DOCSLIB.ORG

A Computational Semantics for Natural Language

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Computational Semantics for Natural Language A Computational Semantics for Natural Language Lewis G. Creary and Carl J. Pollard Hewlett-Packard Laboratories 1501 Page Mill Road Palo Alto, CA 94304, USA Abstract a. a system of conceptual representation for internal use as a computational medium in processes of information In the new Head-driven Phrase Structure Grammar retrieval, inference, planning, etc. (HPSG) language processing system that is currently under b. a system of linkages between expressions of the natural development at Hewlett-Packard Laboratories, the language and those of the conceptual representation, Montagovian semantics of the earlier GPSG system (see and [Gawron et al. 19821) is replaced by a radically different approach with a number of distinct advantages. In place c. a system of linkages between expressions in the concep- of the lambda calculus and standard first-order logic, our tual representation and objects, relations, and states of medium of conceptual representation is a new logical for- affairs in the external world. realism called NFLT (Neo-Fregean Language of Thought); [n this paper, we shall concentrate almost exclusively on compositional semantics is effected, not by schematic the first two components. We shall sketch our ontologi- lambda expressions, but by LISP procedures that operate cal commitments, describe our internal representation lan- on NFLT expressions to produce new expressions. NFLT guage, explain how our grammar (and our computer im- has a number of features that make it well-suited {'or nat- plementation) makes the connection between English and ural language translations, including predicates of variable the internal representations, and finally indicate the present arity in which explicitly marked situational roles supercede status and future directions of our research. order-coded argument positions, sortally restricted quan- tification, a compositional (but nonextensional) semantics Our internal representation language. NFLT. is due to that handles causal contexts, and a princip[ed conceptual Creary 119831. The grammatical theory in which the present raising mechanism that we expect to lead to a computation- research is couched is the theory of head grammar (HG) set ally tractable account of propositional attitudes. The use forth in [Pollard 1984] and [Pollard forthcoming i and imple- of semantically compositional LiSP procedures in place of mented as the front end of the HPSG (Head-driven Phrase lambda-schemas allows us to produce fully reduced trans- Structure Grammar) system, an English [auguage database lations on the fly, with no need for post-processing. This query system under development at Hewlett-Packard Lab- approach should simplify the task of using semantic infor- oratories. The non-semantic aspects of the implementation mation (such as sortal incompatibilities) to eliminate bad are described in IFlickinger, Pollard, & Wasow t9851 and [Proudian & Pollard 1.9851. parse paths. 2. Ontological Assumptions I. Introduction To get started, we make the following assumptions Someone who knows a natural language is able to use about what categories of things are in the world. utterances of certain types to give and receive information about the world, flow can we explain this? We take as a. There are individuals. These include objects of the our point of departure the assumption that members of a usual kind (such as Ron and Nancy) as well as situations. language community share a certain mental system -- a Situations comprise states (such as Ron's being tall) and grammar -- that mediates the correspondence between ut- events (such as Ron giving his inaugural address on January terance types and other things in the world, such as individ- 21, 1985). u~ds, relations, and states of ~ffairs, to a large degree, this b. There are relations (subsuming properties). Exam- system i~ the language. According to the relation theory ples are COOKIE (= the property of being a cookie) and BUY of meaning (Barwise & Perry !1983!), linguistic meaning is (= the relation which Nancy has to the cookies she buys). a relation between types of utterance events and other as- Associated with each relation is a characteristic set of roles pects of objective reality. We accept this view of linguistic appropriate to that relation (such as AGENT, PATIENT, LO- meaning, but unlike Barwise and Perry we focus on how the CATION, etc.) which can be filled by individuals. Simple meaning relation is mediated by the intersubjective psycho- situations consist of individuals playing roles in relations. logical system of grammar. Unlike properties and relations in situation semantics [n our view, a computational semantics ['or a natural [Barwise & Perry 1983[, our relations do not have fixed ar- language has three essential components: ity (number of arguments). This is made possible by taking 172 explicit account of roles, and has important linguistic con- 3. The Internal sequences. Also there is no distinguished ontological cate- Representation Language: NFLT gory of locations~ instead, the location of an event is just the individual that fillsthe LOCATION role. The translation of input sentences into a logical for- c. Some relations are sortal relations, or sorts. Associ- malism of some kind is a fairly standard feature of com- ated with each sort {but not with any non-sortal relation) puter systems for natural-language understanding, and one is a criterion of identity for individuals of that sort [Coc- which is shared by the HPSG system. A distinctive feature chiarella 1977, Gupta 1980 I. Predicates denoting sorts oc- of this system, however, is the particular logical formalism cur in the restrictor-clanses of quantifiers (see section 4.2 involved, which is called NFLT (Neo-Fregean Language of below), and the associated criteria of identity are essential Thought). 2 This is a new logical language that is being to determining the truth values of quantified assertions. developed to serve as the internal representation medium in computer agents with natural language capabilities. The Two important sorts of situations are states and events. language is the result of augmenting and partially reinter- One can characterize a wide range of subsorts of these preting the standard predicate calculus formalism in sev- (which we shall call situation types) by specifying a par- eral ways, some of which will be described very briefly in ticular configuration of relation, individuals, and roles. For this section. Historically, the predicate calculus was de- example, one might consider the sort of event in which Ron ve|oped by mathematical logicians as an explication of the kisses Nancy in the Oval Office, i.e. in which the relation is logic of mathematical proofs, in order to throw light on KISS, Ron plays the AGENT role, Nancy plays the PATIENT the nature of purely mathematical concepts and knowledge. role, and the Oval Office plays the LOCATION role. One Since many basic concepts that are commonplace in natu- might also consider the sort of state in which Ron is a per- ral language (including concepts of belief, desire, intention, son, i.e. in which the relation is PERSON, and Ron plays the INSTANCE role. We assume that the INSTANCE role is temporal change, causality, subjunctive conditionality, etc.) play no role in pure mathematics, we should not be espe- appropriate only for sortal relations. cially surprised to find that the predicate calculus requires d. There are concepts, both subjective and objective. supplementation in order to represent adequately and natu- Some individuals are information-processing organisms that rally information involving these concepts. The belief that use complex symbolic objects (subjective concepts) as com- such supplementation is needed has led to the design of putational media for information storage and retrieval, in- NFLT, ference, planning, etc. An example is Ron's internal rep- resentation of the property COOKIE. This representation While NFLT is much closer semantically to natural lan- in turn is a token of a certain abstract type ~'COOKIE, guage than is the standard predicate calculus, and is to an objective concept which is shared by the vast majority some extent inspired by psycho[ogistic considerations, it of speakers of English. t Note that the objective concept is nevertheless a formal logic admitting of a mathemati- cally precise semantics. The intended semantics incorpo- ~COOKIE, the property COOKIE, and the extension of that rates a Fregean distinction between sense and denotation, property (i.e. the set ofall cookies) are three distinct things associated principles of compositionality, and a somewhat that play three different roles in the semantics of the Eng- non-Fregean theory of situations or situation-types as the lish noun cookie. denotations of sentential formulas. e. There are computational processes in organisms for manipulating concepts e.g. methods for constructing com- 3.1. Predicates of Variable Arity plex concepts from simpler ones, inferencing nmchanisms, etc. Concepts of situations are called propositions; organ- Atomic formulas in NFLT have an explicit ro[e-marker isms use inferencing mechanisms to derive new propositions for each argument; in this respect NFLT resembles seman- from old. To the extent that concepts are accurate repre- tic network formalisms and differs from standard predicate sentations of existing things and the relations in which they t We regard this notion of obiective concept as the appro- stand, organisms can contain information. We call the sys- priate basis on which to reconstruct, ia terms of informa- tem of objective concepts and concept-manipulating mech- tion processing, Saussure's notions of ~ignifiant (signifier) anisms instantiated in an organism its conceptual ~ystem. and #ignifig (signified) [1916!, as well an Frege's notion of Communities of organisms can share the same conceptual Sinn (sense, connotation) [1892 I. system. ~" The formalism is called ~neo-Fregean" because it in- f. Communities of organisms whose common concep- corporates many of the semantic ideas of Gottlob Frege, tual system contains a subsystem of a certain kind called though it also departs from Frege's ideas in several signif- a grammar can cornnmnicate with each other.
Load more

Recommended publications
  • The Generative Lexicon
    The Generative Lexicon James Pustejovsky" Computer Science Department Brandeis University In this paper, I will discuss four major topics relating to current research in lexical seman- tics: methodology, descriptive coverage, adequacy of the representation, and the computational usefulness of representations. In addressing these issues, I will discuss what I think are some of the central problems facing the lexical semantics community, and suggest ways of best ap- proaching these issues. Then, I will provide a method for the decomposition of lexical categories and outline a theory of lexical semantics embodying a notion of cocompositionality and type coercion, as well as several levels of semantic description, where the semantic load is spread more evenly throughout the lexicon. I argue that lexical decomposition is possible if it is per- formed generatively. Rather than assuming a fixed set of primitives, I will assume a fixed number of generative devices that can be seen as constructing semantic expressions. I develop a theory of Qualia Structure, a representation language for lexical items, which renders much lexical ambiguity in the lexicon unnecessary, while still explaining the systematic polysemy that words carry. Finally, I discuss how individual lexical structures can be integrated into the larger lexical knowledge base through a theory of lexical inheritance. This provides us with the necessary principles of global organization for the lexicon, enabling us to fully integrate our natural language lexicon into a conceptual whole. 1. Introduction I believe we have reached an interesting turning point in research, where linguistic studies can be informed by computational tools for lexicology as well as an appre- ciation of the computational complexity of large lexical databases.
    [Show full text]
  • Deriving Programming Theories from Equations by Functional Predicate Calculus
    Calculational Semantics: Deriving Programming Theories from Equations by Functional Predicate Calculus RAYMOND T. BOUTE INTEC, Ghent University The objects of programming semantics, namely, programs and languages, are inherently formal, but the derivation of semantic theories is all too often informal, deprived of the benefits of formal calculation “guided by the shape of the formulas.” Therefore, the main goal of this article is to provide for the study of semantics an approach with the same convenience and power of discov- ery that calculus has given for many years to applied mathematics, physics, and engineering. The approach uses functional predicate calculus and concrete generic functionals; in fact, a small part suffices. Application to a semantic theory proceeds by describing program behavior in the simplest possible way, namely by program equations, and discovering the axioms of the theory as theorems by calculation. This is shown in outline for a few theories, and in detail for axiomatic semantics, fulfilling a second goal of this article. Indeed, a chafing problem with classical axiomatic semantics is that some axioms are unintuitive at first, and that justifications via denotational semantics are too elaborate to be satisfactory. Derivation provides more transparency. Calculation of formulas for ante- and postconditions is shown in general, and for the major language constructs in par- ticular. A basic problem reported in the literature, whereby relations are inadequate for handling nondeterminacy and termination, is solved here through appropriately defined program equations. Several variants and an example in mathematical analysis are also presented. One conclusion is that formal calculation with quantifiers is one of the most important elements for unifying contin- uous and discrete mathematics in general, and traditional engineering with computing science, in particular.
    [Show full text]
  • A Survey of Computational Semantics: Representation, Inference and Knowledge in Wide-Coverage Text Understanding Johan Bos* University of Groningen
    Language and Linguistics Compass 5/6 (2011): 336–366, 10.1111/j.1749-818x.2011.00284.x A Survey of Computational Semantics: Representation, Inference and Knowledge in Wide-Coverage Text Understanding Johan Bos* University of Groningen Abstract The aim of computational semantics is to capture the meaning of natural language expressions in representations suitable for performing inferences, in the service of understanding human language in written or spoken form. First-order logic is a good starting point, both from the representation and inference point of view. But even if one makes the choice of first-order logic as representa- tion language, this is not enough: the computational semanticist needs to make further decisions on how to model events, tense, modal contexts, anaphora and plural entities. Semantic representa- tions are usually built on top of a syntactic analysis, using unification, techniques from the lambda-calculus or linear logic, to do the book-keeping of variable naming. Inference has many potential applications in computational semantics. One way to implement inference is using algo- rithms from automated deduction dedicated to first-order logic, such as theorem proving and model building. Theorem proving can help in finding contradictions or checking for new infor- mation. Finite model building can be seen as a complementary inference task to theorem proving, and it often makes sense to use both procedures in parallel. The models produced by model generators for texts not only show that the text is contradiction-free; they also can be used for disambiguation tasks and linking interpretation with the real world. To make interesting inferences, often additional background knowledge is required (not expressed in the analysed text or speech parts).
    [Show full text]
  • Proceedings of the LFG11 Conference Miriam Butt and Tracy Holloway King (Editors) 2011
    Proceedings of LFG11 Miriam Butt and Tracy Holloway King (Editors) 2011 CSLI Publications http://csli-publications.stanford.edu/ Contents 1 Editors’ Note 4 2 I Wayan Arka: Constructive Number Systems in Marori and Beyond 5 3 Doug Arnold and Louisa Sadler: Resource Splitting and Reintegration with Supplementals 26 4 Rajesh Bhatt, Tina Bögel, Miriam Butt, Annette Hautli, and Sebastian Sulger: Urdu/Hindi Modals 47 5 Adams Bodomo: Reflexivity without Apparent Marking: The Case of Mashan Zhuang 68 6 George Aaron Broadwell, Gregg Castellucci, and Megan Knickerbocker: An Optimal Approach to Partial Agreement in Kaqchikel 89 7 Maris Camilleri and Louisa Sadler: Restrictive Relative Clauses in Maltese 110 8 Damir Cavar and Melanie Seiss: Clitic Placement, Syntactic Disconti- nuity, and Information Structure 131 9 Mary Dalrymple: A Very Long-Distance Anaphor? 152 10 Mary Dalrymple and Louise Mycock: The Prosody-Semantics Inter- face 173 11 Yehuda Falk: Multiple-Gap Constructions 194 12 Anna Gazdik and András Komlósy: On the Syntax-Discourse Inter- face in Hungarian 215 13 Gianluca Giorgolo and Ash Asudeh: Multidimensional Semantics with Unidimensional Glue Logic 236 14 Gianluca Giorgolo and Ash Asudeh: Multimodal Communication in LFG: Gestures and the Correspondence Architecture 257 15 Dag Trygve Truslew Haug: Backward Control in Ancient Greek - Sub- sumption or Linearization? 278 16 Tibor Laczkó and György Rákosi: On Particularly Predicative Parti- cles in Hungarian 299 17 Leslie Lee and Farrell Ackerman: Mandarin Resultative Compounds: A Family
    [Show full text]
  • Detecting Semantic Difference: a New Model Based on Knowledge and Collocational Association
    DETECTING SEMANTIC DIFFERENCE: A NEW MODEL BASED ON KNOWLEDGE AND COLLOCATIONAL ASSOCIATION Shiva Taslimipoor1, Gloria Corpas2, and Omid Rohanian1 1Research Group in Computational Linguistics, University of Wolverhampton, UK 2Research Group in Computational Linguistics, University of Wolverhampton, UK and University of Malaga, Spain {shiva.taslimi, omid.rohanian}@wlv.ac.uk [email protected] Abstract Semantic discrimination among concepts is a daily exercise for humans when using natural languages. For example, given the words, airplane and car, the word flying can easily be thought and used as an attribute to differentiate them. In this study, we propose a novel automatic approach to detect whether an attribute word represents the difference between two given words. We exploit a combination of knowledge-based and co- occurrence features (collocations) to capture the semantic difference between two words in relation to an attribute. The features are scores that are defined for each pair of words and an attribute, based on association measures, n-gram counts, word similarity, and Concept-Net relations. Based on these features we designed a system that run several experiments on a SemEval-2018 dataset. The experimental results indicate that the proposed model performs better, or at least comparable with, other systems evaluated on the same data for this task. Keywords: semantic difference · collocation · association measures · n-gram counts · word2vec · Concept-Net relations · semantic modelling. 1. INTRODUCTION Semantic modelling in natural language processing requires attending to both semantic similarity and difference. While similarity is well-researched in the community (Mihalcea & Hassan, 2017), the ability of systems in discriminating between words is an under-explored area (Krebs et al., 2018).
    [Show full text]
  • Syntactic and Semantic Improvements to Computational Metaphor Processing
    Syntactic and Semantic Improvements to Computational Metaphor Processing by Kevin Stowe B.A., Michigan State University, 2009 M.A., Indiana University, 2011 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirement for the degree of Doctor of Philosophy Department of Linguistics 2019 This thesis entitled: Syntactic and Semantic Improvements to Computational Metaphor Processing written by Kevin Stowe has been approved for the Department of Linguistics Martha Palmer James H. Martin Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii Stowe, Kevin (Phd., Linguistics) Syntactic and Semantic Improvements to Computational Metaphor Processing Thesis directed by Professors Martha Palmer & Jim Martin Identifying and interpreting figurative language is necessary for comprehensive nat- ural language understanding. The main body of work on computational metaphor pro- cessing is based in lexical semantics. We’ve seen recent evidence that syntactic construc- tions play a part in our production and comprehension of metaphors; the goal of this work is to identify areas where these theories can improve metaphor processing. This is done be exploiting dependency parses, syntax-based lexical resources, and distant su- pervision using linguistic analysis. Through these methods we show improvements over state-of-the-art deep learning models in a variety of metaphor processing tasks. iv Dedicated to my parents, Ron and Kristine, who made all of this possible. v Acknowledgements I would first and foremost like to thank my advisors Martha and Jim for all of their help making this possible, and providing me the opportunity to pursue this interesting path of research.
    [Show full text]
  • The Computational Lexical Semantics of Syntagmatic Expressions
    The Computational Lexical Semantics of Syntagmatic Relations Evelyne Viegas, Stephen Beale and Sergei Nirenburg New Mexico State University Computing Research Lab, Las Cruces, NM 88003, USA viegas, sb, sergei©crl, nmsu. edu Abstract inheritance hierarchy of Lexical Semantic Functions In this paper, we address the issue of syntagmatic (LSFs). expressions from a computational lexical semantic perspective. From a representational viewpoint, we 2 Approaches to Syntagmatic argue for a hybrid approach combining linguistic and Relations conceptual paradigms, in order to account for the Syntagmatic relations, also known as collocations, continuum we find in natural languages from free are used differently by lexicographers, linguists and combining words to frozen expressions. In particu- statisticians denoting almost similar but not identi- lar, we focus on the place of lexical and semantic cal classes of expressions. restricted co-occurrences. From a processing view- The traditional approach to collocations has been point, we show how to generate/analyze syntag- lexicographic. Here dictionaries provide infor- matic expressions by using an efficient constraint- mation about what is unpredictable or idiosyn- based processor, well fitted for a knowledge-driven cratic. Benson (1989) synthesizes Hausmann's stud- approach. ies on collocations, calling expressions such as com- 1 Introduction mit murder, compile a dictionary, inflict a wound, etc. "fixed combinations, recurrent combinations" You can take advantage o] the chambermaid 1 is not a or "collocations". In Hausmann's terms (1979) a collocation one would like to generate in the context collocation is composed of two elements, a base ("Ba- of a hotel to mean "use the services of." This is why sis") and a collocate ("Kollokator"); the base is se- collocations should constitute an important part in mantically autonomous whereas the collocate cannot the design of Machine Translation or Multilingual be semantically interpreted in isolation.
    [Show full text]
  • Intensions As Computable Functions Shalom Lappin1
    Intensions as Computable Functions Shalom Lappin1 Classical intensional semantic frameworks, like Montague’s Intensional Logic (IL), identify intensional identity with logical equivalence. This criterion of co-intensionality is excessively coarse-grained, and it gives rise to several well known difficulties. Theories of fine-grained inten- sionality have been been proposed to avoid this problem. Several of these provide a formal solution to the problem, but they do not ground this solution in a substantive account of intensional di↵erence. Apply- ing the distinction between operational and denotational meaning, developed for the semantics of programming languages, to the inter- pretation of natural language expressions, o↵ers the basis for such an account. It permits us to escape some of the complications generated by the traditional modal characterization of intensions. 1 Introduction Classical intensional semantic representation languages, like Montague (1974)’s Intensional Logic (IL) do not accommodate fine-grained inten- sionality. Montague, following Carnap (1947), characterizes intensions as functions from worlds (indices of worlds and times) to denotations, and so reduces intensional identity to equivalence of denotation across possible worlds. Logically equivalent expressions are semantically indis- tinguishable. This is too course a criterion for semantic identity. Logical equivalence is not a sufficient condition for intersubstitutability in all contexts. (1) a. Every prime number is divisible only by itself and 1. <=> b. If A B and B A,thenA = B. ✓ ✓ 1King’s College London LiLT Volume 9 Perspectives on Semantic Representations for Textual Inference. Copyright c 2014, CSLI Publications. 229 230 /ShalomLappin (2) a. John believes that every prime number is divisible only by itself and 1.
    [Show full text]
  • Introducing Computational Semantics for Natural Language Understanding in Conversational Nutrition Coaches for Healthy Eating †
    Proceedings Introducing Computational Semantics for Natural Language Understanding in Conversational Nutrition Coaches for Healthy Eating † Antonio Benítez-Guijarro *, Zoraida Callejas, Manuel Noguera and Kawtar Benghazi Department of Languages and Computer Systems, University of Granada, 18071 Granada, Spain; [email protected] (Z.C.); [email protected] (M.N.); [email protected] (K.B.) * Correspondence: [email protected]; Tel.: +34-958-241000 (ext. 20049) † Presented at the 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018. Published: 18 October 2018 Abstract: Nutrition e-coaches have demonstrated to be a successful tool to foster healthy eating habits, most of these systems are based on graphical user interfaces where users select the meals they have ingested from predefined lists and receive feedback on their diet. On one side the use of conversational interfaces based on natural language processing allows users to interact with the coach more easily and with fewer restrictions. However, on the other side natural language introduces more ambiguity, as instead of selecting the input from a predefined finite list of meals, the user can describe the ingests in many different ways that must be translated by the system into a tractable semantic representation from which to derive the nutritional aspects of interest. In this paper, we present a method that improves state-of-the-art approaches by means of the inclusion of nutritional semantic aspects at different stages during the natural language understanding processing of the user written or spoken input. The outcome generated is a rich nutritional interpretation of each user ingest that is independent of the modality used to interact with the coach.
    [Show full text]
  • An Introduction to Formal Computational Semantics
    An Introduction to Formal Computational Semantics CS224N/Ling 280 – Christopher Manning May 23, 2000; revised 2005 1 Motivation We have seen ways to extract and represent elements of meaning in statistical and other quan- titative ways (word sense disambiguation, semantic similarity, information extraction tem- plates). But there are many NLP domains, such as automated question answering, dialog systems, story understanding, and automated knowledge base construction, where we need to deal with the meanings of sentences in a more sophisticated and precise (yet robust) way. Characterizing what we mean by meaning is a difficult philosophical issue, but the only thing implementation-ready as a system of representation seems to be the tradition of logical seman- tics, appropriately extended to natural language (Montague 1973). I believe that a promising approach for many domain-embedded applications is to use the benefits of statistical models for disambiguation at a lexical/syntactic level, and then to use logical semantic representations for detailed interpretations. In computational semantics, we can isolate the following questions: 1. How can we automate the process of associating semantic representations with expres- sions of natural language? 2. How can we use semantic representations of natural language expressions in the process of drawing inferences? We will concentrate on the first goal. This is for two reasons. Firstly, this issue is normally ignored in logic or knowledge representation and reasoning classes, whereas the second is covered in depth. Secondly, it’s the mapping from language to a meaning representation that is most interesting. One way to do inference with a meaning representation is using logical proofs, but we can also explore quite different methods such as probabilistic models.
    [Show full text]
  • Ontology and Description in Computational Semantics: Models
    Ontology and Description in Computational Semantics: Models and Processes Matthew Stone Computer Science and Cognitive Science Rutgers University Piscataway NJ 08854-8019 [email protected] Abstract make to application reasoning [Artale et al., 1996].Therich- ness of current domain ontologies calls for flexible links be- This paper argues that natural language meanings tween language and concepts. In fact, human speakers don’t should be modeled as DESCRIBING an underly- come to language with a fixed correspondence between, say, ing domain ontology rather than CORRESPOND- verbs and actions, either; people use different verbs to de- ING to it. Theoretically, description provides an at- scribe the same action and the same verbs to describe differ- tractive explanation for the flexibility and context- ent actions [Furnas et al., 1987]. Instead, it seems that people sensitivity of language use in complex domains. link language to concepts creatively based on the task at hand, Practically, description offers a convenient and gen- and interactively come to agree on this conceptualization over eral way to relax algorithmic assumptions so that the course of each conversation [Brennan, 2000].Thisflex- natural language semantics can be connected to do- ibility has been emphasized in many linguistic models, such main ontologies in less constrained ways. I use as Jackendoff’s PREFERENCE SEMANTICS [1983] or Lakoff case studies in natural language generation and de- and colleagues’ models of METAPHOR in language interpre- scriptions of categories to suggest how this gen- tation [1980]. It seems to originate in the genuine complexity eral framework provides an attractive computa- and flexibility with which agents must understand the world tional setting in which to pursue applied models of to act successfully in it.
    [Show full text]
  • CS 181: Natural Language Processing Semantic Ambiguity
    Semantic Ambiguity CS 181: Natural Language Processing Some ambiguities arise at semantic level Lecture 17: Computational Semantics have same parse trees, but different meanings Kim Bruce Every student read a book. Pomona College They each picked their own. Spring 2008 Some liked it, while others did not. Disclaimer: Slide contents borrowed from many sources on web! What about other “Obvious” Semantics meaning? [[Every student read a book]] = [[Every student]] ([[read a book]]) Montague [1973]: Rewrite sentence: A book, every student read it. [[Every student]] = !Q.!x(student(x)"Q(x)) “It” creates a hole to be filled: [[read a book]] = !s:D. #y(book(y) $ read(s,y)) [[every student read it]] = !!!! !z:D.!x.(student(x) " read(x,z)) [[Every student]] ([[read a book]]) [[a book]] = !P.#y.(book(y) $ P(y)) with type = !x(student(x)"(!s:D. #y(book(y) $ read(s,y)))(x)) VPType % Form. = !x(student(x)"#y(book(y) $ read(x,y))) Putting it Together Other Solutions Cooper Storage: “Freeze” meanings for quantifiers, pull out when [[A book, every student read it]] needed. (See book for similar idea) = (!P.#y.(book(y) $ P(y))) !!! (!z:D.!x.(student(x) " read(x,z))) Results in saving multiple meanings. = #y.(book(y) $ (!z:D.!x.(student(x) " Doesn’t work with nested noun phrases !!!!!!!!! read(x,z)))(y))) “Jane read every book of a teacher.” = #y.(book(y) $!x.(student(x) " read(x,y))) Keller suggested an improvement Seems like a trick! !!! Hole semantics incorporates constraints Graphical representation representing constraints. Semantic Ambiguity More attempted solutions: Quasi-Logical Form, Underspecified Logical Form, Underspecified Discourse Representation Theory, Minimal Recursion Semantics, Ontological Promiscuity, Hole Semantics, the Logic in NLTK Constraint Language for Lambda Structures, and Normal Dominance Constraints Sentences w/N quantifiers have up to N! meanings.
    [Show full text]