Semantic Analysis and Concept-based Translation for Multilingual Information Systems

Semantic Analysis and Concept-basedTranslation for Multilingual Information

Systems

Johannes Leveling andSven Hartrumpf and

Rainer Osswald

Intelligent Information and Communication Systems (IICS)University of Hagen (FernUniversität in Hagen)

58084 Hagen, [email protected]

GAL 2007, Hildesheim, Germany

[email protected]

SemanticAnalysis and

Concept-based

Translation

J. Leveling,S. Hartrumpf,R. Osswald

Concept-basedRepresenta-tion:MultiNet

Three Phasesfor a Concept-BasedMultilingual IRSystem

Concept-BasedInformationSystems

Applications

Conclusionand Outlook

References

Outline

1 Concept-based Representation: MultiNet

2 Three Phases for a Concept-Based Multilingual IRSystem

3 Concept-Based Information Systems

4 Applications

5 Conclusion and Outlook

J. Leveling, S. Hartrumpf, R. Osswald Semantic Analysis and Concept-based Translation 2 / 27


Concept-based

Translation





Applications


References

Motivation for Concept-BasedTranslation

• Example 1:Query expansion in information retrieval (IR) withelements from same synset

→ needs word sense disambiguation (differentiation ofconcepts), otherwise loss of precision

• Example 2:Question answering (QA): questions on relationsbetween concepts (situations, events, etc.)Example: Who killed Lee Harvey Oswald?

→ need semantic representation;bag-of-words information retrieval is not enough



Concept-based

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Applications


References

The MultiNet Paradigm

• Meaning and knowledge representation:Multilayered Extended Semantic Networks (Helbig,2001, 2006)

• Semantic network of nodes (concepts) and edges(semantic relations from a fixed set)

• In addition:semantic sorts, semantic features, layer information

• Different types of concepts:lexicalized vs. non-lexicalized

• Language-independence:annotation of English/Czech sentences from the WallStreet Journal with MultiNet (Charles University,Prague)



Concept-based

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Selected Semantic RelationsRelation Description

ASSOC associationATTCH attachment of object to objectCHPA change of sorts (property →abstract object)EXP experiencerMCONT an informational process or objectOBJ neutral objectPRED predicative concept specifying a pluralityPROP property relationshipPARS meronymySCAR carrier of a stateSSPE state specifierSUB conceptual subordination for objectsSUBS conceptual subordination for situationsSYNO synonymyTEMP temporal restriction for a situation?ALTN1 an introduction of alternatives



Concept-based

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The Computational Lexicon –HaGenLex

• Semantically oriented (German) lexical resource(Hartrumpf et al., 2003)

• Consists of multiple lexicons:• full syntactico-semantic information (26,000 entries)• flat lexicon (50,000 entries)• compound lexicon (30,000 entries; structure and

semantics)• name lexicons (250,000 entries)

• Support for the lexicographer: LIAplus workbench



Concept-based

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Sample Concepts (German)

• essen.1.1: (Der Student) (ißt) (eine Schokolade).• essen.1.2: (Der Student) (ißt) sich (satt).• essen.2.1: Das Kind hat kein Essen bekommen.• essen.2.2: Das Essen am Abend dauerte 2 Stunden.• fressen.1.1: (Der Hund) (frißt) (einen Knochen).• fressen.1.2: (Die Großmutter) (frißt) (einen Narren) (an

den Blumen).



Concept-based

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Applications


References

Lexicon Entry (German):essen.1.1

n-sign

morph

[base ”essen”infl-para i129g

]syn

[v-synv-type mainperf-aux habenv-control nocontr

]

semsel

sem

[sementity nonment-action

]c-id ”essen.1.1”

select

⟨

rel{

agt}

sel

syn

[np-syncat np

agr[case nom

]]semsel

[sem

[sementity human-object

]]

rel{

aff}

sel

syn

[np-syncat np

agr[case acc

]]semsel

[sem

[sem

entity[sort co

]]]

⟩

1



Concept-based

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References

Lexicon Entry (German):fressen.1.1

n-sign

morph

[base ”fressen”infl-para i139g

]syn

[v-synv-type mainperf-aux habenv-control nocontr

]

semsel

sem


]c-id ”fressen.1.1”

select

⟨

rel{

agt}

sel

syn

[np-syncat np

agr[case nom

]]semsel

[sem

[sementity animal-object ∨ human-object

]]

rel{

aff}

sel

syn

[np-syncat np

agr[case acc

]]semsel

[sem

[sem

entity[sort co

]]]

⟩

1



Concept-based

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Semantic analysis –The WOCADI parser

• Produces semantic network representation from(German) texts (Hartrumpf, 2003):

• resolves coreferences,• analyzes idioms,• decompounds nouns and adjectives,• identifies metonymy,• resolves deictic expressions etc.

• Applied to large corpora, includingCLEF-NEWS newspaper corpus (275,000 articles) andGerman Wikipedia (500,000 articles)



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SN Example (German)

OBJ

EX

P

SU

BS

SU

B

SU

BS

PRED

PR

OP

MCONT

PR

ED

SUBS

SC

AR S

SPE

SUB

ASSOC

PRED

PR

ED

*ALT

N1

*ALTN1ATTCH

c7c3 c6

c2 c1 c5

c4 berichten.2.2

c9

c10

c8

finden.1.1

prüfungskandidat.1.1prüfung.1.1

du.1.1 streß.1.1 psychisch.1.1

problem.1.1

prüfling.1.1

kandidat.1.1

dokument.1.1

Finde Dokumente, die über psychische Probleme oder Stress vonPrüfungskandidaten oder Prüflingen berichten. (GIRT topic 116)



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SN Example (English)

OBJ

EX

P

SU

BS

SU

B

SU

BS

PRED

PR

OP

MCONT

PR

ED

SUBS

SC

AR S

SPE

SUB

ASSOC

PRED

PR

ED

*ALT

N1

*ALTN1ATTCH

c7c3 c6

c2 c1 c5

c4

c9

c10

c8

you

document

report

problem

exam

mentalstress

examinee

candidate

find

‘Find documents reporting on mental problems or stress of examinationcandidates or examinees.’ (GIRT topic 116)



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Phase 1: Using Statistical MTand Web Services

• Employ (statistical) machine translation (MT) webservice for IR experiments (translation ofqueries/questions): Systran, Promt, ...

• Problems:• translating questions:

most systems trained on declarative sentences;imperative forms often misunderstood(Find documents ... →Fund Dokument ...)

• named entity recognition:not reliable (Neuengland →new narrow country )

• Performance loss from off-the-shelf translation tools forQA@CLEF: 50%further examples: Ligozat et al. (2006)



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Phase 2: AligningConcept-based Tools and

Resources

• Morphology and syntax are different for differentlanguages

• Semantics is the same (in general)• Our approach:

• create lexicons for different languages ;fast construction parallel to existing lexicon(s), e.g.HaGenLex →HaEnLex

• develop parser for different languages• apply methods from IR/QA on SN representation

• General idea: replace concepts (labels) in semanticnetwork representation (as a form of translation)



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Status of Alignment of LexicalResources

• German to English dictionaries: about 100,000word/phrase translations

• Mapping between HaGenLex concepts and GermaNetconcepts, plus GermaNet to EuroWordNet mapping:about 14,000 concept translations

• Wikipedia articles (in German and English): about3,000 proper noun translations for cities, countries,persons, organizations, etc.

• HaEnLex (parallel English version of HaGenLex) withfull morphologic, syntactic, semantic description ofconcepts: about 7,000 English entries



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Linguistic Phenomena (1/6)

Compounds (rare in English):

• with regular semanticsKinderernährung →nutrition of children

• with irregular semanticsFrauenzimmer →dame (?); ladies’ room (?)

• borderline casesBankwesen →banking (system) (?)

→ compound-less semantic representation is possible



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Idioms:• with corresponding idiom:

in den Sinn kommen (DE) →to start thinking about sth.to come into mind (EN) →to start thinking about sth.

• without equivalent idiom:to be someone’s cup of tea (EN) →to like

→ semantic representation of idioms



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Metonymy:• with corresponding metonymy pattern (for regulat

metonymy):The White House agreed, that ... (EN)→place-for-governmentDas Weiße Haus stimmte zu, dass ... (DE)→place-for-government

• without: ?→ no problems, yet



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Proper nouns:• transcriptions and transliterations, historic name

variants• Böll →Boell ;

Gorbatschow →Gorbatchev, Gorbatchov→ can be solved using aligned online resources e.g.

Wikipedia→ treat name variants as elements of the same synset



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Semantic gaps/lexical gaps:• Fohlen (DE) →colt (if male),• Fohlen (DE) →filly (if female)• Alignment of lexicon entries: morpho-syntactic features

differ in different languages, syntactic features also,semantic features do not (in general) but: netentries/rules/entailments may be slightly different?!,because they already involve other concepts (whichhave to be translated)



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Linguistic Phenomena (6/6)Semantic gaps/lexical gaps:

essen.1.1 →eat.1.1 AND fressen.1.1 →eat.1.1

n-sign

morph

[base ”eat”infl-para i20

]syn

[v-synv-type main

]

semsel

sem


]c-id ”eat.1.1”

select

⟨

rel{

agt}

sel

syn

[np-syncat np

]semsel

[sem

[sementity animal-object ∨ human-object

]]

rel{

aff}

sel

syn

[np-syncat np

]semsel

[sem

[sem

entity[sort co

]]]

⟩

1



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Phase 3: Towards aConcept-Based Translation

• Assumption that the same inventory of relations hold(about 140 relations) for different languages

• Natural language generation (for German)• Possible solution: English parser, generate natural

language from semantic network representation



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Monolingual Concept-Based IR

• Techniques of standard IR: stemming and stopwordremoval

• Monolingual concept-based IR:• represent queries (and documents) as semantic

networks• (translate concepts)• employ methods on semantic network representation

• Advantages:• semantics of compounds (relation to its constituents)• semantics of prepositions is typically represented by

semantic relation or function (no full translation needed)• lemmatizing (instead of stemming)• query expansion with elements of synsets



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Multilingual Concept-Based IR

• Three different approaches at supporting a multilingualsearch

1 translate queries into the document language2 translate documents into the query language3 translate both queries and documents into an

interlingua

• Multilingual concept-based IR: same as monolingualapproach, but translate concepts (1, 2, or 3)→towards an interlingua



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Projects and Evaluations

• GeoCLEF (Leveling and Veiel, 2006): Web service forMT (query translation)

• GIRT-4 experiments (Leveling, 2004, 2006a): combinedconcept and word translation

• NLI-Z39.50 (Leveling, 2006b): replace terminalconcepts in SN, then treat translation alternatives as asynset for query expansion (no decision for a singlereading necessary)

• QA@CLEF (Hartrumpf and Leveling, 2007): Webservice for MT, then analysis; concept-based translationwith rudimentary English parser (preliminaryexperiments)



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Conclusion

• General approach:• Parse queries• Translate concepts in SN representation• Operate on SN representation

• Aims at multilingual information systems for differentpurposes:IR, QA

• 3 phases (currently phase 2)



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Outlook

• Create a repository of interlingua concepts:allow for a concept-based machine-translation of text→natural language generation→MT

• Outlook for IR/QA:index semantic relations as well



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Hartrumpf, Sven (2003). Hybrid Disambiguation in Natural LanguageAnalysis. Osnabrück, Germany: Der Andere Verlag.

Hartrumpf, Sven; Hermann Helbig; and Rainer Osswald (2003). Thesemantically based computer lexicon HaGenLex – Structure andtechnological environment. Traitement automatique des langues,44(2):81–105.

Hartrumpf, Sven and Johannes Leveling (2007). Interpretation andnormalization of temporal expressions for question answering. InEvaluation of Multilingual and Multi-modal Information Retrieval: 7thWorkshop of the Cross-Language Evaluation Forum, CLEF 2006(edited by Peters, Carol; Paul Clough; Fredric C. Gey; Jussi Karlgren;Bernardo Magnini; Douglas W. Oard; Maarten de Rijke; andMaximilian Stempfhuber), volume 4730 of LNCS, pp. 432–439. Berlin:Springer.

Helbig, Hermann (2001). Die semantische Struktur natürlicher Sprache:Wissensrepräsentation mit MultiNet. Berlin: Springer.

Helbig, Hermann (2006). Knowledge Representation and the Semanticsof Natural Language. Berlin: Springer.

Leveling, Johannes (2004). University of Hagen at CLEF 2003: Naturallanguage access to the GIRT4 data. In Comparative Evaluation ofMultilingual Information Access Systems: 4th Workshop of theCross-Language Evaluation Forum, CLEF 2003 (edited by Peters,



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Carol; Julio Gonzalo; Martin Braschler; and Michael Kluck), volume3237 of LNCS, pp. 412–424. Berlin: Springer.

Leveling, Johannes (2006a). A baseline for NLP in domain-specificinformation retrieval. In Accessing Multilingual InformationRepositories: 6th Workshop of the Cross-Language Evaluation Forum,CLEF 2005 (edited by Peters, Carol; Fredric C. Gey; Julio Gonzalo;Gareth J. F. Jones; Michael Kluck; Bernardo Magnini; Henning Müller;and Maarten de Rijke), volume 4022 of LNCS, pp. 222–225. Berlin:Springer.

Leveling, Johannes (2006b). Formale Interpretation von Nutzeranfragenfür natürlichsprachliche Interfaces zu Informationsangeboten imInternet. Der andere Verlag, Tönning, Germany.

Leveling, Johannes and Dirk Veiel (2006). University of Hagen atGeoCLEF 2006: Experiments with metonymy recognition indocuments. In Results of the CLEF 2006 Cross-Language SystemEvaluation Campaign, Working Notes for the CLEF 2006 Workshop(edited by Nardi, Alessandro; Carol Peters; and José Luis Vicedo).Alicante, Spain.

Ligozat, Anne-Laure; Brigitte Grau; Isabelle Robba; and Anne Vilnat(2006). Evaluation and improvement of cross-lingual questionanswering strategies. In Proceedings of the EACL 2006 Workshop onMultilingual Question Answering (MLQA’06), pp. 23–30. Trento, Italy.


Technology

Semantic Analysis and Concept-based Translation for Multilingual Information Systems