Combined Distributional and Logical Semantics
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Semantics and Pragmatics
Semantics and Pragmatics Christopher Gauker Semantics deals with the literal meaning of sentences. Pragmatics deals with what speakers mean by their utterances of sentences over and above what those sentences literally mean. However, it is not always clear where to draw the line. Natural languages contain many expressions that may be thought of both as contributing to literal meaning and as devices by which speakers signal what they mean. After characterizing the aims of semantics and pragmatics, this chapter will set out the issues concerning such devices and will propose a way of dividing the labor between semantics and pragmatics. Disagreements about the purview of semantics and pragmatics often concern expressions of which we may say that their interpretation somehow depends on the context in which they are used. Thus: • The interpretation of a sentence containing a demonstrative, as in “This is nice”, depends on a contextually-determined reference of the demonstrative. • The interpretation of a quantified sentence, such as “Everyone is present”, depends on a contextually-determined domain of discourse. • The interpretation of a sentence containing a gradable adjective, as in “Dumbo is small”, depends on a contextually-determined standard (Kennedy 2007). • The interpretation of a sentence containing an incomplete predicate, as in “Tipper is ready”, may depend on a contextually-determined completion. Semantics and Pragmatics 8/4/10 Page 2 • The interpretation of a sentence containing a discourse particle such as “too”, as in “Dennis is having dinner in London tonight too”, may depend on a contextually determined set of background propositions (Gauker 2008a). • The interpretation of a sentence employing metonymy, such as “The ham sandwich wants his check”, depends on a contextually-determined relation of reference-shifting. -
Reference and Sense
REFERENCE AND SENSE y two distinct ways of talking about the meaning of words y tlkitalking of SENSE=deali ng with relationshippggs inside language y talking of REFERENCE=dealing with reltilations hips bbtetween l. and the world y by means of reference a speaker indicates which things (including persons) are being talked about ege.g. My son is in the beech tree. II identifies persons identifies things y REFERENCE-relationship between the Enggplish expression ‘this p pgage’ and the thing you can hold between your finger and thumb (part of the world) y your left ear is the REFERENT of the phrase ‘your left ear’ while REFERENCE is the relationship between parts of a l. and things outside the l. y The same expression can be used to refer to different things- there are as many potential referents for the phrase ‘your left ear’ as there are pppeople in the world with left ears Many expressions can have VARIABLE REFERENCE y There are cases of expressions which in normal everyday conversation never refer to different things, i.e. which in most everyday situations that one can envisage have CONSTANT REFERENCE. y However, there is very little constancy of reference in l. Almost all of the fixing of reference comes from the context in which expressions are used. y Two different expressions can have the same referent class ica l example: ‘the MiMorning St’Star’ and ‘the Evening Star’ to refer to the planet Venus y SENSE of an expression is its place in a system of semantic relati onshi ps wit h other expressions in the l. -
The Meaning of Language
01:615:201 Introduction to Linguistic Theory Adam Szczegielniak The Meaning of Language Copyright in part: Cengage learning The Meaning of Language • When you know a language you know: • When a word is meaningful or meaningless, when a word has two meanings, when two words have the same meaning, and what words refer to (in the real world or imagination) • When a sentence is meaningful or meaningless, when a sentence has two meanings, when two sentences have the same meaning, and whether a sentence is true or false (the truth conditions of the sentence) • Semantics is the study of the meaning of morphemes, words, phrases, and sentences – Lexical semantics: the meaning of words and the relationships among words – Phrasal or sentential semantics: the meaning of syntactic units larger than one word Truth • Compositional semantics: formulating semantic rules that build the meaning of a sentence based on the meaning of the words and how they combine – Also known as truth-conditional semantics because the speaker’ s knowledge of truth conditions is central Truth • If you know the meaning of a sentence, you can determine under what conditions it is true or false – You don’ t need to know whether or not a sentence is true or false to understand it, so knowing the meaning of a sentence means knowing under what circumstances it would be true or false • Most sentences are true or false depending on the situation – But some sentences are always true (tautologies) – And some are always false (contradictions) Entailment and Related Notions • Entailment: one sentence entails another if whenever the first sentence is true the second one must be true also Jack swims beautifully. -
Two-Dimensionalism: Semantics and Metasemantics
Two-Dimensionalism: Semantics and Metasemantics YEUNG, \y,ang -C-hun ...:' . '",~ ... ~ .. A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Philosophy In Philosophy The Chinese University of Hong Kong January 2010 Abstract of thesis entitled: Two-Dimensionalism: Semantics and Metasemantics Submitted by YEUNG, Wang Chun for the degree of Master of Philosophy at the Chinese University of Hong Kong in July 2009 This ,thesis investigates problems surrounding the lively debate about how Kripke's examples of necessary a posteriori truths and contingent a priori truths should be explained. Two-dimensionalism is a recent development that offers a non-reductive analysis of such truths. The semantic interpretation of two-dimensionalism, proposed by Jackson and Chalmers, has certain 'descriptive' elements, which can be articulated in terms of the following three claims: (a) names and natural kind terms are reference-fixed by some associated properties, (b) these properties are known a priori by every competent speaker, and (c) these properties reflect the cognitive significance of sentences containing such terms. In this thesis, I argue against two arguments directed at such 'descriptive' elements, namely, The Argument from Ignorance and Error ('AlE'), and The Argument from Variability ('AV'). I thereby suggest that reference-fixing properties belong to the semantics of names and natural kind terms, and not to their metasemantics. Chapter 1 is a survey of some central notions related to the debate between descriptivism and direct reference theory, e.g. sense, reference, and rigidity. Chapter 2 outlines the two-dimensional approach and introduces the va~ieties of interpretations 11 of the two-dimensional framework. -
Acquaintance Inferences As Evidential Effects
The acquaintance inference as an evidential effect Abstract. Predications containing a special restricted class of predicates, like English tasty, tend to trigger an inference when asserted, to the effect that the speaker has had a spe- cific kind of `direct contact' with the subject of predication. This `acquaintance inference' has typically been treated as a hard-coded default effect, derived from the nature of the predicate together with the commitments incurred by assertion. This paper reevaluates the nature of this inference by examining its behavior in `Standard' Tibetan, a language that grammatically encodes perceptual evidentiality. In Tibetan, the acquaintance inference trig- gers not as a default, but rather when, and only when, marked by a perceptual evidential. The acquaintance inference is thus a grammaticized evidential effect in Tibetan, and so it cannot be a default effect in general cross-linguistically. An account is provided of how the semantics of the predicate and the commitment to perceptual evidentiality derive the in- ference in Tibetan, and it is suggested that the inference ought to be seen as an evidential effect generally, even in evidential-less languages, which invoke evidential notions without grammaticizing them. 1 Introduction: the acquaintance inference A certain restricted class of predicates, like English tasty, exhibit a special sort of behavior when used in predicative assertions. In particular, they require as a robust default that the speaker of the assertion has had direct contact of a specific sort with the subject of predication, as in (1). (1) This food is tasty. ,! The speaker has tasted the food. ,! The speaker liked the food's taste. -
Scope Ambiguity in Syntax and Semantics
Scope Ambiguity in Syntax and Semantics Ling324 Reading: Meaning and Grammar, pg. 142-157 Is Scope Ambiguity Semantically Real? (1) Everyone loves someone. a. Wide scope reading of universal quantifier: ∀x[person(x) →∃y[person(y) ∧ love(x,y)]] b. Wide scope reading of existential quantifier: ∃y[person(y) ∧∀x[person(x) → love(x,y)]] 1 Could one semantic representation handle both the readings? • ∃y∀x reading entails ∀x∃y reading. ∀x∃y describes a more general situation where everyone has someone who s/he loves, and ∃y∀x describes a more specific situation where everyone loves the same person. • Then, couldn’t we say that Everyone loves someone is associated with the semantic representation that describes the more general reading, and the more specific reading obtains under an appropriate context? That is, couldn’t we say that Everyone loves someone is not semantically ambiguous, and its only semantic representation is the following? ∀x[person(x) →∃y[person(y) ∧ love(x,y)]] • After all, this semantic representation reflects the syntax: In syntax, everyone c-commands someone. In semantics, everyone scopes over someone. 2 Arguments for Real Scope Ambiguity • The semantic representation with the scope of quantifiers reflecting the order in which quantifiers occur in a sentence does not always represent the most general reading. (2) a. There was a name tag near every plate. b. A guard is standing in front of every gate. c. A student guide took every visitor to two museums. • Could we stipulate that when interpreting a sentence, no matter which order the quantifiers occur, always assign wide scope to every and narrow scope to some, two, etc.? 3 Arguments for Real Scope Ambiguity (cont.) • But in a negative sentence, ¬∀x∃y reading entails ¬∃y∀x reading. -
Compositional and Lexical Semantics • Compositional Semantics: The
Compositional and lexical semantics Compositional semantics: the construction • of meaning (generally expressed as logic) based on syntax. This lecture: – Semantics with FS grammars Lexical semantics: the meaning of • individual words. This lecture: – lexical semantic relations and WordNet – one technique for word sense disambiguation 1 Simple compositional semantics in feature structures Semantics is built up along with syntax • Subcategorization `slot' filling instantiates • syntax Formally equivalent to logical • representations (below: predicate calculus with no quantifiers) Alternative FS encodings possible • 2 Objective: obtain the following semantics for they like fish: pron(x) (like v(x; y) fish n(y)) ^ ^ Feature structure encoding: 2 PRED and 3 6 7 6 7 6 7 6 2 PRED 3 7 6 pron 7 6 ARG1 7 6 6 7 7 6 6 ARG1 1 7 7 6 6 7 7 6 6 7 7 6 6 7 7 6 4 5 7 6 7 6 7 6 2 3 7 6 PRED and 7 6 7 6 6 7 7 6 6 7 7 6 6 7 7 6 6 2 3 7 7 6 6 PRED like v 7 7 6 6 7 7 6 6 6 7 7 7 6 6 ARG1 6 ARG1 1 7 7 7 6 6 6 7 7 7 6 6 6 7 7 7 6 6 6 7 7 7 6 ARG2 6 6 ARG2 2 7 7 7 6 6 6 7 7 7 6 6 6 7 7 7 6 6 6 7 7 7 6 6 4 5 7 7 6 6 7 7 6 6 7 7 6 6 2 3 7 7 6 6 PRED fish n 7 7 6 6 ARG2 7 7 6 6 6 7 7 7 6 6 6 ARG 2 7 7 7 6 6 6 1 7 7 7 6 6 6 7 7 7 6 6 6 7 7 7 6 6 4 5 7 7 6 6 7 7 6 4 5 7 6 7 4 5 3 Noun entry 2 3 2 CAT noun 3 6 HEAD 7 6 7 6 6 AGR 7 7 6 6 7 7 6 6 7 7 6 4 5 7 6 7 6 COMP 7 6 filled 7 6 7 fish 6 7 6 SPR filled 7 6 7 6 7 6 7 6 INDEX 1 7 6 2 3 7 6 7 6 SEM 7 6 6 PRED fish n 7 7 6 6 7 7 6 6 7 7 6 6 ARG 1 7 7 6 6 1 7 7 6 6 7 7 6 6 7 7 6 4 5 7 4 5 Corresponds to fish(x) where the INDEX • points to the characteristic variable of the noun (that is x). -
Sentential Negation and Negative Concord
Sentential Negation and Negative Concord Published by LOT phone: +31.30.2536006 Trans 10 fax: +31.30.2536000 3512 JK Utrecht email: [email protected] The Netherlands http://wwwlot.let.uu.nl/ Cover illustration: Kasimir Malevitch: Black Square. State Hermitage Museum, St. Petersburg, Russia. ISBN 90-76864-68-3 NUR 632 Copyright © 2004 by Hedde Zeijlstra. All rights reserved. Sentential Negation and Negative Concord ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus Prof. Mr P.F. van der Heijden ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Aula der Universiteit op woensdag 15 december 2004, te 10:00 uur door HEDZER HUGO ZEIJLSTRA geboren te Rotterdam Promotiecommissie: Promotores: Prof. Dr H.J. Bennis Prof. Dr J.A.G. Groenendijk Copromotor: Dr J.B. den Besten Leden: Dr L.C.J. Barbiers (Meertens Instituut, Amsterdam) Dr P.J.E. Dekker Prof. Dr A.C.J. Hulk Prof. Dr A. von Stechow (Eberhard Karls Universität Tübingen) Prof. Dr F.P. Weerman Faculteit der Geesteswetenschappen Voor Petra Table of Contents TABLE OF CONTENTS ............................................................................................ I ACKNOWLEDGEMENTS .......................................................................................V 1 INTRODUCTION................................................................................................1 1.1 FOUR ISSUES IN THE STUDY OF NEGATION.......................................................1 -
Semantic Computing
SEMANTIC COMPUTING Lecture 7: Introduction to Distributional and Distributed Semantics Dagmar Gromann International Center For Computational Logic TU Dresden, 30 November 2018 Overview • Distributional Semantics • Distributed Semantics – Word Embeddings Dagmar Gromann, 30 November 2018 Semantic Computing 2 Distributional Semantics Dagmar Gromann, 30 November 2018 Semantic Computing 3 Distributional Semantics Definition • meaning of a word is the set of contexts in which it occurs • no other information is used than the corpus-derived information about word distribution in contexts (co-occurrence information of words) • semantic similarity can be inferred from proximity in contexts • At the very core: Distributional Hypothesis Distributional Hypothesis “similarity of meaning correlates with similarity of distribution” - Harris Z. S. (1954) “Distributional structure". Word, Vol. 10, No. 2-3, pp. 146-162 meaning = use = distribution in context => semantic distance Dagmar Gromann, 30 November 2018 Semantic Computing 4 Remember Lecture 1? Types of Word Meaning • Encyclopaedic meaning: words provide access to a large inventory of structured knowledge (world knowledge) • Denotational meaning: reference of a word to object/concept or its “dictionary definition” (signifier <-> signified) • Connotative meaning: word meaning is understood by its cultural or emotional association (positive, negative, neutral conntation; e.g. “She’s a dragon” in Chinese and English) • Conceptual meaning: word meaning is associated with the mental concepts it gives access to (e.g. prototype theory) • Distributional meaning: “You shall know a word by the company it keeps” (J.R. Firth 1957: 11) John Rupert Firth (1957). "A synopsis of linguistic theory 1930-1955." In Special Volume of the Philological Society. Oxford: Oxford University Press. Dagmar Gromann, 30 November 2018 Semantic Computing 5 Distributional Hypothesis in Practice Study by McDonald and Ramscar (2001): • The man poured from a balack into a handleless cup. -
Chapter 1 Negation in a Cross-Linguistic Perspective
Chapter 1 Negation in a cross-linguistic perspective 0. Chapter summary This chapter introduces the empirical scope of our study on the expression and interpretation of negation in natural language. We start with some background notions on negation in logic and language, and continue with a discussion of more linguistic issues concerning negation at the syntax-semantics interface. We zoom in on cross- linguistic variation, both in a synchronic perspective (typology) and in a diachronic perspective (language change). Besides expressions of propositional negation, this book analyzes the form and interpretation of indefinites in the scope of negation. This raises the issue of negative polarity and its relation to negative concord. We present the main facts, criteria, and proposals developed in the literature on this topic. The chapter closes with an overview of the book. We use Optimality Theory to account for the syntax and semantics of negation in a cross-linguistic perspective. This theoretical framework is introduced in Chapter 2. 1 Negation in logic and language The main aim of this book is to provide an account of the patterns of negation we find in natural language. The expression and interpretation of negation in natural language has long fascinated philosophers, logicians, and linguists. Horn’s (1989) Natural history of negation opens with the following statement: “All human systems of communication contain a representation of negation. No animal communication system includes negative utterances, and consequently, none possesses a means for assigning truth value, for lying, for irony, or for coping with false or contradictory statements.” A bit further on the first page, Horn states: “Despite the simplicity of the one-place connective of propositional logic ( ¬p is true if and only if p is not true) and of the laws of inference in which it participate (e.g. -
A Comparison of Word Embeddings and N-Gram Models for Dbpedia Type and Invalid Entity Detection †
information Article A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection † Hanqing Zhou *, Amal Zouaq and Diana Inkpen School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1N 6N5, Canada; [email protected] (A.Z.); [email protected] (D.I.) * Correspondence: [email protected]; Tel.: +1-613-562-5800 † This paper is an extended version of our conference paper: Hanqing Zhou, Amal Zouaq, and Diana Inkpen. DBpedia Entity Type Detection using Entity Embeddings and N-Gram Models. In Proceedings of the International Conference on Knowledge Engineering and Semantic Web (KESW 2017), Szczecin, Poland, 8–10 November 2017, pp. 309–322. Received: 6 November 2018; Accepted: 20 December 2018; Published: 25 December 2018 Abstract: This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. Keywords: semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning 1. Introduction The Semantic Web is defined by Berners-Lee et al. -
Distributional Semantics
Distributional Semantics Computational Linguistics: Jordan Boyd-Graber University of Maryland SLIDES ADAPTED FROM YOAV GOLDBERG AND OMER LEVY Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 1 / 19 j j From Distributional to Distributed Semantics The new kid on the block Deep learning / neural networks “Distributed” word representations Feed text into neural-net. Get back “word embeddings”. Each word is represented as a low-dimensional vector. Vectors capture “semantics” word2vec (Mikolov et al) Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 2 / 19 j j From Distributional to Distributed Semantics This part of the talk word2vec as a black box a peek inside the black box relation between word-embeddings and the distributional representation tailoring word embeddings to your needs using word2vec Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 3 / 19 j j word2vec Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 4 / 19 j j word2vec Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 5 / 19 j j word2vec dog cat, dogs, dachshund, rabbit, puppy, poodle, rottweiler, mixed-breed, doberman, pig sheep cattle, goats, cows, chickens, sheeps, hogs, donkeys, herds, shorthorn, livestock november october, december, april, june, february, july, september, january, august, march jerusalem tiberias, jaffa, haifa, israel, palestine, nablus, damascus katamon, ramla, safed teva pfizer, schering-plough, novartis, astrazeneca, glaxosmithkline, sanofi-aventis, mylan, sanofi, genzyme, pharmacia Computational Linguistics: Jordan Boyd-Graber UMD Distributional Semantics 6 / 19 j j Working with Dense Vectors Word Similarity Similarity is calculated using cosine similarity: dog~ cat~ sim(dog~ ,cat~ ) = dog~ · cat~ jj jj jj jj For normalized vectors ( x = 1), this is equivalent to a dot product: jj jj sim(dog~ ,cat~ ) = dog~ cat~ · Normalize the vectors when loading them.