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Table 3: Event extraction performance (% F1) Baseline NB k-NN SVM Event identification 5.82 ± 0.69 68.85 ± 0.63 71.07 ± 1.31 77.40 ± 0.80 Event classification 0.84 ± 0.20 33.56 ± 1.27 43.63 ± 2.93 48.04 ± 3.21 OCCURRENCE 3.10 ± 0.76 55.40 ± 2.04 53.33 ± 2.14 62.34 ± 1.23 REPORTING 0.69 ± 0.87 75.28 ± 1.09 76.44 ± 3.32 79.91 ± 2.36 ASPECTUAL 0.39 ± 1.24 12.25 ± 2.31 58.42 ± 7.37 59.21 ± 5.48 PERCEPTION 0.00 ± 0.00 24.66 ± 8.65 50.80 ± 15.64 56.32 ± 18.18 I ACTION 0.99 ± 0.90 28.63 ± 1.88 24.21 ± 4.26 24.28 ± 2.40 STATE CHANGE 0.72 ± 0.93 25.11 ± 3.62 23.18 ± 8.04 23.17 ± 6.49 HALF GENERIC 0.00 ± 0.00 13.59 ± 2.11 18.99 ± 5.70 31.04 ± 6.15 Table 4: Temporal relation extraction performance (% F1) Baseline NB k-NN SVM Temporal relation classification 6.44 ± 0.01 38.77 ± 1.87 32.17 ± 1.86 51.16 ± 2.94 BEFORE 51.43 ± 0.05 63.63 ± 1.74 59.61 ± 3.47 73.12 ± 0.85 AFTER – 59.35 ± 2.18 56.16 ± 3.83 71.08 ± 1.46 OVERLAP – 11.88 ± 11.70 0.00 ± 0.00 32.07 ± 19.44 OVERLAPPED BY – 2.64 ± 2.37 0.00 ± 0.00 20.67 ± 23.82 DURING – 55.59 ± 3.14 46.16 ± 4.26 60.41 ± 2.89 COVERS – 36.51 ± 2.52 24.49 ± 3.72 50.83 ± 3.49 EQUALS – 36.91 ± 3.54 33.87 ± 2.30 46.01 ± 3.43 NON DETERMINABLE – 43.63 ± 3.71 37.05 ± 7.22 55.11 ± 8.20 further work should be focused on a more detailed analysis of the annotations and the improvement in annotation guidelines. Secondly, the emergence of new linguistic tools for Croatian language presents an opportunity for using more complex features for classification. Finally, we intend to work on methods for relating the events to normalized temporal expressions (TIMEXes) extracted from text, which should aid in the classification of temporal relations between events. 7. Acknowledgments The authors would like to thank Damir Cvetovac, Latica Čačković, Marijana Marović, and Kristijan Pavlović for manually annotating the corpus. This work has been supported by the Ministry of Science, Education and Sports, Republic of Croatia, under Grant 036-1300646-1986. 8. References J.F. Allen. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11):832– 843. S. Bethard and J.H. Martin. 2006. Identification of event mentions and their semantic class. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 146–154. B. Boguraev and R.K. Ando. 2005. Timebank-driven TimeML analysis. Annotating, Extracting and Reason- ing about Time and Events, (05151). B. C. Bruce. 1972. A model for temporal references and its application in a question answering program. Artificial intelligence, 3:1–25. J. Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37–46. A. Galton. 1990. A critical examination of Allen’s theory of action and time. Artificial Intelligence, 42(2-3):159– 188. J.R. Landis and G.G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics, pages 159–174. M. Lapata and A. Lascarides. 2004. Inferring sentenceinternal temporal relations. In Proceedings of HLT- NAACL, pages 153–160. M. Lapata and A. Lascarides. 2006. Learning sentenceinternal temporal relations. Journal of Artificial Intelligence Research, 27(1):85–117. C.S. Lee, Y.J. Chen, and Z.W. Jian. 2003. Ontology-based fuzzy event extraction agent for Chinese e-news summarization. Expert Systems with Applications, 25(3):431– 447. I. Mani, M. Verhagen, B. Wellner, C.M. Lee, and J. Pustejovsky. 2006. Machine learning of temporal relations. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, pages 753–760. 145
L. Polanyi and A. Zaenen. 2006. Contextual valence shifters. Computing attitude and affect in text: Theory and applications, pages 1–10. N.M. Preradović, D. Boras, and S. Kisićek. 2009. CROVALLEX: Croatian verb valence lexicon. In Information Technology Interfaces, 2009. ITI’09. Proceedings of the ITI 2009 31st International Conference on, pages 533–538. IEEE. J. Pustejovsky, J. Castano, R. Ingria, R. Sauri, R. Gaizauskas, A. Setzer, G. Katz, and D. Radev. 2003a. TimeML: Robust specification of event and temporal expressions in text. New Directions in Question Answering, 2003:28–34. J. Pustejovsky, P. Hanks, R. Sauri, A. See, R. Gaizauskas, A. Setzer, D. Radev, B. Sundheim, D. Day, L. Ferro, et al. 2003b. The TimeBank corpus. In Corpus Linguistics, volume 2003, page 40. J. Pustejovsky. 1991. The syntax of event structure. Cognition, 41(1):47–81. H. Reichenbach. 1980. Elements of symbolic logic. Dover Publications. R. Saurí, R. Knippen, M. Verhagen, and J. Pustejovsky. 2005. Evita: a robust event recognizer for QA systems. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 700–707. E.V. Siegel and K.R. McKeown. 2000. Learning methods to combine linguistic indicators: Improving aspectual classification and revealing linguistic insights. Computational Linguistics, 26(4):595–628. Z. Vendler. 1957. Verbs and times. The philosophical review, 66(2):143–160. M. Verhagen, R. Gaizauskas, F. Schilder, M. Hepple, G. Katz, and J. Pustejovsky. 2007. Semeval-2007 task 15: Tempeval temporal relation identification. In Proceedings of the 4th International Workshop on Semantic Evaluations, pages 75–80. M. Verhagen, R. Sauri, T. Caselli, and J. Pustejovsky. 2010. Semeval-2010 task 13: Tempeval-2. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 57–62. M. Verhagen. 2005. Temporal closure in an annotation environment. Language Resources and Evaluation, 39(2):211–241. H. Verkuyl. 2005. Aspectual composition: Surveying the ingredients. Perspectives on aspect, pages 19–39. J. ˇSnajder, B. Dalbelo Baˇsić, and M. Tadić. 2008. Automatic acquisition of inflectional lexica for morphological normalisation. Information Processing and Management, 44(5):1720–1731. 146
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Zbornik 15. mednarodne multikonfere
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PREDGOVOR MULTIKONFERENCI INFORMACI
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KONFERENČNI ODBORI CONFERENCE COMM
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KAZALO / TABLE OF CONTENTS Jezikovn
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Zbornik 15. mednarodne multikonfere
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RECENZENTI • doc. dr. Simon Dobri
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language similarity as a feature of
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ually annotating 345 sentences and
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Disambiguating vectors for bilingua
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Language POS Source word Slovene se
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4. Evaluation and discussion of the
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, Iztok Kosem**, Berginc*** * Troj
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vseh besed, se pravi, da bi ta
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3. Spletni vmesnik za dostop do kor
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SPOOK.Sem: semantično označevanje
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uporabili samo za angleški del kor
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Vsekakor pa nas je pri označevanju
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Sistem vsebinskega priporočanja do
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poljubno, avtor pa svetuje vrednost
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Tabela 5: Filtriranje z dinamično
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Luka *, * Artificial Intelligence
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4. Technical approaches and algorit
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Tehnologije govorjenega jezika v pa
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zma. Možno jih je namre uporabiti
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Kaja Dobrovoljc, 1 Simon Krek, 2 Ja
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3.1.4. fazah: v prvi fazi s
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(del, dol, vez, skup) in pri poveza
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ˇSirjenje slovarja in dvoprehodni
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Uporabili smo orodje s katerim smo
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zbirka besedil, korpus, slovar Toma
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-8"?> -c.org/ns/1.0" type="pb" xml:
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6. Dostopnost virov dejanska možn
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prinašajo 185 milijonov besed oz.
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Ugotovimo lahko, da po številu pol
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predlog, zadeva, podatek, podlaga,
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Their main differences between them
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The corpus was PoS-tagged and lemma
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5. Conclusions In this paper we pre
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2000) is an English computer tutor
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Table 5: Classifier performance wit
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forms of Croatian proper names, but
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on the output of the first CRF. We
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Table 4: CroNER performance dependi
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Table 1: Description of the picture
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syntactic movement out of the objec
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and other agreement markers are use
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Figure 2. A FST representing a simp
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encoded text representation as seen
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Izhodišče segmentacijsko-tokeniza
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3. Učni korpus ssj500k 4 Učni kor
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Page 105: težavnosti; pri oceni berljivosti
Page 110 and 111: Kako dobro programi popravljajo vej
Page 112 and 113: okviru projekta ESS Uspešno vklju
Page 114 and 115: Besana opozarja na manjkajoče veji
Page 116 and 117: Umetno tvorjenje slovenskega govora
Page 118 and 119: Tabela 1: Analiza čistopisa zbirke
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Page 122 and 123: 3.2.1. Random indexing Random index
Page 124 and 125: Table 2: Accuracy for all considere
Page 126 and 127: Avtomatsko luščenje leksikalnih p
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Page 130 and 131: začetku povedi in upoštevanje t.
Page 132 and 133: Izdelava XML-shem za slovarske proj
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Page 138 and 139: Building Named Entity Recognition M
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Page 142 and 143: morphological information, for Slov
Page 144 and 145: Luščenje terminoloških kandidato
Page 146 and 147: 3.1. Enobesedni terminološki kandi
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Page 152 and 153: Table 1: Event annotation summary E
Page 156 and 157: Korpusna analiza slovenskega delež
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Page 164 and 165: not present” by 23 annotators. Th
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Page 182 and 183: Topic ontology construction from En
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Page 198 and 199: Table 1: Feature selection analysis
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F1 1.0 0.8 0.6 0.4 0.2 0.0 0.63 0.4
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sloWCrowd: orodje za popravljanje w
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2.2. Uporabniški vmesnik Osnovna f
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uporabnikov z večanjem stopnje zan
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Korpus slovenskega znakovnega jezik
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Posnetki se v anonimizirani obliki
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ZEN: zasnova glasovnih e-storitev v
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Rešitev je bila izvedena na podlag
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predpisane aktivnosti v poteku zdra
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Indeks avtorjev / Author index Agi
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