Projection in Discourse a Data-Driven Formal Semantic Analysis

Total Page:16

File Type:pdf, Size:1020Kb

Projection in Discourse a Data-Driven Formal Semantic Analysis Projection in Discourse A data-driven formal semantic analysis Noortje Joost Venhuizen The work presented in this thesis was carried out under the auspices of the Center for Language and Cognition Groningen (CLCG) of the Faculty of Arts of the Uni- versity of Groningen. Groningen Dissertations in Linguistics 141 ISSN: 0928-0030 ISBN: 978-90-367-8071-1 (printed version) ISBN: 978-90-367-8070-4 (electronic version) c 2015, Noortje Joost Venhuizen Document prepared with LATEX 2" and typeset by pdfTEX (TEX Gyre Schola font) Cover design by Noortje Joost Venhuizen and Harm Brouwer Printed by Ridderprint BV, Ridderkerk. Printed on FSC certified paper. Projection in Discourse A data-driven formal semantic analysis Proefschrift ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen op gezag van de rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties. De openbare verdediging zal plaatsvinden op donderdag 26 november 2015 om 12.45 uur door Noortje Joost Venhuizen geboren op 19 oktober 1986 te Groningen Promotores Prof. dr. ing. J. Bos Prof. dr. P. Hendriks Beoordelingscommissie Prof. dr. N. M. Asher Prof. dr. J. Hoeksema Prof. dr. R. A. van der Sandt Voor Harm En voor Kees, aan wie ik dit werk zo graag had willen laten lezen Acknowledgements Bovenaan het lijstje met mensen die ik wil bedanken voor hun steun en inzet tijdens de ontwikkeling van dit proefschrift staan natuurlijk mijn twee promotores en directe begeleiders: Johan Bos en Petra Hendriks. Johan, jij hebt mij geleerd data-driven te denken, en de waarde daarvan in te zien. Vandaar ook een data-driven aanpak tot mijn dankwoord aan jou. Eerste observatie: jouw deur staat altijd open. Tweede observatie: je bent enorm enthousiast over wat je doet, en dat werkt aanstekelijk. Derde observatie: je bent open en toegankelijk, ook buiten werktijd om (bijvoorbeeld voor een borrel, of een lift van Groningen naar Saarbrüc- ken). Hieruit kan ik maar één conclusie trekken: heel erg bedankt voor een prettige en inspirerende samenwerking! Petra, jij bent wat later bij mijn promotietraject betrokken geraakt, maar dat doet niet af aan de invloed die je hebt gehad op de ontwikkeling van dit proefschrift en van mijzelf. Ik heb veel geleerd van jouw gedetailleerd en constructief com- mentaar, en heb onze uitgebreide gesprekken over ‘de wetenschap en meer’ altijd erg gewaardeerd. Ontzettend bedankt voor je waardevolle adviezen, ik hoop van harte dat we onze regelmatige Skype-gesprekken ook in de toekomst nog zullen voortzetten! Next, I would like to express my gratitude to Nick Asher, Jack Hoek- sema, and Rob van der Sandt, who participated in my reading commit- tee. I highly value each of your opinions, and therefore I feel honoured that you have taken the time to read my dissertation, and have deemed it scientifically sound (@Rob: I hope that the name of the implementa- tion “pdrt-sandbox” did not go unnoticed). There is a number of people vii viii from the scientific community that I have talked to about my work and that have contributed to it in different ways. In this context, I would especially like to thank Bart Geurts, Larry Horn, Hans Kamp, Andrew Kehler, and Emar Maier. I have been very lucky to work within a close group of colleagues, who have warmed many office hours, lunches, and the not-too-occasional ‘vrijdagmiddagborrel’ at Hugo’s jazz café. For this, I would like to thank the entire Informatiekunde department: Dieke, George, Gert- jan, Gosse, Gregory, Hessel, Johan, Johannes, John, Kilian, Leonie, Malvina, Martijn, Rob, Simon, and Valerio. Special thanks go to Kil- ian, Valerio and Johannes, who were my direct collaborators in Johan’s group, my very ‘gezellige’ roommates, as well as great company out- side the office, of course together with their respective (to be) spouses Sara and Vivian. Furthermore, there are various colleagues outside Informatiekunde who I have really enjoyed working and/or chatting with. Thanks, Albert, Aletta, Alice, Dicky, Jennifer, John, Karin, Mar- ijke, Mark, Meriel, and Ryanne. This last year, I have been warmly welcomed by the colleagues of the Universität des Saarlandes in Saar- brücken. Many thanks to Matt Crocker, for so generously providing me with a place in his department, and to the other colleagues there: Clau- dia, Elizabeth, Elli, Francesca, Heiner, Jesús, Les, Lisa, Mindaugas, Sahri, Torsten and Yoana. Ook buiten het werk om is er een aantal mensen dat ik graag zou willen bedanken voor de nodige ontspanning en relativering tijdens de afgelopen vier jaar. Lieve Lotte en Ben, jullie zijn een baken voor mij, en het is altijd een feest om met jullie te eten, te praten, en te lachen. Lieve Merel, Lieke en Lodi, hoewel ons contact soms wat (te) schaars is, is jul- lie vriendschap enorm belangrijk voor mij. Lieve Bas, Emmy, Frank, Manja, Mark, Peter, Richard, en Sytze, bedankt voor het mij zo makke- lijk opnemen in jullie warme vriendenclub. En ook de volgende mensen zijn op verschillende punten erg belangrijk geweest: Bedankt, John, Catharina, Anna en David, Lotte en Jeroen, Nienke, Wouter, Sanne, en Theo. Op deze plek zou ik graag mijn paranimfen speciaal willen be- danken. Kilian, ik heb het altijd heel erg prettig gevonden om met jou samen te werken: of het nou gaat om grote wetenschappelijke vraag- stukken, kleine problemen oplossen, of organisatorische zaken, jouw ix toewijding, zorgvuldigheid, en geduld heb ik altijd erg gewaardeerd. Niet alleen als collega, maar ook als goede vriend. Emmy, wij kennen elkaar nog niet zo heel erg lang, maar het voelt al vanaf het begin ont- zettend vertrouwd om met elkaar om te gaan. Ik vind het heerlijk om zo nu en dan een kijkje te krijgen in jullie gezinsleven, en om te praten over zowel de grote als de kleine dingen in het leven. Bedankt allebei, ik ben er trots op jullie als mijn paranimfen te hebben! Tenslotte wil ik hier mijn familie noemen, hoewel een ‘bedankt’ niet helemaal recht doet aan mijn gevoel ten opzichte van hen. Elly, Tonnis en Netty, betere ouders had ik mij niet kunnen wensen. Lieve broer Guus, en Karin, voor mij is ons contact ontzettend waardevol. Ik ben heel blij dat ik ook Wiebout en Richelle, en Sjoerdieke, Charles, Chris- topher en Simon tot mijn familie mag rekenen. Mijn skoanfamylje Oe- bele, Siska en Sytze, bij wie ik mij al vanaf dag één thuis heb gevoeld. En natuurlijk Harm. Uit het feit dat jij hebt meegewerkt aan een groot deel van de hoofdstukken in dit proefschrift blijkt al dat jij voor mij meer bent dan mijn man. Je bent mijn ijkpunt, mijn inspiratie, en mijn drijfveer. Je bent mijn alles. Saarbrücken, september 2015 Contents Contents x List of Figures xv List of Tables xvi 1 Introduction 1 1.1 Projection in Discourse . 2 1.2 Data-driven formal semantics . 3 1.3 About this thesis . 3 1.4 Publications . 5 I Projection Phenomena in Discourse 7 2 Projection phenomena and information status 9 2.1 Introduction . 9 2.2 Projection phenomena . 11 2.2.1 Presuppositions . 11 2.2.1.1 Presupposition triggers . 12 2.2.1.2 The projection problem . 14 2.2.2 Anaphora . 15 2.2.2.1 Discourse anaphora . 16 2.2.2.2 Presuppositions as anaphora . 17 2.2.3 Conventional implicatures . 18 x Contents xi 2.3 The information status of projected content . 21 2.3.1 The structure of communication . 22 2.3.2 Categorizing information status . 24 2.3.3 Related categorizations . 28 2.4 Conclusions . 31 II Projective Discourse Representation Theory 33 3 Toward a formal treatment of projection 35 3.1 Introduction . 35 3.2 Projection in Discourse . 37 3.2.1 Discourse Representation Theory . 37 3.2.2 Presuppositions as anaphora . 40 3.2.3 Layered DRT . 43 3.3 Projective Discourse Representation Theory . 45 3.3.1 Projection as variable binding . 45 3.3.2 Preliminaries . 46 3.3.3 PDRS composition . 47 3.3.4 Projection in PDRT . 49 3.3.4.1 Asserted versus projected content . 50 3.3.4.2 Conventional Implicatures . 51 3.3.5 Comparison with related approaches . 52 3.4 Translation PDRT to DRT . 53 3.4.1 Translation procedure . 54 3.4.2 Example translation . 55 3.5 Conclusions and future work . 56 4 How and why conventional implicatures project 59 4.1 Introduction . 60 4.2 The projection behavior of conventional implicatures . 61 4.2.1 CIs, presuppositions, and at-issue content . 63 4.2.2 CI projection as anchoring . 65 4.3 Toward a formal framework of projection . 69 4.3.1 Projective Discourse Representation Theory . 70 4.3.2 Presuppositions in PDRT . 71 xii Contents 4.4 Implementing the projection behavior of CIs . 72 4.4.1 CI anchoring in PDRT . 73 4.4.2 Predicting CI infelicity in PDRT . 74 4.4.3 Expressive CIs and speaker-orientedness . 76 4.5 Discussion . 79 4.6 Conclusion . 81 5 Aligning discourse semantics with surface structure 83 5.1 Introduction . 84 5.2 Toward a formal framework of projection . 86 5.2.1 Discourse Representation Theory . 86 5.2.2 Beyond the surface structure . 88 5.2.3 Introducing PDRT . 91 5.2.3.1 Representing projected content . 91 5.2.3.2 Non-hierarchical variable binding . 94 5.3 Projective Discourse Representation Theory . 95 5.3.1 Syntax of PDRSs . 95 5.3.2 Binding and Accessibility in PDRT . 98 5.3.2.1 Projection graph . 100 5.3.2.2 Binding of projection variables . 102 5.3.2.3 Binding of projected referents . 103 5.3.3 Structural properties . 104 5.3.4 Composition in PDRT .
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]