation of a phrase, resolving en route the ambiguity of lexical items at their source.", | |
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| "text": "The richer structure for the lexical entry proposed here takes to an extreme the established notions of predicate-argument structure, primitive decompos/tion and conceptual organization; these are then viewed as defining a space of possible contexts in which a word can be used. Rather than committing to an enumeration of a pre-determined number of different word senses, a lexical entry for a word now encodes a range of deeper aspects of lexical meaning. Looking at a word in isolation, these meaning components simply denote the sernantic boundaries appropriate to its use. Viewing a word in the context of other words, mutually compatible aspects in the respective lexical decompositions become more prominent, thus forcing a specific interpretation of each individual word. It is important to realize that this is a generative process, which goes well beyond the simple matching of features. On the contrary, such a framework requires, in addition to a flexible notation for expressing semantic generalizations at the lexical level, a mechanism for composing these individual entries on the phrasal level.", | |
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| "section": "Ambiguity and CompositionaHty", | |
| "sec_num": "3" | |
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| { | |
| "text": "To get a better understanding of how the distinctions in lexical meaning manifest themselves, it is imcoercion -see Briscoe et el. [1990] for a computationa] approach, based on a suitably enriched lexical representation and utilizing the notions of type coercion (Pustejovsky, [1989a] ) and quzdia structure (see below). portant to study and detine the role that all lexical types play in contributing to the overall meaning of a phrase. Thi,~ is not just a methodological point: crucial to the processes of semantic interpretation which the lexicon is targeted for is the notion of composi-I.ionality, necessarily different from the more conventional pairing of e.g. verbs (as functions) and nouns (~ks arguments). As we indicated earlier, if the semantic load in the lexicon is entirely spread among the verb entries --as many existing computational systems assume ---differences like those exemplified in (6-7) and (8-11) can only be accounted for by treating bake, forget, and so forth as polysemous verbs.", | |
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| "start": 131, | |
| "end": 137, | |
| "text": "[1990]", | |
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| "end": 284, | |
| "text": "[1989a]", | |
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| "section": "Ambiguity and CompositionaHty", | |
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| "text": "lf, on the other hand, elaborate lexical meanings of e.g. verbs and adjectives could be made sensitive to componenLs of equally elaborate decompositions of e.g. nouns, the notion of spreading the semantic load evenly across the lexicon becomes the key organizing prirleiple in expressing the knowledge necessary for disambiguation.", | |
| "cite_spans": [], | |
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| "section": "Ambiguity and CompositionaHty", | |
| "sec_num": "3" | |
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| { | |
| "text": "In order to be able to express the distinctions, at lexical level, required for analyzing the examples ill the last section, it is necessary to go beyond viewing lexical decomposition as base.d only on a 1)rcdetermiued set of primitives; rather, what is needed is the con bmction of being able to sl;ecify (e.g. by means of sets of predicates) different levels, or per-~;pectives, of lexical representation alid being able to compose (via a fixed numbe:\" of generative devices) these predicates. A 'static' definition of a word now only provides its literal meaning; suitaMe compositions of apl)ropriately 'highlighted' [)rejections of (syntactically) related words, generate meanings in context.", | |
| "cite_spans": [], | |
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| "section": "Ambiguity and CompositionaHty", | |
| "sec_num": "3" | |
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| { | |
| "text": "In such a way, many of the short coufings in particular those from the perspeetiw', of automatic language processing -of the more descriptive approach inlmrent in exhaustive enunw.ration of word senses can be overcome. What makes this possible is the combination of two notions, both of them tbllowing from general principles of KIt theory. First, by positing a language R)r describing dif[crent levels of word meanings, we are no longer confined to the con-;:~traints following from operating with a fixed inventory of primitives; moreover, we now also haw.' a way of incorporating in this language l,he set of rules governing the generative processes. Secondly, through the very nature of these rules, we are assured that the ~:~emantic representations ultimately associated with text (fragments) are going to be well-formed.", | |
| "cite_spans": [], | |
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| "section": "Ambiguity and CompositionaHty", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "Pustejovsky [t989b] proposes several levels of lexical representation. Following an analysis of a broad range of (traditionally ambiguous) constructions, and in particular the aspects of word meanings which account for the ambiguities, he argues for four structures that a theory of computational lexical semantics needs to capture.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Lew?ls of Lexical Meaning", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Argmnent Structure This defines the c.ouventional mapping fi'om a word to a function, and relates the syntactic realization of a word to tile number and type of arguments that are identified at the level of syntax and made use of at tile level of semantics (cf. Grimshaw [1990] ).", | |
| "cite_spans": [ | |
| { | |
| "start": 262, | |
| "end": 277, | |
| "text": "Grimshaw [1990]", | |
| "ref_id": null | |
| } | |
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| "eq_spans": [], | |
| "section": "Lew?ls of Lexical Meaning", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Event Structure This identifies the particular event type for a ve,'b or a phrase. There are essentially three components to this structure: the primitive event type --state (S), process (p) or transition (T); the focus of the event; and the rules for event composition.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Lew?ls of Lexical Meaning", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Qualia Structure This defines the essential attributes of an object associated with a lexical item.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Lew?ls of Lexical Meaning", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "is, in essence, argument structure for nominals, nouns are elevated from the status of being passive arguments to active functions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By positing separate components (see below) in what", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Lexical Inheritance Structure This determines the way(s) in which a word is related to other concepts in tile lexicon. In addition to answering questions concerning the organization of a (lexical) knowledge base, this level of word meaning makes it possible to link lexieal knowledge with general world (common sense) knowledge.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "By positing separate components (see below) in what", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Since the only level of lexical representation not extensively discussed in the literature is that of qualia structure, we briefly outline its components below.", | |
| "cite_spans": [], | |
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| "section": "By positing separate components (see below) in what", | |
| "sec_num": null | |
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| "text": "The essence of tile proposal is that there is a system of relations that characterizes the semantics of' nominals, very much like the argument structure of a verb. Pustejovsky [1989b] calls this the Qualia Structure, inspired by Aristotle's theory of explanation and ideas from Moravesik [1975] . In effect, the qualia structure of a noun determines its meaning tus much as the list of arguments determines a verb's meaning. The elements that make up a qualia structure include notions such as container, space, surface, figure, artifact, and so on. 2 Briefly, the. Qualia Structure of a word specifies ['our tLspects of its meaning: o the relation between an object and its constituent parts; o that which distinguishes it within a larger do= main; o its purpose and fum;tion; o factors inw)lved iu its origin or \"bringing it, about\".", | |
| "cite_spans": [ | |
| { | |
| "start": 164, | |
| "end": 183, | |
| "text": "Pustejovsky [1989b]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 278, | |
| "end": 294, | |
| "text": "Moravesik [1975]", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Qualia Structure", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "These aspects of a word's meaning are called its", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Qualia Structure", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Constitutive Role, I'brmal Role, 2blic Role, and Agentive Role, respectively, a The motivation tbr >I'hese components of an object's denotation have long been considered crucial for our commonsense understanding of how things interact in the world. Cf. Hayes [1979] , tlobbs et aL [1987a], and Croft [1986] for discussion of the,;e qualitative aspects of me,thing.", | |
| "cite_spans": [ | |
| { | |
| "start": 259, | |
| "end": 265, | |
| "text": "[1979]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 281, | |
| "end": 293, | |
| "text": "[1987a], and", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 294, | |
| "end": 306, | |
| "text": "Croft [1986]", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Qualia Structure", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "agorae of these roles arc reminiscent of descriptors used by various comptttational researchers, such as Wilks [1975] , llayes [1979 [ ], ~nd Ilol,bs et al. [1987a . Within the theory outlined here, these roles determine a minimal semantic description of ~t word which has both semantic ~tnd gra.mm~tieal consequences.", | |
| "cite_spans": [ | |
| { | |
| "start": 105, | |
| "end": 117, | |
| "text": "Wilks [1975]", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 127, | |
| "end": 132, | |
| "text": "[1979", | |
| "ref_id": "BIBREF2" | |
| }, | |
| { | |
| "start": 133, | |
| "end": 163, | |
| "text": "[ ], ~nd Ilol,bs et al. [1987a", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Qualia Structure", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "positing such characterizations of word meaning is that by enriching the semantic descriptions of nominal types, we will be able to \"spread the semantic load\" more evenly through the lexicon, while accounting for novel word senses arising in syntactic composition.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Qualia Structure", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Let us examine how this view is able to account for the ambiguities discussed in the previous section. Consider first the example with fast We can capture the general behavior of how such adjectives predicate by making reference to the richer internal structure for nominals suggested above. That is, we can view fast as always predicating of the :1bile role of a nominal. To illustrate this, consider the qualia structure for a noun such ~us car:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Lexlcal Ambiguity Resolution", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "car(*x*)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Lexlcal Ambiguity Resolution", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "[Coast: Notice that the Tell( role specifies the purpose and function of the noun. In the phrase, a fast car, it is the relation specified there (seen as an event -namely, a process, P) which is modified by tile adjective as being fast. Similarly, for the nouns typist, waltz, book, and reader, it is their 'relic role that is interpreted as being fast (without going into details, we note here that the Telic role of lypis! determines the activity being performed, namely typing; similarly for waltz, its Telic role retZrs to (lancing). tlence, the interpretations of fast in the examples (1-5) above can all be derived from a siugle word sense, and there is no need for enumerating the different senses (cf. Pustejovsky [forthcoming] ). The lexical semantics for this adjective will indicate that it acts as an event predicate, modifying the 'relic role of the noun, as illustrated in the minimal lexical semantic structure for fast below:", | |
| "cite_spans": [ | |
| { | |
| "start": 722, | |
| "end": 735, | |
| "text": "[forthcoming]", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Lexlcal Ambiguity Resolution", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "fast(,x.) ~ (Tell(: AP3E[fast(E) A f)(E, .x.)])", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Lexlcal Ambiguity Resolution", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Notice that, in addition to obviating the need for separate senses, we can generate the novel use of fast mentioned above in the phrase a fast motorway, since the Tell( role of mot.orway specifies its purpose, and it is this activity which is interpreted ~ fast: The composition of the expression defining fast with the lexical aspect it specifies ms its 'target' --the 'relic role of its argument (motorway) -results in an interpretation corresponding to a use of the word when referring to a road: one that allows for fast travel by cars.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Lexlcal Ambiguity Resolution", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "The framework proposed above is very attractive for NLP, for at lea~st two reasons. Firstly, it can be formalized, and thus make the basis for a computational procedure for word interpretation in context. Secondly, it does not require the notion of exhaustive enumeration of all the different ways ill which a word can behave, in particular in collocations with other words. Consequently, the fi:amework can naturally cope with the 'creative' use of language; that is, the open-ended nature of word combinations and their associated meanings. The method of fine-grained characterization of lexical entries, as proposed here, effectively allows us to conflate different word senses (in the traditional meaning of this term) into a single meta-ent, ry, thereby offering great potential not only for systematically encoding regularities of word behavior dependent on context, but also for greatly reducing the size of the lexicon. The theoretical claim here is that such a characterization constrains what a possible word meaning can be, through the mechanism of logically well-formed semantic expressions. The expressive power of a Kl~. formalism can then be viewed as simply a tool which gives substance to this claim.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Implications for Natural Language Processing", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "So far we have looked at the \"classical\" problem of ambiguity of words, manifested in the problem of how to select suitable word senses for a word in running text, according to some notion of context. As we pointed out just now, the solution outlined in the previous section, in addition to offering an alternative way of approachng the problem, has the important 'side effect' on the size of the lexicon. In this section we address, at more depth, the question of how the techniq~es and methods of KR relate directly to the problem of lexical ambiguity resolution, the information to bear on it, and the methods for solving it. The discussion is carried out in the context of an alternative manifestation of the ,~ame problem, which we refer to as \"hiddell\" lexical ambiguity. We also use the shine context for presenting, intbrreally, some aspects of the lexical inheritance structure as another level of lexical meaning. 4", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Knowledge Representation and Global Organization of Lexicon", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "One of the implications of positing quails structures is the necessity to have, superimposed on the lexicon, a realization of more than one lattice structure. Earlier attempts at conceptual hierarchies faced this problem all the time: conceptual models typically make heavy use of multiple inheritance: \"book\" is_a \"literature\", \"book\" is_a \"object\", \"dictionary\" 2s_a \"object\", \"dictionary\" is_a \"reference\", \"car\" is_a \"vehicle .... car\" is_a \"artifact\", and so 4Introducing inheritance into the lexicon is not an entirely new idea. For example, Flickinger el al. [1985] discuss the value of inheritance as a representational device for capturing generalizations across classes of lexical entries. A further argument for the usefulness of inheritance mechanisms is provided by Briscoe et hi. [1990] , who show how a mechanism of lexieal inference can augment a text analysis systexn which performs syntactic analysis and semantic interpretation by making reference to detailed lexical decomposition of entries in the style of Pustejovsky [1989@ forth. Still, as descriptive as such relations may appear, models like these suffer from a very limited notion of lexical structure. Thus, even though elaborate mechanisms have been proposed to control and limit the flow of information along the e.g. generalization/ specialization links, there has been no theory to either (a) explain how to assign structure to lexical items, or (b) specify lexical relations between lexical items in terms of links between only certain ~pects of their respective lexical structures. Pustejovsky's theory of lexical semantics [1989b] , with its several distinct, levels of semantic description, and in particular the qualia structure, are relevant to just this issue.", | |
| "cite_spans": [ | |
| { | |
| "start": 566, | |
| "end": 572, | |
| "text": "[1985]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 779, | |
| "end": 800, | |
| "text": "Briscoe et hi. [1990]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1040, | |
| "end": 1046, | |
| "text": "[1989@", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1608, | |
| "end": 1615, | |
| "text": "[1989b]", | |
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| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Knowledge Representation and Global Organization of Lexicon", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "On this view, a lexical item inherits infornmtion according the qualia structure it carries. In this way, the different senses for words can be rooted into suitable lattices. To illustrate this point, consider the two is._a relations below, and the differences in what relations the objects enter into.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Knowledge Representation and Global Organization of Lexicon", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "play (is_a book) (dictionary is_a book) Lexical inheritance theory, oll the other hand, posits a separate lattice per role in the qualia structure. Briefly, inheritance through qualia amounts to the following relations for this exarnple: book is_form phys-object, book is_telic literature, dictionary is-~orm book, dictionary is_relic reference, book is._agent literature, dictionary is.agent compiled-material, play is_agent literature, play is_relic performance. With the qualia roles differentiating the lattice structures, we can exclude the unwanted inferences listed above.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Knowledge Representation and Global Organization of Lexicon", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "We have outlined a framework for lexical semantic research that we believe earl be uselifl for both cornpvtational linguists and theoretical linguists alike. We argued against the view that word meanings are fixed and inflexible, where lexical ambiguity must be treated by multiple word entries in the lexicon. Rather, the lexicon can be seen ~ a generative system, where word senses are related by logical operations defined by the well-formedness rules of the semantics. In this view, much of the lexical ambiguity of highly ambiguous lexical items is eliminated because the semantic load is spread more evenly throughout the lexicon to the other lexical categories; furthermore, the lexical knowledge we propose as neeessary for ambiguity resolution is seen ms factored out at different levels of lexical representation. We looked at two of these levels, qualia structnre and lexieal inheritance, as they turn out to be of particular relevance to the structuring of the semantic information carried by e.g. nouns and adjectives, and applying it, via composition, to the construction of semantic interpretation of complex expressions. The methods underlying the analysis of ambignous phrases and the construction of corresponding semantic expressions make extended use of Kt{. devices and techniques.", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": "5" | |
| } | |
| ], | |
| "back_matter": [], | |
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| "BIBREF0": { | |
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| "num": null, | |
| "urls": [], | |
| "raw_text": "Wilks, 1975] Y. Wilks. \"An Intelligent Analyzer and Understander for li;nglish\", CACM, 18, 264-274.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "FIGREF0": { | |
| "type_str": "figure", | |
| "uris": null, | |
| "text": "{body, engine .... }] car-shape (* x* ) ] move(P,*x*), drive(P,y,*x*)] artifact (*x*)]", | |
| "num": null | |
| }, | |
| "FIGREF1": { | |
| "type_str": "figure", | |
| "uris": null, | |
| "text": "Tdic : travd(P, cars) A on(P, *x*)].", | |
| "num": null | |
| } | |
| } | |
| } | |
| } |