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Extreme underspecification Using semantics to integrate deep and shallow processing

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Acknowledgements Alex Lascarides, Ted Briscoe, Simone Teufel, Dan Flickinger, Stephan Oepen, John Carroll, Anna Ritchie, Ben Waldron Deep Thought project members Cambridge Masters students … Other colleagues at Cambridge, Saarbrücken, Edinburgh, Brighton, Sussex and Oxford

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Talk overview Why integrate deep and shallow processing? and why use compositional semantics? Semantics from shallow processing Flattening deep semantics Underspecification Minimal semantic units Composition without lambdas Integration experiments with broad-coverage systems/grammars (LinGO ERG and RASP) How does this fit with deeper semantics?

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Deep processing Detailed, linguistically-motivated, e.g., HPSG, LFG, TAG, varieties of CG Precise; detailed compositional semantics possible; generation as well as parsing Some are broad coverage and fast enough for real time applications BUT: not robust (coverage gaps, ill-formed input), too slow for IE etc, massive ambiguity

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Shallow (and intermediate) processing Shallow: e.g. POS tagging, NP chunking Intermediate: e.g., grammars with only a POS tag lexicon (RASP) Fast; robust; integrated stochastic techniques for disambiguation BUT: no long-distance dependencies, allow ungrammatical input (so limitations for generation), no conventional semantics without subcategorization

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Why integrate deep and shallow processing? Complementary strengths and weaknesses Weaknesses of each are inherent: more complexity means larger search space, greater information requirement hand-coding vs machine learning is not the main issue – treebanking costs, sparse data problems Lexicon is the crucial resource difference between deep and shallow approaches

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Applications that may benefit from integrated approaches Summarization: shallow parsing to identify possible key passages, deep processing to check and combine response: deep parser uses shallow parsing for disambiguation, back off when parse failure Information extraction: shallow first (as summarization), named entities Question answering: deep parse questions, shallow parse answers

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Compositional semantics as the common representation Need a common representation language: pairwise compatibility between systems is too limiting Syntax is theory-specific Eventual goal should be semantics Crucial idea: shallow processing gives underspecified semantic representation

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Shallow processing and underspecified semantics Integrated parsing: shallow parsed phrases incorporated into deep parsed structures Deep parsing invoked incrementally in response to information needs Reuse of knowledge sources: domain knowledge, recognition of named entities, transfer rules in MT Integrated generation Formal properties clearer, representations more generally usable

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Semantics from POS tagging every_AT1 cat_NN1 chase_VVD some_AT1 dog_NN1 _every_q(x1), _cat_n(x2 sg ), _chase_v(e past ), _some_q(x3), _dog_n(x4 sg ) Tag lexicon: AT1 _lemma_q(x) NN1_lemma_n(x sg ) VVD _lemma_v(e past )

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Deep parser output Conventional semantic representation Every dog chased some cat every(x,cat(x sg ),some(y sg,dog1(y sg ),chase(e sp,x sg,y sg ))) some(y sg,dog1(y sg ),every(x sg,cat(x sg ),chase(e sp,x sg,y sg ))) Compositional: reflects morphology and syntax Scope ambiguity

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Modifying syntax of deep grammar semantics: overview 1.Underspecification of quantifier scope: in this talk, using Minimal Recursion Semantics (MRS) 2.Robust MRS Separating relations Explicit equalities Conventions for predicate names and sense distinctions Hierarchy of sorts on variables

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Scope underspecification Standard logical forms can be represented as trees Underspecified logical forms are partial trees (or descriptions of sets of trees) Constraints on scope control how trees may be reconstructed

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Logical forms Generalized quantifier notation: every(x,cat(x sg ),some(y sg,dog1(y sg ),chase(e sp,x sg,y sg ))) forall x [cat(x) implies exists y [ dog1(y) and chase(e,x,y) ]] some(y sg,dog1(y sg ),every(x sg,cat(x sg ),chase(e sp,x sg,y sg ))) exists y [ dog1(y) and forall x [cat(x) implies chase(e,x,y) ]] Event variables: e.g., chase(e,x,y)

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PC trees every x cat x some y dog1 chase y xy some y dog1 y every x cat chase x Every cat chased some dog e xye

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PC trees share structure every x cat x some y dog1 chase y some y dog1 y every x cat chase x xye xye

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Bits of trees every x cat x some y dog1 y chase Reconstruction conditions: tree-ness variable binding xye

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Label nodes and holes lb1:every xlb2:cat x lb4:some y lb5:dog1 y lb3:chase h6 h7 h0 h0 – hole corresponding to the top of the tree Valid solutions: equate holes and labels xye

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Maximize splitting lb1:every x lb2:cat x lb4:some y lb5:dog1 y lb3:chase h6 h7 h0 h8 Constraints: h8=lb5 h9=lb2 h9 xye

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Notation for underspecified scope lb1:every(x,h9,h6) lb2:cat(x) lb5:dog1(y) lb4:some(y,h8,h7) lb3:chase(e,x,y) top: h0 h9=lb2 h8=lb5 MRS actually uses: h9 qeq lb2 h8 qeq lb5

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Extreme underspecification Splitting up predicate argument structure Explicit equalities Hierarchies for predicates and sorts Goal is to split up semantic representation into minimal components

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Separating arguments lb1:every(x,h9,h6), lb2:cat(x), lb5:dog1(y), lb4:some(y,h8,h7), lb3:chase(e,x,y), h9=lb2,h8=lb5 goes to: lb1:every(x), RSTR(lb1,h9), BODY(lb1,h6), lb2:cat(x), lb5:dog1(y), lb4:some(y), RSTR(lb4,h8), BODY(lb4,h7), lb3:chase(e),ARG1(lb3,x),ARG2(lb3,y), h9=lb2,h8=lb5

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Explicit equalities lb1:every(x1), RSTR(lb1,h9), BODY(lb1,h6), lb2:cat(x2), lb5:dog1(x4), lb4:some(x3), RSTR(lb4,h8), BODY(lb4,h7), lb3:chase(e),ARG1(lb3,x2),ARG2(lb3,x4), h9=lb2,h8=lb5,x1=x2,x3=x4

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Naming conventions lb1:_every_q(x1 sg ),RSTR(lb1,h9),BODY(lb1,h6), lb2:_cat_n(x2 sg ), lb5:_dog_n_1(x4 sg ), lb4:_some_q(x3 sg ),RSTR(lb4,h8),BODY(lb4,h7), lb3:_chase_v(e sp ),ARG1(lb3,x2 sg ),ARG2(lb3,x4 sg ) h9=lb2,h8=lb5, x1 sg = x2 sg, x3 sg = x4 sg

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POS output as underspecification DEEP – lb1:_every_q(x1 sg ), RSTR(lb1,h9), BODY(lb1,h6), lb2:_cat_n(x2 sg ), lb5:_dog_n_1(x4 sg ), lb4:_some_q(x3 sg ), RSTR(lb4,h8), BODY(lb4,h7),lb3:_chase_v(e sp ), ARG1(lb3,x2 sg ),ARG2(lb3,x4 sg ), h9=lb2,h8=lb5, x1 sg =x2 sg, x3 sg =x4 sg POS – lb1:_every_q(x1), lb2:_cat_n(x2 sg ), lb3:_chase_v(e past ), lb4:_some_q(x3), lb5:_dog_n(x4 sg ) (as previous slide but added labels)

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POS output as underspecification DEEP – lb1:_every_q(x1 sg ), RSTR(lb1,h9),BODY(lb1,h6), lb2:_cat_n(x2 sg ), lb5:_dog_n_1(x4 sg ), lb4:_some_q(x3 sg ), RSTR(lb4,h8), BODY(lb4,h7),lb3:_chase_v(e sp ), ARG1(lb3,x2 sg ),ARG2(lb3,x3 sg ), h9=lb2,h8=lb5, x1 sg =x2 sg, x3 sg =x4 sg POS – lb1:_every_q(x1), lb2:_cat_n(x2 sg ), lb3:_chase_v(e past ), lb4:_some_q(x3), lb5:_dog_n(x4 sg )

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Hierarchies e sp (simple past) is defined a subtype of e past in general, hierarchy of sorts defined as part of the semantic interface (SEM-I) dog_n_1 is a subtype of dog_n by convention, lemma_POS_sense is a subtype of lemma_POS

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Extreme Underspecification Factorize deep representation to minimal units Only represent what you know for each type of processor Compatibility: Sorts and (some) closed class word information in SEM-I for consistency No lexicon for shallow processing (apart from POS tags)

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Semantics from RASP RASP: robust, domain-independent, statistical parsing (Briscoe and Carroll) can’t produce conventional semantics because no subcategorization can sometimes identify arguments: S -> NP VP NP supplies ARG1 for V partial identification: VP -> V NP S -> NP S NP might be ARG2 or ARG3

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Underspecification of arguments ARGN ARG1or2ARG2or3 ARG2ARG1ARG3 RASP arguments can be specified as ARGN, ARG2or3 etc Also useful for Japanese deep parsing?

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Software etc Open Source LinGO English Resource Grammar (ERG) LKB system: parsing and generation, now includes MRS-RMRS interconversion RMRS output as XML RMRS comparison Preliminary RASP-RMRS First version of SEM-I

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Composition without lambdas Formalized, consistent composition integration at subsentential level standardization Traditional lambda calculus unsuitable Doesn’t allow underspecification Syntactic requirements mixed up with the semantics Algebra is rational reconstruction of a feature structure approach to composition

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Lexicalized composition [h,e1], {[h3,x] subj }, {h:_probably(h2), h3:_sleep(e), arg1(h3,x)}, {e1=e},{h2 qeq h3} 1. hook: externally accessible information 2. slots: when functor, slot is equated with argument hook 3. relations: accumulated monotonically 4. equalities: record hook-slot equations (not shown from now on) 5. scope constraints: (ignored from now on)

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probably sleeps [h3,e], {[h3,x] subj }, {h3:_sleep(e), ARG1(h3,x)} sleeps [h,e1], {[h2,e1] mod }, {h:_probably(h2)} probably Syntax defines probably as semantic head, composition using mod slot [h,e1], {[h3,x] subj },{h:_probably(h3), h3:_sleep(e1), arg1(h3,x)} probably sleeps

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Non-lexicalized grammars Lexicalized approach is a rational reconstruction of semantic composition in the ERG (Copestake et al, 2001) Without lexical subcategorization, rely on grammar rules to provide the ARGs `anchors’ rather than slots, to ground the ARGs (single anchor for RASP)

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Some cat sleeps (in RASP) [h3,e],, {h3:_sleep(e)} sleeps [h,x],, {h1:_some(x),RSTR(h1,h2),h2:_cat(x)} some cat S->NP VP: Head=VP, ARG1(, ) [h3,e],, {h3:_sleep(e), ARG1(h3,x), h1:_some(x),RSTR(h1,h2),h2:_cat(x)} some cat sleeps

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The current project …

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Deep Thought Saarbrücken, Sussex, Cambridge, NTNU, Xtramind, CELI Objectives: demonstrate utility of deep processing in IE and response German, Norwegian, Italian and English October 2002 – October 2004

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Integrated IE: a scenario Example: I don’t like the PBX 30 Shallow processing finds interesting sentences Named entity system isolates entities h1:name(x,”PBX-30”) Deep processor identifies relationships, modals, negation etc h2:neg(h3), h3:_like(y,x), h3:name(x,”PBX-30”)

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Some issues `shallow’ processors can sometimes be deeper: e.g. h1:model-name(x,”PBX-30”) Compatibility and standardization: defining SEM-I (semantic interface) Limits on compatibility: e.g., causative- inchoative Efficiency of comparison: indexing representations by character position

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The bigger picture... `deep’ processing reflects syntax and morphology but limited lexical semantics conventional vs predictable: count/mass: lentils/rice, furniture, lettuce adjectives: heavy defeat, ?large problem prepositions and particles: up

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Incremental development of wide-coverage semantics corpus-based acquisition techniques: shallow processing eventual integration with deep processing statistical model of predicates: e.g., large_j_rel pointer to vector space logic isn’t enough but is needed

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Conclusion every x cat x some y dog1 chase y x y some y dog1 y every x cat chase x e x y e lb1:every(x), RSTR(lb1,h9), BODY(lb1,h6), lb2:cat(x), lb5:dog1(y), lb4:some(y), RSTR(lb4,h8), BODY(lb4,h7), lb3:chase(e),ARG1(lb3,x), ARG2(lb3,y), h9=lb2,h8=lb5

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Conclusion: extreme underspecification Split up information content as much as possible Accumulate information by simple operations Don’t represent what you don’t know but preserve everything you do know Use a flat representation to allow pieces to be accessed individually

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