Some background and current work Talk overview rmrs: integrating processors via semantics

RMRS

• some background and current work

Talk overview

• RMRS: integrating processors via semantics

• Underspecified semantics from shallow processing

• Integration experiments with broad-coverage systems/grammars (LinGO ERG and RASP)

• Planned work

Integrating processing

• No single system can do everything: deep and shallow processing have inherent strengths and weaknesses

• Domain-dependent and domain-independent processing must be linked

• Parsers and generators

• Common representation for processing `above sentence level’ (e.g., anaphora)

Compositional semantics as a common representation

• Need a common representation language for systems: pairwise compatibility between systems is too limiting

• Syntax is theory-specific and unnecessarily language-specific

• Eventual goal should be semantics

• Core idea: shallow processing gives underspecified semantic representation, so deep and shallow systems can be integrated

• Full interlingua / common lexical semantics is too difficult (certainly currently), but can link predicates to ontologies, etc.

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

• Deep semantics taken as normative

RMRS approach: current and planned applications

• Question answering:

• Cambridge CSTIT: deep parse questions, shallow parse answers
• QA from structured knowledge: Frank et al

• Information extraction:

• Deep Thought
• Chemistry texts (SciBorg (?))

• Dictionary definition parsing for Japanese and English

• Bond and Flickinger

• Rhetorical structure, multi-document summarization, email response ...

• also LOGON: semantic transfer. MRSs from LFG used in HPSG generator.

RMRS: Extreme underspecification

• Goal is to split up semantic representation into minimal components (cf Verbmobil VITs)

• Scope underspecification (MRS)
• Splitting up predicate argument structure
• Explicit equalities
• Hierarchies for predicates and sorts

• Compatibility with deep grammars:

• Sorts and (some) closed class word information in SEM-I (API for grammar, more later)
• No lexicon for shallow processing (apart from POS tags and possibly closed class words)

RMRS principles

• Split up information content as much as possible

• Accumulate information monotonically 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

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

Naming conventions:predicate names without a lexicon

• lb1:_every_q(x1sg),RSTR(lb1,h9),BODY(lb1,h6),

• lb2:_cat_n(x2sg),

• lb5:_dog_n_1(x4sg),

• lb4:_some_q(x3sg),RSTR(lb4,h8),BODY(lb4,h7),

• lb3:_chase_v(esp),ARG1(lb3,x2sg),ARG2(lb3,x4sg)

• h9=lb2,h8=lb5, x1sg=x2sg,x3sg=x4sg

POS output as underspecification

• DEEP –

• lb1:_every_q(x1sg), RSTR(lb1,h9), BODY(lb1,h6), lb2:_cat_n(x2sg), lb5:_dog_n_1(x4sg), lb4:_some_q(x3sg), RSTR(lb4,h8), BODY(lb4,h7),lb3:_chase_v(esp), ARG1(lb3,x2sg),ARG2(lb3,x4sg), h9=lb2,h8=lb5, x1sg=x2sg,x3sg=x4sg

• POS –

• lb1:_every_q(x1), lb2:_cat_n(x2sg), lb3:_chase_v(epast), lb4:_some_q(x3), lb5:_dog_n(x4sg)

POS output as underspecification

• DEEP –

• lb1:_every_q(x1sg), RSTR(lb1,h9),BODY(lb1,h6), lb2:_cat_n(x2sg), lb5:_dog_n_1(x4sg), lb4:_some_q(x3sg), RSTR(lb4,h8), BODY(lb4,h7),lb3:_chase_v(esp), ARG1(lb3,x2sg),ARG2(lb3,x3sg), h9=lb2,h8=lb5, x1sg=x2sg,x3sg=x4sg

• POS –

• lb1:_every_q(x1), lb2:_cat_n(x2sg), lb3:_chase_v(epast), lb4:_some_q(x3), lb5:_dog_n(x4sg)

Semantics from RASP

• RASP: robust, domain-independent, statistical parsing (Briscoe and Carroll)

• can’t produce conventional semantics because no subcategorization

• can often identify arguments:

• S -> NP VP NP supplies ARG1 for V

• potential for partial identification:

• VP -> V NP
• S -> NP S NP might be ARG2 or ARG3

Underspecification of arguments

RMRS construction

• ERG etc – uses MRS -> RMRS converter

• argument splitting etc
• also RMRS -> MRS conversion

• POS-RMRS: tag lexicon

• RASP-RMRS: tag lexicon plus semantic rules associated with RASP rules to match ERG

• defaults when no rule RMRS specified

RMRS composition with non-lexicalized grammars

• MRS composition assumes a lexicalized approach: algebra defined in Copestake, Lascarides and Flickinger (2001)

• RMRS with non-lexicalised grammars: has similar basic algebra

• without lexical subcategorization, rely on grammar rules to provide the ARGs
• `anchors’ rather than slots, to ground the ARGs (single anchor for RASP)
• developed on basis of semantic test suite
• most rules written by Anna Ritchie

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

Real rule ...

• S/np_vp

• NPVP

• RULE

• EH1

• PRPSTN_M_RELH1H2

• ARG1H3X

• H2H

• XNPINDEX

• HVPLABEL

• H3VPANCHOR

• EVPINDEX

ERG-RMRS / RASP-RMRS

Inchoative

Infinitival subject (unbound in RASP-RMRS)

Ditransitive: missing ARG3

Mismatch: Expletive it

Mismatch: larger numbers

Comments on RASP-RMRS

• Fast enough (not significant compared to RASP processing time because no ambiguity)

• Too many RASP rules! Need to generalise over classes.

• Requires SEM-I – API for MRS/RMRS from deep grammar

• RASP and ERG may change:

• compatible test suites – semi-automatic rule update?
• alternative technique for composition?

• Parse selection – need to generalise over RMRSs

• weighted intersections of RMRSs (cf RASP grammatical relations)

SEM-I: semantic interface

• Meta-level: manually specified `grammar’ relations (constructions and closed-class)

• Object-level: linked to lexical database for deep grammars

• Object-level SEM-I auto-generated from expanded lexical entries in deep grammars (because type can contribute relations)
• Validation of other lexicons

• Need closed class items for RMRS construction from shallow processing

Alignment and XML

• Comparing RMRSs for same text efficiently uses characterization

• labels RMRSs according to their source in the text
• currently characters, but byte offset? Japanese etc?

• RMRS-XML

• RMRS seen as levels of mark-up: standoff annotation

SciBorg: Chemistry texts

• eScience project starting in October at Cambridge

• Computer Laboratory (Copestake, Teufel), Chemistry (Murray-Rust), CeSC (Parker)

• Aims:

• Develop an NL markup language which will act as a platform for extraction of information. Link to semantic web languages.
• Develop IE technology and core ontologies for use by publishers, researchers, readers, vendors and regulatory organisations.
• Model scientific argumentation and citation purpose in order to support novel modes of information access.
• Demonstrate the applicability of this infrastructure in a real-world eScience environment.

Research markup

• Chemistry: The primary aims of the present study are (i) the synthesis of an amino acid derivative that can be incorporated into proteins /via/ standard solid-phase synthesis methods, and (ii) a test of the ability of the derivative to function as a photoswitch in a biological environment.

• Computational Linguistics: The goal of the work reported here is to develop a method that can automatically refine the Hidden Markov Models to produce a more accurate language model.

RMRS and research markup

• Specify cues in RMRS

• Deep process cues: feasible because domain-independent

• more general and reliable than shallow techniques
• allows for complex interrelationships

• Use zones for advanced citation maps and other enhancements to repositories

Conclusions

• RMRS: semantic representation language allowing linking of deep and shallower processors

• RMRS construction: phrase-level compatibility between processors

• Many potential applications