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Final HPSGs Cleaning up and final aspects, semantics, overview to statistical NLP

Final HPSGs Cleaning up and final aspects, semantics, overview to statistical NLP. HPSGs An Overlooked Topic: Complements vs. Modifiers • Intuitive idea: Complements introduce essential participants in the situation denoted; modifiers refine the description.

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Final HPSGs Cleaning up and final aspects, semantics, overview to statistical NLP

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  1. Final HPSGs Cleaning up and final aspects, semantics, overview to statistical NLP Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  2. HPSGs An Overlooked Topic: Complements vs. Modifiers • Intuitive idea: Complements introduce essential participants in the situation denoted; modifiers refine the description. • Generally accepted distinction, but disputes over individual cases. • Linguists rely on heuristics to decide how to analyze questionable cases (usually PPs). Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  3. HPSGs Heuristics for Complements vs. Modifiers • Obligatory PPs are usually complements. • Temporal & locative PPs are usually modifiers. • An entailment test: If X Ved (NP) PPdoes not entail X did something PP, then the PP is a complement. Examples – Pat relied on Chris does not entailPat did something on Chris – Pat put nuts in a cup does not entailPat did something in a cup – Pat slept until noon does entailPat did something until noon – Pat ate lunch at Bytes does entailPat did something at Bytes Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  4. HPSGs Agreement • Two kinds so far (namely?) • Both initially handled via stipulation in theHead-Specifier Rule • But if we want to use this rule for categories that don’t have the AGR feature (such as PPs and APs, in English), we can’t build it into the rule. Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  5. HPSGs The Specifier-Head Agreement Constraint (SHAC) Verbs and nouns must be specified as: Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  6. HPSGs The Count/Mass Distinction • Partially semantically motivated – mass terms tend to refer to undifferentiated substances (air, butter, courtesy, information) – count nouns tend to refer to individuatable entities (bird, cookie, insult, fact) • But there are exceptions: – succotash (mass) denotes a mix of corn & lima beans, so it’s not undifferentiated. – furniture, footwear, cutlery, etc. refer to individuatable artifacts with mass terms – cabbage can be either count or mass, but many speakers get lettuce only as mass. Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  7. HPSGs – Semantics The Linguist’s stance: Building a precise model • Some statements are statements about how the model works: “[prep] and [AGR 3sing] cannot be combined because AGR is not a feature of the type prep.” • Some statements are statements about how (we think) English or language in general works. “The determiners a and many only occur with count nouns, the determiner much only occurs with mass nouns, and the determiner the occurs with either.” • Some are statements about how we code a particular linguistic fact within the model. “All count nouns are [SPR < [COUNT +]>].” Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  8. HPSGs – Semantics The Linguist’s stance:A Vista on the Set of Possible English Sentences • ... as a background against which linguistic elements (words, phrases) have a distribution • ... as an arena in which linguistic elements “behave” in certain ways Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  9. HPSGs - Semantics So far, our “grammar” has no semantic representations. We have, however, been relying on semantic intuitions in our argumentation, and discussing semantic contrasts where they line up (or don't) with syntactic ones. Examples? • structural ambiguity • S/NP parallelism • count/mass distinction • complements vs. modifiers Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  10. HPSGs - Semantics Aspects of meaning we won’t account for • Pragmatics • Fine-grained lexical semantics: Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  11. HPSGs - Semantics Our Slice of a World of Meanings “... the linguistic meaning of Chris saved Pat is a proposition that will be true just in case there is an actual situation that involves the saving of someone named Pat by someone named Chris.” Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  12. HPSGs - Semantics Our Slice of a World of Meanings What we are accounting for is the compositionality of sentence meaning. • How the pieces fit together Semantic arguments and indices • How the meanings of the parts add up to the meaning of the whole. Appending RESTR lists up the tree Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  13. HPSGs – Semantics in Constraint-based grammar Constraints as generalized truth conditions • proposition: what must be the case for a proposition to be true • directive: what must happen for a directive to be fulfilled • question: the kind of situation the asker is asking about • reference: the kind of entity the speaker is referring to Syntax/semantics interface: Constraints on how syntactic arguments are related to semantic ones, and on how semantic information is compiled from different parts of the sentence. Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  14. HPSGs – Semantics – Feature Geometry Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  15. HPSGs – Semantics – How the pieces fit together Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  16. HPSGs – Semantics – How the pieces fit together Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  17. HPSGs – Semantics – How the pieces fit together Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  18. HPSGs – Semantics (pieces together) Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  19. HPSGs – Semantics (more detailed view of same tree) Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  20. HPSGs – Semantics To Fill in Semantics for the S-node, we need the Semantics Principles The Semantic Inheritance Principle: • In any headed phrase, the mother's MODE and INDEX are identical to those of the head daughter. The Semantic Compositionality Principle: • In any well-formed phrase structure, the mother's RESTR value is the sum of the RESTR values of the daughter. Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  21. HPSGs – Semantics – semantics inheritance illustrated Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  22. HPSGs – Semantics - semantic compositionality illustrated Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  23. HPSGs – Semantics – what identifies indices Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  24. HPSGs – Semantics – summary words contribute predications ‘expose’ one index in those predications, for use by words or phrases relate syntactic arguments to semantic arguments Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  25. HPSGs – Semantics – summary, grammar rules identify feature structures (including the INDEX value) across daughters Head Specifier Rule Head Complement Rule Head Modifier Rule Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  26. HPSGs – Semantics – summary, grammar rules identify feature structures (including the INDEX value) across daughters license trees which are subject to the semantic principles - SIP ‘passes up’ MODE and INDEX from head daughter Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  27. HPSGs – Semantics – summary, grammar rules identify feature structures (including the INDEX value) across daughters license trees which are subject to the semantic principles • SIP ‘passes up’ MODE and INDEX from head daughter • SCP: ‘gathers up’ predications (RESTR list) from all daughters Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  28. HPSGs – other aspects of semantics Tense, Quantification (only touched on here) Modification Coordination Structural Ambiguity Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  29. HPSGs – what were are trying to do Objectives • Develop a theory of knowledge of language • Represent linguistic information explicitly enough to distinguish well-formed from ill-formed expressions • Be parsimonious, capturing linguistically significant generalizations. Why Formalize? • To formulate testable predictions • To check for consistency • To make it possible to get a computer to do it for us Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  30. HPSGs –how we construct sentences The Components of Our Grammar • Grammar rules • Lexical entries • Principles • Type hierarchy (very preliminary, so far) • Initial symbol (S, for now) We combine constraints from these components. • Question: What says we have to combine them? Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  31. HPSGs – an example A cat slept. • Can we build this with our tools? • Given the constraints our grammar puts on well-formed sentences, is this one? Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  32. HPSGs – lexical entry for “a” • Is this a fully specified description? • What features are unspecified? • How many word structures can this entry license? Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  33. HPSGs – lexical entry for “cat” • Which feature paths are abbreviated and Is this fully specified? • What features are unspecified? • How many word structures can this entry license? Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  34. HPSGs - Effect of Principles: the SHAC Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  35. HPSGs - Description of Word Structures for cat Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  36. HPSGs - Description of Word Structures for a Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  37. HPSGs - Building a Phrase Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  38. HPSGs - Constraints Contributed by Daughter Subtrees Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  39. HPSGs - Constraints Contributed by the Grammar Rule Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  40. HPSGs - A Constraint Involving the SHAC Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  41. HPSGs - Effects of the Valence Principle Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  42. HPSGs - Effects of the Head Feature Principle Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  43. HPSGs - Effects of the Semantic Inheritance Principle Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  44. HPSGs - Effects of the Semantic Compositionality Principle Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  45. HPSGs - Is the Mother Node Now Completely Specified? Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  46. HPSGs - Lexical Entry for slept Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  47. HPSGs - Another Head-Specifier Phrase Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  48. HPSGs - Is this description fully specified? Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  49. HPSGs - Does the top node satisfy the initial symbol? Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

  50. HPSGs - RESTR of the S node Instructor: Nick Cercone - 3050 CSEB - nick@cse.yorku.ca

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