1. Semantic Roles and Ontologies pala@fi.muni.cz

2. Ontologies Growing interest in the data structures known as ontologies Language expressions covering the individual domains, hierarchically organized E.g. Top Ontology from EuroWordNet – has been designed for English as whole Others – SUMO/MILO, CYC, etc. How they are built – introspectively, designed usually from the ‘top’ Large ontologies (TO EWN) are not based on evidence, on language data coming from corpora In the area called Semantic Web ontologies are developed quite pragmatically, thus their compatibility is questionable

3. Semantic roles In NLP area much attention is paid to ‘valency frames’ of verbs Data structures that describe the relational properties of the verbs Thus we speak about predicate-argument structure of verbs, e.g. for drink there is someone who drinks (AGENT) and the respective beverage (beer) – labeled as PATIENT Semantic roles are labels used for description of the arguments – we have their inventories (their number usually is about 40-50 Inventories of the semantic roles are built mainly from the ‘top’ as well (i.e. introspectively)

4. How Ontologies and SRs are related? Inventories of the semantic roles can be, in fact, viewed as a kind of ontologies Number of verbs in Czech is about 35 000 and we want to have the semantic roles for them We need them also for discrimination of senses – this is a critical problem How to obtain the adequate inventory of the semantic roles – certainly not from the ‘top’ It is necessary to have look at the language data that can be found in corpora There are some tools that can help us with that

5. Verballex -Valency Frames for Czech An example: PítPít:1/drink:1, impf AGAG obl(kdo1) person:1animal:1kdo1 VERB SUBSSUBS obl(co4)beverage:1co4 - The lexicon of such frames is being built (presently about 8 000 Czech verbs) - Approx. 3 000 of them are linked to their English equivalents in Czech WordNet

6. Verballex – cont. The semantic roles within Verballex are two- level labels General labels like AGENT, PATIENT, ADDRESSEE, SUBSTANCE, … about 50, taken from EWN TO Lower labels such as human:1, animal:1 – literals from WordNet – approx. 200 (the list) The frames can be used to obtain semantic classes of verbs Our inventory of the roles is based on WordNet – there are some problems

7. If we have a look at the real data? Verbs like vidět/see – we can see ANYTHING, the right argument is then ENTITY This does not help us too much, we want to describe what one can really see? Corpora and Word Sketches – table for vidět, what follows from it? AG(osoba|zvíře|organizace) – vidět – PAT(SITUAT{situace, problém, věc, svět, rozdíl} CAUSE{důvod, příčina, smysl, chyba, nebezpečí} STARTPOINT{východisko, perspektiva, budoucnost, možnost} OBJECT{film, karta, tvář, silueta, obrys, světlo, spousta, svět})

8. If we have a look…cont. The verb slyšet/hear – Word Sketch table AG(osoba|zvíře|organizace|ucho) – slyšet – PAT(SOUND{hluk, rachot, hukot, hučení, klapot}| SHOOT{výstřel, střelba, rána}| VOICE{křik, výkřik, řev, zpěv, pláč, nářek, smích, volání}| WORD{slovo}| NOISE{bzukot, šplouchání, pleskání }| IDIOM1{trávu růst })

9. What is to be done? The inventory of the s. roles we are using in Verballex cannot capture semantic nature of some verb arguments It concerns frequent verbs We obviously need a better inventory/ontology for Czech/English verbs (and others as well – universality of the VFs The task – how this can be done using Word Sketches and possibly semi- automatically?

10. One Solution – CPA? Do meanings (senses) exist? Can they be empirically justified? Can corpus data help us? An example: how many meanings of the verb držet are there? Meaning potentials (Hanks, Pustejovsky) – realization through contexts Learning from the failure of WSD (up to 80 %) What is the cause of the WSD failure?

11. One Solution – cont. WSD people are making a wrong assumption – meanings exist and can be enumerated (if we use dictionaries) But dictionaries differ and yield different answers to this question, see držet or get in English What is the rescue? Look at the verb contexts, sort them, design an adequate list of their semantic roles, i. e. prepare an adequate ontology of s. roles

12. CPA solution