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Selectional Preferences in Linguistic Behavior

This paper explores selectional preferences in linguistic behavior, highlighting the importance of Yorick Wilks' insights and the need for a pattern dictionary to capture the nuanced meanings associated with words in context.

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Selectional Preferences in Linguistic Behavior

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  1. Syntagmatic Preferences Patrick Hanks Masaryk University In honour of Yorick Wilks BCS, London, June 22, 2007

  2. What's so important about “My car drinks gasoline”? • Violation of “selection restrictions” is normal. • So selectional restrictions aren't restrictions at all • They are, in fact selectional preferences • Different combinations of selectional preferencesactivate different senses • Yorick's insights of the 1970s deserve to be followed up more vigorously and systematically than they have been.

  3. A language is a double helix • Start from the bottom up: • Let’s look at what the words do. • How do people use words to make meanings? • A natural language is a system of norms and exploitations: • Norms: Animals drink water, people drink beverages • Exploitations: My car drinks gasoline • Syntagmatic rules governing normal linguistic behaviour systematically interact with exploitation rules governing how those norms are exploited

  4. Patterns of linguistic behaviour • Normal linguistic behaviour is highly patterned. • Words in isolation have meaning potential, not meaning • A meaning potential is a more or less vague cluster of possibilities – e.g. what does fire mean? • A burning process? (and if so is it a good thing – in a house, under control – or a bad thing, raging out of control in a forest?) An electric heater? A sense of enthusiasm? Dismiss from employment? Operate a gun? Shoot an arrow? Cause to enthuse? Bake? • All of these and more. • Much overlap. • Sense enumeration doesn’t get it (cf Pustejovsky’s lexical conceptual paradigms) • In context, the range of possible interpretations of a word is severely limited: • People firing guns, ideas that fire people with enthusiasm, employers firing their staff, firing pottery in a kiln

  5. Word Use, Meaning, and Linguistic Theory • The normal uses of a word can begrouped into patterns, and meanings can be associated with the patterns (rather than the word in isolation) • So far they haven’t been. Why not? • Lack of evidence • Lexical analysis can only be done effectively with large corpora • Tradition and intuition • direttissimo assaults on word meaning • No one thought to go the long way round, via patterns • The tyranny of “all and only” • Lexicographers aimed to cover all possible uses, not just all normal uses • NLP and linguistic theory focused on boundary cases • Syntactocentrism in linguistic theory • misses the point about syntagmatics • Lack of a suitable theory • Aha! Preference Semantics provides the basis for such a theory • We should take PS seriously and ally it with other relevant theoretical work (Wittgenstein, Putnam, Rosch, Sinclair, Hoey, Pustejovsky, …)

  6. Why is a Pattern Dictionary Necessary? • Standard dictionaries do not provide the contexts that distinguish one sense of a word from another. • very poor syntagmatic information • give equal prominence to normal and merely possible senses • definitions (and senses) are not mutually exclusive • WordNet: synsets ≠ word senses! • FrameNet: frames ≠ word senses!

  7. Identifying norms is hard • ... and boring • The painful rediscovery of the obvious, • which is only obvious when pointed out • Only by painstaking corpus analysis is identifying norms possible. • What counts as a normal use of any verb? – e.g. drink

  8. Norms for 'drink', v. • 55% [[Human]] drink [[{Liquid = Water} | Beverage]] • 4% [[Animal]] drink [[Liquid = Water]] • 39% [[Human]] drink [NO OBJ] • 1% [[Human]] drink [[Experience]] {in} • 1% [[Human]] drink ([[Liquid = Beverage]]) {up}

  9. Some Exploitations of 'drink' A metaphor (or literary allusion): • The child of a nonconformist father learnt to drink deep of the Catholic tradition . • Owen Chadwick, 1991. Michael Ramsey: a life. A coercion: • ` He knows them all , ' she says adoringly , ` and they all drink shampoo -- nearly every night . • The Guardian, 1989.

  10. How pervasive is ambiguity? • Not as pervasive as you might think. • If we attach meanings to patterns, not to words, most “ambiguities” don't get a chance to rear their ugly heads. • But here's one: He drank. • Could be a null-object alternation of “he drank [[Beverage]]” • or it could mean that he had a problem with alcohol (pattern 2)

  11. Getting the right level of generalization is hard “John fired at a line of stags” • Corpus evidence shows that fire at does not prefer ANIM in the prepositional object slot. Any PHYSOBJ will do. • Building a pattern dictionary is a constant struggle to get “the right level” (or at least an acceptable level ) of generalization • Art is required to choose a level. • There are no right answers (no absolutes). • But plenty of wrong ones!

  12. Semantic Types and Semantic Roles • fire at assigns the semantic role “Target” to words of semantic type [[Physical Object]] • Semantic types are the intrinsic prototypical values of nouns – their essences • Semantic roles are assigned by context

  13. Word Meaning: a complex linguistic Gestalt • In the mind of an English speaker, the verb land is primed for any or all of the following: • passengers land from a plane – the pilot lands the plane – the plane lands – we landed at Heathrow – passengers land from a boat (but more probably they are soldiers) – a commander lands his troops (but not from a plane) – a boat lands its cargo – a trawler lands its catch – an angler lands a fish – Yorick landed the role of Caliban – He landed a job in Sheffield – someone else may land in trouble – or be landed with a problem – and someone may even land a blow on your nose

  14. Imposing order on chaos In the Pattern Dictionary: • Verbs are sorted into patterns • Exploitations are flagged for later analysis • Nouns (“lexical sets”) are clustered into an ontology • The ontology is “distorted” by usage • Lexical sets “shimmer”

  15. Lexical Sets “shimmer” • [[Human]] attend [[Event]] • Lexical set [[Event]] = { meeting, conference, funeral, ceremony, course, school, seminar, lecture, session, class, rally, dinner, hearing, briefing, reception, workshop, wedding, inquest, summit, concert, event, premiere, …} • [[Human]] participate {in [[Event]]} • Lexical set [[Event]] = {debate, election, exercise, coup, demonstration, activity, process, conference, consultation, selection, meeting, …} • [[Human]] hail [[Event]] • Lexical set [[Event]] = {victory, success, agreement, vote, opening, development, result, start, resurgence, …}

  16. Patterns are contrastive • 2% [[Human]] launch [[Boat]] • 7% [[Human]] launch [[Projectile]] • 58% [[Human | Institution]] launch [[Activity | Plan]] • 24% [[Institution]] launch [[{Artifact = Product} | {Activity = Service}]]

  17. What is a Pattern Dictionary? • a inventory of all normal patterns of verb use • not all possible uses. • an ontology of “shimmering” lexical sets (clusters of nouns according to semantic type and argument roles) • an inventory of semantically motivated syntagmatic distinctions

  18. Tools needed to build a Pattern Dictionary • A balanced corpus of the language (i.e. general language) • A theory • An initial lexical architecture that guides clustering Wilks, Pustejovsky, Sinclair, … • A lexical model that distinguishes norms from exploitations • A methodology: Corpus Pattern Analysis • Hanks 2004, Hanks and Pustejovsky 2005 • Including statistical corpus analysis • Church and Hanks 1989, Kilgarriff et al. 2004, 2005 • A shallow ontology • A hierarchical organization of semantic types, reflecting word groupings, not scientific conceptualization of the universe • A suite of corpus tools: Manatee, Bonito, Word Sketch Engine • Kilgarriff, Rychlý

  19. CPA procedure • Create a sample concordance (KWIC index) for a word: • 250 examples of actual uses of the word • Identify the typical syntagmatic patterns. • Assign each line of the sample to one of the patterns. • Take further samples if necessary. • Introspection is used to interpret data, but not to create data. • Store the pattern in the entry manager.

  20. In CPA, every line in the sample must be classified The choices are: • Norms • Exploitations • Alternations • Names (Midnight Storm: name of a horse, not a storm) • Mentions (to mention a word or phrase is not to use it) • Errors (e.g. learned mistyped as leaned) • Unassignables • See Proceedings of the Eleventh EURALEX International Congress, pages 105–116, Lorient, France, 2004.

  21. How normal are norms? How frequent are exploitations? • Roughly 75% of all clauses activate “primary norms” • About 20% activate secondary norms • including conventional metaphors • and some expressions that may once have been exploitations themselves • About 4% of all clauses involve exploitations of various sorts • dynamic metaphors, other tropes, coercions, ellipsis, etc. • About 1% of all clauses are unclassifiable

  22. Browsing and Feedback • The English Pattern Dictionary • http://nlp.fi.muni.cz/projects/cpa/ • Browse the first 50 verbs athttps://apollo.fi.muni.cz:8007/ • Login and password are both “guest” • Click on the pattern number to see the whole pattern • Click on “lines” to see supporting corpus evidence • 50 verb entries have been completed and released • Feedback, please! • 400 additional entries have been analysed, awaiting release • A shallow ontology has been drafted and is being edited • But not populated with nouns yet • 6500 verbs remain to be analysed • EPD will not include rare words like saltate or saccharify

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