1. Integrating Semantic Dictionaries for English, French and Bulgarian into the NooJ System for the Purposes of Information Retrieval Svetla Koeva, Max Silbetztein 8th INTEX / NooJ Workshop, 30 May, 2005

2. Main research goals To provide a sufficient methodology for the implementation of the natural language semantic relations into the NooJ system: –to create specialized Semantic Dictionaries for English, French and Bulgarian based on WordNet semantic relations; –to provide compete formalization of the inflection for simple and compound words included in the Wn structure.

3. History The integration of semantic relations into the INTEX system was initially proposed at the sixth INTEX workshop. Later on the idea was advanced into the Joint research RILA project Information retrieval based on semantic relations –LASELDI, Université de Franche-Comté –Department of Computational Linguistics, IBL, Bulgarian Academy of Sciences.

4. Language resources Bulgarian grammatical dictionary (BGD) – over 83 000 lemmas and 1 100 000 word forms; English WordNet 2.0 – 115 424 synonymous sets; Bulgarian WordNet (BalkaNet project) – 22 867 synonymous sets; French WordNet (EuroWordNet project) – 33 512 synonymous sets; English dictionary – over 30 000 lemmas (not inflected); French dictionary – extracted with INTEX.

5. Implementation tasks To transform the format of the BGD into the NooJ standard; To create semantic dictionaries for Bulgarian and English; To associate lemmas from the Bulgarian semantic dictionaries with the corresponding inflection types; To add missing lemmas and inflection types in BGD, if any; To create extensive dictionaries and corresponding inflection types for compounds.

6. BGD – Information structure design Category information – 6 classes: Noun, Verb, Adjective, Pronoun, Numeral, Others (Adverb, Preposition, Conjunction, Particle, Interjection) ; Paradigmatic information – Personal, Transitive, Perfective, Common, …; Grammatical information – Inflection, Conjugation, Sound alternations, ….

7. BGD – Grammatical subclasses Nouns - 22 subclasses with respect of their Type (Common, Proper, Singularia tantum, Pluralia tantum) and Gender; Verbs – 32 subclasses with respect of Transitivity, Perfectiveness, and Personality; Adjectives – 2 subclasses; Pronouns – 26 subclasses with respect of their Type and Possessor; Numerals – 6 sunclasses.

8. BGD – Grammatical types Noun – Number, Definiteness, Counting form, Case, Optional forms – 266 types; Verb – Person, Number, Tense, Mood, Voice, Participles, Gender, Definiteness – 257 types; Adjective – Gender, Number, Definiteness – 30 types; Pronoun – Gender, Person, Number, Definiteness, Case, Clitic, Possessing – 28 types; Numeral – Gender, Number, Definiteness, Approximate form, Male form – 20 types.

9. BGD – Dictionary format а,ЧА,0ПРИ, 7 sm0, Ok, ‘‘ абсол`ютен, ПРИ, 7smh, Ok, '2RCия‘ `август, С+М, 10sml, Ok, '2RCият‘ авиокомп`ания, С+Ж, 1sf0, Ok, '2RCа‘ австр`ийски, ПРИ, 3sfd, Ok, '2RCата‘ автоб`ус, С+М, 11sn0, Ok, '2RCо‘ автомат`ичен, ПРИ, 7snd, Ok, '2RCото‘ адрес`ирам, Г+Н+Т, 4p0, Ok, '2RCи‘ агит`ирам, Г+Н+Т, 4pd, Ok, '2RCите'

10. Transforming BGD Perl Script Dictionary Grammatical types Transliteration of labels

11. NooJ dictionary → aбсол`ютен, ПРИ, 7 aбсолютен,A+FLX=A-7 `август, С+М, 10август,N+M+FLX=N_M-10 авиокомп`ания, С+Ж,1авиокомпания,N+F+FLX=N_F-1 aвстр ` ийски, ПРИ, 3aвстрийски,A+FLX=A-3 автоб`ус, С+М, 11автобус,N+M+FLX=N_M-11 автомат`ичен, ПРИ, 7автоматичен,A+FLX=A-7 адрес`ирам,Г+Н+Т,4адресирам,V+IT+FLX=V_IT-4

12. NooJ formal descriptions → sm0, Ok, ‘‘ A-7 = /sm0 + smh, Ok, '2RCия‘ ия /smh + sml, Ok, '2RCият‘ ият /sml + sf0, Ok, '2RCа‘ а /sf0 + sfd, Ok, '2RCата‘ ата /sfd + sn0, Ok, '2RCо‘ о /sn0 + snd, Ok, '2RCото‘ ото /snd + p0, Ok, '2RCи‘ и /p0 + pd, Ok, '2RCите‘ ите /pd;

13. WordNet semantic relations ILRPOS/POSEW2.0BulNet HYPERONYMY N/N V/V94 84415 838 NEAR ANTONYMY N/N A/A V/V7 6421 847 PART MERONYMY N/N8 6361 241 MEMBER MERONYMY N/N12 205841 PORTION MERONYMY N/N787107 SUBEVENT V/V409162 CAUSES V/V439104 SIMILAR TO A/A V/V22 1961 479 VERB GROUP V/V1 748848 ALSO SEEA/A V/V 3 240895

14. Other relations ILRPOS/POSEW2.0BulNet BE IN STATEA/N1 296591 BG DERIVATIVEN/V36 6306 469 DERIVEDA/N6 8091 071 PARTICIPLEA/V40156 REGION DOMAINN/N V/N A/N B/N1 2804 USAGE DOMAINN/N V/N A/N B/N98322 CATEGORY DOMAINN/N V/N A/N B/N6 166638

15. Selected relations Synonymy (reflexive, symmetric, and transitive relation of equivalence); Hypernymy (inverse, asymmetric, and transitive relation between synonym sets), Meronymy (inverse, asymmetric, and transitive relation between synonym sets): Part meronymy; Member meronymy; Portion meronymy.

16. Selected relations Similar to (symmetric relation between similar adjectival synsets); Verb group (symmetric relation between semantically related verb synsets); Also see (symmetric relation between synsets - verbs or adjectives, that are close in meaning); Category domain (asymmetric extralinguistic relation between synsets denoting a concept and the sphere of knowledge it belongs to).

17. DELAF semantic dictionaries These dictionaries consist of pairs of literals defined for the corresponding semantic relation: – car,automobile.N – auto,automibile.N All possible combinations between literals in the given synsets are listed: – car,automobile.N – cars,automobile.N – auto,automibile.N – autos,automibile.N

18. NooJ Semantic dictionaries Synonymy relation ‘a plant consisting of buildings with facilities for manufacturing’ фабрика,N+FLX=ENG20-03196165-n предпрятие,N+FLX=ENG20-03196165-n factory,N+FLX=ENG20-03196165-n mill,N+FLX=ENG20-03196165-n manufacturing plant,N+FLX=ENG20-03196165-n manufactory,N+FLX=ENG20-03196165-n

19. NooJ Semantic dictionaries Hypernymy relation ‘the organized action of making of goods and services for sale’ производство,N+FLX=ENG20-00859333-n промишленост,N+FLX=ENG20-00859333-n индустрия,N+FLX=ENG20-00859333-n production,N+FLX=ENG20-00859333-n industry,N+FLX=ENG20-00859333-n manufacture,N+FLX=ENG20-00859333-n

20. Inflecting wordnet... otstranqwam (to remove) … ГНТ12...... remove something concrete, as by lifting, pushing, taking off, etc. or remove something abstract...

21. NooJ Semantic descriptions ‘the organized action of making of goods and services for sale’ ENG20-00859333-n = /Hs0 + то/Hsd + а /Hp0 + ата /Hpd + мишленост /Ss0 + мишлеността /Ssd + мишлености /Sp0 + мишленостите /Spd + индустрия/Ss0 + индустрията/Ssd + индустрии/Sp0 + индустриите/Spd; ENG20-00859333-n = /Hs + industry/Ss + industries/Sp0+ manifactures/Ss + manifactures/Sp;

22. After the nice solutions Lemmas which are not included in the BGD: –Lemmas classification to existing inflection types; –Formal description of new inflection types –Literals in Latin; –Validating WordNet. Semantic ambiguity - literals with two inflectional descriptions in BGD; Compound words –Formal description of inflection types; –Compounds classification.

23. NooJ Compound semantic descriptions ENG20-04182583-n = /Ss0 + та/Ssd + и (и/p0 +ите/pd) + завод ен/Ss0 + завод ния/Ssh + завод ният/Ssl + заводи ни/Sа0 + заводи ните/Sа0 + рафинерия/Ss0 + рафинерия та/Ssd + рафинерии и/Sp0 + рафинерии ите/Spd;

27. Applications of the Semantic Dictionaries Information retrieval by means of semantic equivalence with synonymy dictionaries; Information retrieval by means of semantic specification with hyperonymy and meronymy dictionaries; Information retrieval by means of similarity; Information retrieval by means thematic domains affiliations; Validation WordNet structure against its completeness and consistency.

28. Future directions Extensions and enhancements of the semantic dictionaries by means of: –Extension of the dictionaries coverage; –Addition of other semantic relations; –Inclusion of additional information to the entries. Integration of multilingual semantic extraction with NooJ using the Inter-Lingual-Index relation.