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<?xml version="1.0" standalone="yes"?> <Paper uid="A92-1003"> <Title>AN APPROACH TO MULTILEVEL SEMANTICS FOR APPLIED SYSTEMS</Title> <Section position="1" start_page="0" end_page="0" type="metho"> <SectionTitle> AN APPROACH TO MULTILEVEL SEMANTICS FOR APPLIED SYSTEMS </SectionTitle> <Paragraph position="0"> IRST, Istituto per la Ricerca Scientifica e Tecnologica I - 38050 Povo TN, Italy</Paragraph> </Section> <Section position="2" start_page="0" end_page="0" type="metho"> <SectionTitle> Abstract </SectionTitle> <Paragraph position="0"> Multilevel semantics has been proposed as a powerful architecture for semantic analysis. We propose a methodology that, while maintaining the generality of the multilevel approach, is able to establish formal constraints over the possible ways to organize the level hierarchy. More precisely, we propose a &quot;strong&quot; version of the multilevel approach in which a level can be defined if and only if it is possible to characterize a &quot;meaningfulness&quot; notion peculiar to that level. Within such an architecture each level reached during the analysis computes its meaningfulness value; this result is then handled according to modalities that are peculiar to that level.</Paragraph> <Paragraph position="1"> The component described in this paper was designed to be portable with respect to the application domain and so far has been tested as the semantic analysis component of two multimedial dialog systems, ALFresco and MAIA.</Paragraph> </Section> <Section position="3" start_page="0" end_page="17" type="metho"> <SectionTitle> 1. Introduction </SectionTitle> <Paragraph position="0"> Multilevel semantics has been proposed \[Scha, 1983\] as a powerful architecture for semantic analysis. In this approach, interpreting a natural language sentence is a multi-stage process, which starts out with a high-level meaning representation that reflects the semantic structure of the sentence rather directly. Then translation rules, which specify how the language-oriented semantic primitives relate to those used at deeper levels of analysis, are applied. One of the advantages of the multilevel approach is that it allows a natural decomposition of complex tasks and the functional modularization of semantic analysis. However, when multilevel architecture is used in concrete applications, a simple functional approach does not solve the problem of a clear definition of the semantics for each level. This fact is evident for applied systems whose semantic component must deal with many linguistic phenomena (e.g. lexical and structural ambiguities, quantifier scoping, anaphorical references, discourse topic and focus, referent retrieval, etc.). In such systems the definition of the semantics for a level has at least two advantages: (i) modules for specific phenomena could be easily introduced within the appropriate level, provided that the module functions contribute to the definition of the semantics for that level; (ii) a better understanding of the semantic analysis would be allowed: particularly, when a sentence is rejected at a certain level, it would mean that the semantic constraints for that level have been violated.</Paragraph> <Paragraph position="1"> In this paper we suggest a methodology that, while maintaining the generality of the multilevel approach, is able to establish formal constraints over the possible ways to organize the level hierarchy. More precisely, we propose a &quot;strong&quot; version of the multilevel approach in which a level can be defined if and only if it is possible to characterize a &quot;meaningfulness&quot; notion peculiar to that level. Within such an architecture each level reached during the analysis computes its meaningfulness value; this result is then handled according to modalities that are peculiar to that level.</Paragraph> <Paragraph position="2"> We shall show how our approach to multilevel semantics is concretely applied to organize the semantic component developed by the NLP group at IRST; this component is currently responsible for semantic analysis in two dialog systems, ALFresco and MAIA. At present two levels are included in the semantic component and they will be described in detail: the lexical level and the logical-interpretative level. At the lexical level the meaningfulness is defined by the consistency notion, which is computed by means of the lexical discrimination module; this module tries to select only the sentence readings meaningful in a given Domain Model (DM). When the propositional content of the sentence is proven to be consistent, the semantic representation produced by this level is passed to the next one; otherwise, if consistency cannot be proved, the whole sentence is rejected. At the logical-interpretative level the meaningfulness is defined by means of the validity notion, which is satisfied when referents for the sentence are identified. Three modules interact at this level: the quantification module, which finds the correct interpretation of the quantifiers, resolving possible scoping ambiguities; the topic module, which organizes the mentioned referents; the interpretation module, which identifies the part of the sentence to extensionalize and is responsible for referent retrieval. At this level, when validity cannot be proved, a special pragmatic procedure is activated.</Paragraph> <Paragraph position="3"> Section 2 surveys a few relevant approaches to multilevel semantic analysis. In Section 3 the formal requirements for the &quot;strong&quot; multilevel semantics version are introduced. The architecture and the functional modules of the two levels of the semantic component we have developed are described in Sections 4 and 5. Finally, Section component has been used and Section 7 outlines some future developments.</Paragraph> </Section> <Section position="4" start_page="17" end_page="17" type="metho"> <SectionTitle> 2. Multilevel Semanlics Applied </SectionTitle> <Paragraph position="0"> One of the first and most direct multilevel-based systems is the BBN spoken language system \[Boisen et al., 1989\].</Paragraph> <Paragraph position="1"> At every level of analysis, the meaning of an input utterance is represented as an expression of a logical language; the languages used at the various levels of analysis differ in that at every level the descriptive constants are chosen so as to correspond to the semantic primitives assumed at that level.</Paragraph> <Paragraph position="2"> At the highest semantic level, the meaning of an input utterance is represented as an expression of the English-oriented Formal Language (EFL). The constants of EFL correspond to the descriptive terms of English. An important feature of EFL is that descriptive constants are allowed to be ambiguous. The logical language used at the domain-dependent level of representation is called the World Model Language (WML). This is an unambiguous language, with an ordinary model-theoretic interpretation. Its constants are chosen to correspond to the concepts that constitute the domain of discourse. During the crossing of the EFL and the WML level (when domain dependent rewriting rules are called), the discrimination process is carried out. A type checking mechanism provides acceptance only for interpretations for which a domain knowledge compatible type has been computed. A further step of translation occurs when the WML is translated into DBL (DataBase Language) used to access the database to retrieve appropriate answers.</Paragraph> <Paragraph position="3"> While having sound theoretical foundations, the main drawback of this approach is that it postpones semantic discrimination until domain knowledge is available; in the meantime, a complete sentence representation is built for each analysis the parser produces. However, IRUS-II \[Ayuso et al., 1989\], an applicative system also developed at BBN, confirms that in a real system it is useful to connect the discrimination process to the parser. It implements a rule system that translates each syntactic constituent directly into a WML form, skipping the domain independent level of representation. While this solution improves system efficiency, lexical discrimination is carried out by domain dependent rules in a way that limits system modularity.</Paragraph> <Paragraph position="4"> Another system with a clear distinction between the domain independent and the domain dependent level is XTRA \[Allgayer et al., 19891. However, in this case at each level .the same language (i.e. the knowledge representation language SB-ONE) is used. The domain independent level, called Functional-Semantic Structure (FSS), is intended as an intermediate structure that incorporates linguistic knowledge, substantially invariant in respect to the particular application domain. On the contrary, the domain dependent level, called Conceptual Knowledge Base (CKB), is necessary to adequately model the relations of the underlying expert system. In XTRA it is necessary that each analysis produced by the parser is consistent with the FSS level: this is achieved by means of a classification of the sentence instance with the SB-ONE mechanisms (the realizer and the matcher). If the classification succeeds, the analysis goes on to the CKB level, otherwise the syntactic analysis is rejected. In this approach the discriminatiol process is profitably anticipated, and a powerful (eve1 though computationally expensive) consistency checkinl mechanism is provided.</Paragraph> <Paragraph position="5"> Both systems exploit the difference between knowledg about the application domain and knowledge that i independent of the particular domain (e.g., linguisti knowledge). Although this distinction is relevant fc allowing portability to different application domains, th semantic component described here focuses on the effecl that domain dependent knowledge has on the type checkin mechanism.</Paragraph> <Paragraph position="6"> To make the problem clearer, let us consider ho~ domain knowledg e is exploited in the systems ju~, described. In the BBN spoken language system the typ checking is carried on by means of domain knowledge; o the other hand, within the XTRA system the discriminatio process is based only on domain independent knowledge. W think that an effective discrimination process should also b based on the application domain, it being unclear how t assign a proper meaning to a sentence without having fixe a particular context. Moreover, it seems useful to considc lexical discrimination as an incremental process: i discrimination works in parallel with the parser, it i possible to discriminate over single syntactic phraset checking the semantic content of each phrase.</Paragraph> <Paragraph position="7"> From the previous remarks, it can be noted that system that employ the multilevel semantics approach can assig the same functionalities to different levels. Hence, it coul be useful trying to define the relations among each level in &quot;stronger&quot; way, facing the problem of coherenc maintenance.</Paragraph> </Section> <Section position="5" start_page="17" end_page="18" type="metho"> <SectionTitle> 3. Definitions of Meaningfulness </SectionTitle> <Paragraph position="0"> We have seen that in a multilevel semantics approac the main idea is to divide different functionalities int distinct levels. We propose a &quot;strong&quot; approach to such methodology in which for each level the definition of semantics is required. This is achieved by means of th assignment of a proper meaningfulness notion that defin~ the semantic behavior of the level. In other words a level i a multilevel semantics hierarchy can be identified, if an only if it is possible to characterize a meaningfulness notic peculiar to that level. We have defined theoretically such notion for two levels: the lexical level and logica interpretative level (called consistency and validit respectively).</Paragraph> <Paragraph position="1"> Let T be a theory of types that models our domain, l our multilevel semantics the notion of consistency is meal to demonstrate that an expression, representing tl~ propositional content of a sentence, has type; i.e. given expression w, it means to assign a type, if possible, to according to our type system. An expression has n meaning at the lexical level, if the type checking fails.</Paragraph> <Paragraph position="2"> Validity, i.e. the meaningfulness at interpretation leve means to give a description of the objects of the tyl: suggested by the lexical level. Such a description can be i terms of relations, sets or intensional expressior (mandatory for infinite denotations). An expression has r meaning at the logical-interpretative level if such description cannot be found.</Paragraph> <Paragraph position="3"> As the meaning of a sentence is always relative to a level in the multilevel architecture, every level manages the acceptance or the rejection of a sentence in a different manner. As examples: (1) A mule paints a fresco The components of the sentence have the following types: a mule : Mule, a fresco : Fresco, to paint : Painter --9 Painting.</Paragraph> <Paragraph position="4"> Given the fact that &quot;mules cannot paint&quot; (only painters can), the type checking mechanism fails to assign an appropriate type and this causes the meaningfulness for the lexical level not to be satisfied.</Paragraph> <Paragraph position="5"> (2) Show me a work painted by all the painters born in Florence Sentence 2 satisfies the lexical level, but not the logical-interpretative one, because no description of the referents of the sentence can be proposed, i.e. there is no painting painted by all the painters born in Florence.</Paragraph> <Paragraph position="6"> Once the functionalities of the levels are theoretically stated, the implementative choices can be very different and subject to criteria of portability. Type checking can be made using logical formalisms such as typed ~,-calculi or intensional logics (possibly exploiting Curry-Howard's isomorphism between typed ~.-terms and intuitionistic logic \[Hindley and Seldin, 1986\]). The interpretation level can retrieve the referred elements using functional applications or some algebraic formalisms. However, these approaches, although well founded, may not be the right ones from an implementative point of view, especially for large integrated systems. For example one has to define 'a priori' a theory of admissible types but when the domain changes, the theory does too. Another way is to use a hybrid knowledge representation system. As will be clear in the next section, we refer to a terminological component (Tbox) in order to obtain the type checking and to an assertional component (Abox) in order to retrieve the relations that verify the analyzed expression. This choice allows us to parameterize the type checking according to the knowledge representation. Indeed the portability of the modules encourages this alternative. Another possibility (to be explored) is to use a data base instead of the Abox, exploiting relational data theories.</Paragraph> </Section> <Section position="6" start_page="18" end_page="19" type="metho"> <SectionTitle> 4. Lexical Level </SectionTitle> <Paragraph position="0"> The semantic component (see Figure 1) interacts with both a parser and a hybrid knowledge representation system that includes the domain knowledge. As we have already mentioned, the semantic component consists of two levels and each level includes one or more specialized modules. In the following we will give a description of the functionalities of the various levels and modules of the semantic component.</Paragraph> <Paragraph position="1"> The lexical level \[Lavelli and Magnini, 1991\] incrementally interacts with the parser: whenever the parser tries to build a (partially recognized) constituent, the discrimination module is triggered to check the consistency of the semantic part of such a constituent.</Paragraph> <Section position="1" start_page="18" end_page="19" type="sub_section"> <SectionTitle> 4.1. Lexicon </SectionTitle> <Paragraph position="0"> The discrimination module uses semantic information from two different sources: lexical entries (which are domain dependent) and phrase-structure rules (which are domain independent). The representation produced by this module constitutes the input for the quantification module (at the logical-interpretative level) and is still neutral with respect to quantifier scopings.</Paragraph> <Paragraph position="1"> Each lexical entry, along with the usual syntactic information (such as the lexical category of the word, the specification of the subcategorization frame of the entry, the superficial linguistic function that each subcategorized element holds) specifies a semantic representation and a mapping between syntactic functions and semantic functions. In such a way, within the semantic representation the syntactic distinction between the word complements (i.e. the arguments) and its adjuncts (i.e. its modifiers) is preserved.</Paragraph> <Paragraph position="2"> As an example consider the simplified lexical entry for the verbal form &quot;dipinse&quot;, painted (past tense) (see Figure 2). Morphological analysis enriches the information associated with the root and is able (for example in the case of passive) to change the mapping between linguistic functions and semantic functions. The semantic part of the lexical entry is built using the domain model knowledge (see Section 7 for a discussion on the portability problem) and it includes one (or more) semantic descriptions (this allows words with the same syntactic behavior, but different semantics, to be dealt with). Each semantic description contains the name of the DM concept (paint) associated with the word, along with its roles, which have a syntactic realization as arguments of the word and their restrictions (in this case, agent with restriclion painter and goal with restriction painting).</Paragraph> <Paragraph position="3"> dipinse category: V lingfunctions: ((subj agent) (obj goal)) <other syntactic information> semantics: ((paint ((agent painter x) As for the rules, they also include both a syntactic and a semantic part. In the semantic part, the consistency is computed and the construction of the semantic representation is carried out. During this process, possible ambiguities taken from lexical items are reduced.</Paragraph> </Section> <Section position="2" start_page="19" end_page="19" type="sub_section"> <SectionTitle> 4.2. Consistency checking </SectionTitle> <Paragraph position="0"> We define the consistency check operation such that it succeeds if selectional-restriction (i.e. the concept that represents the selectional restriction of a given argument position) denotes a concept that is compatible with the concept that semantic-head (i.e. the concept associated with the constituent which has to fill such a position) denotes.</Paragraph> <Paragraph position="1"> There exist several possibilities to check the compatibility between two concepts within a terminological hierarchy. Within the JANUS system \[Weischedel, 1989\] the consistency is implemented by means of a double subsumption check that guarantees success both when semantic-head is a descendant of selectional-restriction and when it is an ancestor. This double subsumption test does not consider the cases, sometimes relevant, in which semantic-head is a brother concept of selectional-restriction (e.g. &quot;Has a sculptor painted a fresco?&quot;); this case recursively extends to all the cases in which semantic-head is a brother either of a descendant or of an ancestor for a selectional-restriction (e.g. &quot;Which object did Giotto paint?&quot;). This case is slightly more complex than the others. In fact, while it is always true that along the ISA hierarchy there can be a non-empty intersection between two concepts, this is not true for concepts that are brothers. If an explicit disjoinmess is placed between two brother concepts, there cannot be a common intersection and the consistency procedure must fail; otherwise it is assumed that a common intersection can exist, and the consistency-test procedure will succeed. KR languages with disjointness are usually provided with a specific predicate holding between two concepts when their intersection is empty. It is worth noting that this predicate includes all the subsumption cases among concepts, in which cases it is always false.</Paragraph> <Paragraph position="2"> Now we will illustrate how the whole process works using Sentence (3) (in the rest of the paper, all the examples refer to the DM knowledge in Figure 3; we will use &quot;concept&quot; characters to indicate DM objects): (3) 'Mostrami tutti gli affreschi dipinti da Giotto in un monumento di Padova' Show me all the frescoes painted by Giotto in a monument of Padova In this sentence there is a typical case of ambiguity, that of the preposition 'di' (of); at least two senses for 'di' are possible in DM: the spatial interpretation, in which the mapping is to the spatial-location role, and the temporal interpretation, in which the mapping is to the temporal-location role. The selection of the right interpretation (the spatial one) is carried out through the application of the consistency check between the argument selectional-restrictions (the domain and the range of a role) and the semantic-head that tries to fill the position. In this case the temporal interpretation is rejected (it does not satisfy the meaningfulness notion for the lexical level) because the range restriction (time-period)is not consistent with the proposed semantic-head (Padova).</Paragraph> <Paragraph position="3"> 4.3. First logical form The final result of the lexical level is a form that uses a predicate-argument notation that allows abstracting from time and context. Omitting for the moment the intensional aspects, four relevant constructs for the resolution of role at interpretation level (see Section 5.3). A demonstrterm has the task of representing a demonstrative NP. The representation has to take into account the possible multimediality that the system treats at this level (the touch on the touchscreen for a deictic reference). A pronoun-term represents a pronoun. The lexical level gives a suggestion with <pred-restriction> on the type of semantic restriction that the bound variable can have. Then this information will be used by the interpretation module. The <features> keep syntactic information of the NP ready for use in the interpretation module.</Paragraph> <Paragraph position="4"> (show hearer</Paragraph> <Paragraph position="6"> The resulting form produced by the lexical level for Sentence (3), omitting the <features> information, is shown in Figure 4.</Paragraph> </Section> </Section> class="xml-element"></Paper>