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Present eval

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Slides for my presentation in Turin

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Present eval

  1. 1. ITVENSES - A SYMBOLIC SYSTEM FOR ASPECT BASED SENTIMENT ANALYSIS RODOLFO DELMONTE DIPARTIMENTO DI STUDI LINGUISTICI E CULTURALI COMPARATI UNIVERSITÀ CA’ FOSCARI EMAIL: DELMONT@UNIVE.IT WEBSITE: RONDELMO.IT 1
  2. 2. OUTLINE • Systems’ Architectures • A Walkthrough Example 2
  3. 3. ITGETARUNS 3
  4. 4. ITVENSES To develop the system I used 20% of the dataset and the remaining 80% for testing 4
  5. 5. WALKTHROUGH EXAMPLE TAGGING AND LEMMATIZING • opn(1240342904,[0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"M anca il dentifricio in bagno."). • 1240342904_1 - ['Manca'-v,il-art,dentifricio-n,in-p,bagno-n,'.'- punto], • 1240342904_1-[ i(1,’Manca',v,mancare-[sems=intr,mfeats=kl3s]), i(2,il,art,il-[sems=def,mfeats=ms]), i(3,dentifricio,n,dentifricio- [mfeats=ms]), i(4,in,p,in), i(5,bagno,n,bagno- [sems=com,mfeats=ms]), i(6,’.',[punto],-) 5
  6. 6. WALKTHROUGH EXAMPLE SYNTAX & SEMANTICS • 1240342904_1-[ ibar-[‘Manca'-v-sn], obj-[il-art-sn,dentifricio-n-sn], obl-[in-p-sp,bagno-n-sn]] • 1240342904_1-[ • bagno-obl-1-[obl-[in-p-sp,bagno-n-sn]], • dentifricio-obj-1-[obj-[il-art-sn,dentifricio-n-sn]], • mancare-ibar-1- ibar-[Manca-v-sn] • ] 6
  7. 7. WALKTHROUGH EXAMPLE SYNTAX & SEMANTICS pas(1240342904_1, mancare - [ refex(1240342904_1-1, v, 'Manca' - mancare, [sems=intr,mfeats=kl3s], [activ,not_exten]), refex(1240342904_1-3, n, dentifricio-dentifricio, [3,mas,sing], [mfeats=ms], 1, subj / theme), [ i(4,in,p,in,sp,[],1,-), refex(1240342904_1-5, n, bagno - bagno, [def=indef,3,mas,sing], [ [act,agnt,artf,bld,cse,dyn,liqd,locat,med,obj,part], polsem = neut], 4, obl / theme) ] 1 - [ lemma = mancare, disc_m = nil, polsem = negative, subcat = [activ-not_exten], parola = ‘Manca’, change = gradual, view = external, factive = factive, moodtense = presente] ) 7
  8. 8. WALKTHROUGH EXAMPLE FROM ITGETARUNS TO ITVENSES • Try Match Aspect/s from refexs, i.e. Nouns, Verbs, Adjectives - bagno aspect 2; mancare aspect 3 • Try Match Polarity/ies from refexs, i.e. Nouns, Verbs, Adjectives - mancare marked as negative • sievesall: recomposes aspects and polarities which can be multiple for every sentence in a text 8
  9. 9. WALKTHROUGH EXAMPLE FROM ITGETARUNS TO ITVENSES • sievescheck: invertpols (invert polarities for the current aspect) • sievescheck: focalizers (spots focalizers, minimizers, downtoners) • sievescheck: checknegpriv (finds negation and its scope) • sievescheck: syntax sieves (deletes current aspect assignment identifiers) • Ind=2;Ind=3;Ind=6;Ind=7 - bagno Ind=2 (deleted) • Ind=3 albergo;hotel;struttura & centro;centrale;a_due_passi • Ind=2 camera;moquet;asciugamano;stanza;ambiente;bagno;letto & spazioso;comodo & + pulito • Ind=7 strada;piazza & rumoroso • Ind=7 arrivare;raggiungere & difficile;distante;scomodo;scarso 9
  10. 10. WALKTHROUGH EXAMPLE • collapseall: recovers all clause level analysis of the current sentence both at propositional and at subjective/factivity level and collects them together • now each evaluation term is made up by a text index - a set of semantic propositional level representations for that sentence - one aspect assignment - one associated polarity assignment, made up by a positive and a negative slot 10
  11. 11. WALKTHROUGH EXAMPLE AUGMENTED PREDICATE ARGUMENT STR. • 1240342904-[ • 1240342904_1-mancare(neg,statement,dentifricio-dentifricio-3, bagno-bagno-5)]- • [mancare]- Aspect seeds • [[],[Manca]]- Polarities: Positive+Negative • 3] Aspect Identifier 11
  12. 12. WALKTHROUGH EXAMPLE • evalothers: evaluates sentences marked with aspect n.8 and associates semantic representations • reduceevals: collapses evaluation terms for the same sentence with identical values • othersieve: sieves and modifies aspect value using combinations of aspect assignments present at text level; fires preferences for combined aspect values which modify one or more value 12
  13. 13. WALKTHROUGH EXAMPLE • comparevals: sieves and modifies those texts declaring “tutto bene” or the opposite with an all aspects positive/negative marking • checks for texts made up by a couple of aspects each evaluated to the contrary • checks for texts which have a semantic propositional level analysis as nonfactual or as negated and marks them with negative polarity - if + double negations 13
  14. 14. WALKTHROUGH EXAMPLE • Outputs the resulting 0/1 string • 1240342904-[0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]-true www.rondelmo.it 14
  15. 15. PERFORMANCE OF ITVENSES 15 Delayed Results for Test Set After Ablation Experiment
  16. 16. PERFORMANCE OF ITVENSES 16 Results for Development Set
  17. 17. PERFORMANCE OF ITVENSES 17 Published Results for Test Set
  18. 18. ITVENSES FOR IRONITA TASK A: Binary classification TASK B: Multiclass classification 18

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