Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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Events Jahrestagung, Rundreise, Überprüfung, Exekution, Militärputsch, Auftauchen, Bergung, Pubertät, Freiheitsstrafe, Wiederkehr, Niederschlagung, Militäraktion, Wiedereröffnung, Auszählung, Praxissemester, Prophylaxe, Umfrage, Vorbereitungsphase, Feierstunde, Boom, Planungsphase, Währungsreform, Ratifizierung, Klausurtagung, 90er-Jahr, Machtkampf, Ersatzdienst, Tagung, Volksabstimmung, Ritt, Ultraschalluntersuchung Objects Antenne, Medaille, Nase, Mitmensch, Steuerzahler, Nadel, ∗ Religionsausübung, Häuschen, Schlüssel, Wirtschaftsguts, Auge, Segel, Deckel, Nachbarstaat, Ministerin, Kapelle, Gefäß, Krankenkasse, Hase, Handschuh, Mitgliedsland, Tarifpartei, Konfliktpartei, Passagier, Beschwerdeführer, Linse, Schürze, Schwan, ∗ Handlungsfähigkeit , Gebietskörperschaft, Flair, Fötus, 5tel, Ärztekammer , Elefant, Mehrheit, Gesundheitsamt, Eisenbahnlinie, Table 2: Sample of acquired nouns Verb features (events) anzetteln-OBJ, ableisten-OBJ, verstreichen-SUBJ, mitschneiden-OBJ, vertagen-OBJ, anberaumen- OBJ, jähren-SUBJ, verbüßen-OBJ, vorverlegen- OBJ, dauern-SUBJ Noun features (events) Ableistung, Beendigung, Vorabend, Anmelder, Wirren, Schlußphase, Gräuel, Ausbruch, Veteran, Jahrestag, Vortag, Siegermacht, Versäumung, Verbüßung, Zurückverweisung, Ablegung, Ablauf Adjective features (events) 30jährig, erkennungsdienstlich, mündlich, medikamentös, anderweitig, monatelang, konzertant, amtsärztlich, ambulant, wochenlang, menschenunwürdig, physiotherapeutisch Verb features (objects) trinken-OBJ, erkranken-SUBJ, errichten-OBJ, investieren-SUBJ, engagieren-SUBJ, schütteln- OBJ, waschen-OBJ, erwerben-SUBJ, schöpfen- OBJ, spenden-OBJ, transportieren-OBJ Noun features (objects) Seele, Osten, Beziehung, Ministerpräsident, Bürgermeister, Gründung, Erwerb, Auge, Wiederaufbau, Köpfen, Anstalt, Einwohner, Sohn, Wettbewerbsfähigkeit, Verkauf, Verabschiedung Adjective features (objects) mittelständisch, rund, behindert, rot, kreisfrei, ätherisch, tätig, erworben, gehandelt, hell, schwerbehindert, arm, beruflich, blau, interessiert, elektrisch, teilen, blind, gebildet, golden, ansässig Table 3: Sample of acquired features recall due to the low frequency of the “embedded NP” construction (cf. Section 2.1), but the precision is also imperfect: many nouns can embed events as well as objects (Beziehung, Anmelder (relation, registrant)). The verb model works substantially better. Notably, it has an almost perfect precision – there are verbs almost all subject (or objects, respectively) of which belong to one of the two classes. However, its recall is still fairly low. The best model is the adjective-based one, with the highest recall and an almost equal precision. Open questions include the influence of target word frequency and domain. Given the space constraints, these must be left for future work. 4 Conclusion We have presented a simple corpus-based method to learn event-denoting nouns and object-denoting nouns from corpora using a bootstrapping cycle to alternately acquire nouns and context features. Our method shows good results on German, but Recall Precision F 1 Adjective features 82.89 92.86 87.59 Verb features 61.66 95.46 74.92 Noun features 17.80 72.95 28.62 All features 100 100 100 Table 4: Results of feature ablation analysis (evaluation measures relative to full model) is essentially language-independent. It requires only a large parsed corpus and a seed set of nouns from both classes. Our feature ablation analysis indicates that full parsing may even be dispensable, as long as Adj-N-pairs can be identified reliably. In the current paper, we have mostly ignored the issue of ambiguous nouns. In future work, we plan to apply our model to the disambiguation of nouns instances (rather than lemmas), which will involve considerably more sparsity. Acknowledgments. This work was partially supported by the EC-funded project EXCITE- MENT (FP7 ICT-287923). 262 Proceedings of KONVENS 2012 (Main track: poster presentations), Vienna, September 20, 2012
References Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2008. The WaCky Wide Web : A Collection of Very Large Linguistically Processed Web-Crawled Corpora. Language Resources and Evaluation, 43(3):209–226. Kurt Eberle, Gertrud Faaß, and Ulrich Heid. 2009. Corpus-based identification and disambiguation of reading indicators for german nominalizations. In Proceedings of Proceedings of the 5th Corpus Linguistics Conference, Liverpool, UK. Joseph L Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378–382. Lynette Hirschman and Robert Gaizauskas. 2002. Natural language question answering: the view from here. Natural Language Engineering, 7(4):275–300. George Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine Miller. 1990. Introduction to WordNet: An On-Line Lexical Database. International Journal of Lexicography, 3(4):235–244. Patrick Pantel and Marco Pennacchiotti. 2006. Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations. In Proceedings of COLING/ACL, pages 113–120, Sydney, Austrial. Yves Peirsman, Kris Heylen, and Dirk Geeraerts. 2008. Size matters: tight and loose context definitions in English word space models. In Proceedings of the ESSLLI Workshop on Distributional Lexical Semantics, pages 34–41. Aina Peris, Mariona Taulé, and Horacio Rodríguez. 2012. Empirical methods for the study of denotation in nominalizations in spanish. Computational Linguistics. To appear. Deepak Ravichandran and Eduard H Hovy. 2002. Learning surface text patterns for a Question Answering System. In Proceedings of ACL, Philadelphia, PA. Roser Saurí, Robert Knippen, Marc Verhagen, and James Pustejovsky. 2005. Evita: a robust event recognizer for QA systems. In Proceedings of HLT/EMNLP, pages 700–707, Vancouver, BC. Michael Schiehlen. 2003. A cascaded finite-state parser for German. In Proceedings of EACL, pages 163–166, Budapest, Hungary. Michael Thelen and Ellen Riloff. 2002. A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts. In Proceedings of EMNLP, pages 214–221, Philadelphia, PA. Matthew J Traxler, Martin J Pickering, and Brian McElree. 2002. Coercion in sentence processing: evidence from eye-movements and self-paced reading. Journal of Memory and Language, 47(4):530–547. Peter D Turney and Patrick Pantel. 2010. From Frequency to Meaning: Vector Space Models of Semantics. Artificial Intelligence, 37(1):141–188. 263 Proceedings of KONVENS 2012 (Main track: poster presentations), Vienna, September 20, 2012
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11 th Conference on Natural Languag
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Table of Contents Main conference P
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Comparing variety corpora with vis-
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LexSem 2012: Workshop on Recent Dev
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KONVENS 2012 The 11 th Conference o
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Organizers: Jeremy Jancsary (ÖGAI/
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Ines Rehbein (Potsdam University) M
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Integrating viewpoints into newspap
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Phonetically aided syntactic parsin
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The Statistical Approach to Natural
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Data-driven Knowledge Extraction fo
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4 The Different Relations Types In
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Relation Type #Patterns Examples Su
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Suits-to Served-with Substituted-by
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References Timothy Chklovski and Pa
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1.2 Problem Definitions In a MRA, t
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oot person organisation product ...
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Group Organis. Persons Products all
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Method α α+β α+γ all SentiWS 0
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A Supervised POS Tagger for Written
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Our tagset mixes and matches tags a
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ecause the enclitic pronoun is an i
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stochastic rules as opposed to larg
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Kevin Duh, and Katrin Kirchhoff. 20
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forms, and that existentials can be
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Pseudo Verb Punctuation PNC
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NLP Workflow for On-line Definition
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nological phrases on the basis of p
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Figure 2: A screenshot of the entir
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When analyzing the evaluation sets
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Projecting Semantic Roles via Tai M
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quent prediction, return it (iii) i
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for the B/T/FT settings. This is pr
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ut it is implied by Table 3 and Fig
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References Wanxiang Che, Zhenghua L
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Our ultimate goal is to develop com
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ADORNING CAUSE_FLUIDIC_MOTION COTHE
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English nouns, verbs, adjectives, a
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considerably worse than FrameNet. T
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Extraction. In Proceedings of the 1
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lexicon from a document-derived voc
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Initialize hypernym set H to contai
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ulary distance. Let d(a, b) denote
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the reduced lexicon (243) was neare
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Adding nominal spice to SALSA - fra
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SALSA verbs nouns 0 FE 0 2 1 FE 68
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gen, which in one of its meanings i
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In the next section we describe how
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Acknowledgments This work was funde
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most commonly used (Turney and Pant
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anteed because the forward feature
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5 000 feat ICA50 SVD50 SENNA Semcat
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words bicycle and tricycle go with
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of the Twelfth European Conference
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incorporate the context information
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those candidate phrases that are an
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5.2 Evaluation Metrics for WPD The
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Runs F-measures α=0.5 α=0.2 Pur.
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Evaluations (SemEval-2007), June (2
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2 Related Work The majority of pape
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and Roth, 2009; Lin and Wu, 2009).
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Features Dev Test CoNLL-2003 w 0 +
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Feature Dev Test Test+ — 93.78 91
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the Joint Conference of the 47th An
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tion. It was first included as a sp
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indicates the presence of the ellip
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[Armada britainiarrak] [Ipar Irland
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Información, (JOTRI 2003), pages 1
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Phonetically aided Syntactic Parsin
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mar network or PCFG. Grammar networ
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phonetic confusion that has the con
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in confusion in the network. This i
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Linguistic analyses of the LAST MIN
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Table 1: Comparison between corpora
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Table 2: Most frequent POS patterns
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Table 3: A problematic dialog situa
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6 Summary We have presented the cur
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S-restricted monotone alignments St
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s k n i i: f phh ohehnh ih xh s k n
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Dataset µ G µ P P [G>15] P [P >15
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Perplexity H(L | P ) 2PKE-Uni 7.002
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(1, 1) (2, 1) (3, 1) (4, 1) (1, 2)
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Use of linguistic features for impr
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whereas English has a verb-initial
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Accuracy of Accuracy of Accuracy of
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English Persian Train: Sentences 30
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to compensate for sparsity of paral
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Name # Documents # Tokens # Lemmas
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Figure 1: Precision-Recall graphs c
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A Multi-level Annotation Model for
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and sentence level with the aim of
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(Schmid, 1995; Schiller et al., 199
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EI EII Tag AKR MARK Typography ITER
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References Saima Aman and Stan Szpa
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Robust processing of noisy web-coll
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1. first strip of the papery skin o
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References Jon Chamberlain, Massimo
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ments between its LSRs. Additionall
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Figure 3: In the sense comparison v
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Using Information Retrieval Technol
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against every tree. Others avoid th
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A 0.780715 NN Figure 4: A sp
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# #Documents Index Size 1 29,704 1.
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References P. Bański, C. Belica, H
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(2012), in their corpus study of va
Page 211 and 212: DET NSUBJ ADV OBJA AMOD Seine Tante
Page 213 and 214: A Computational Semantic Analysis o
Page 215 and 216: (6) a. Is Spain very large? b. Is B
Page 217 and 218: References Alan C. Bale. 2008. A Un
Page 219 and 220: 219 Proceedings of KONVENS 2012 (Ma
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Page 223 and 224: Using subcategorization frames to i
Page 225 and 226: 5.2 Word clustering based on the Le
Page 227 and 228: erage of english grammars. In Proce
Page 229 and 230: cases not disambiguated using langu
Page 231 and 232: Not surprisingly, the results show
Page 233 and 234: Automatic Identification of Languag
Page 235 and 236: Differences also appear at the synt
Page 237 and 238: Acknowledgments The authors express
Page 239 and 240: 2 Related Work Previous work on POS
Page 241 and 242: The XY tag is used for all non-word
Page 243 and 244: Comparing Variety Corpora with Vis-
Page 245 and 246: INPUT variety corpus comparability
Page 247 and 248: References Andrea Abel and Stefanie
Page 249 and 250: Features Context features (token wi
Page 251 and 252: 0.7 0.7 0.6 0.6 0.5 0.5 F-measure 0
Page 253 and 254: ties into fine-grained classes. In
Page 255 and 256: verbials (e.g., therefore, still).
Page 257 and 258: word raw freq. non conn. conn. (of
Page 259 and 260: Corpus-based Acquisition of German
Page 261: Events Objects IPrec CPrec IPrec CP
Page 265 and 266: Preface The present proceedings con
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Page 269 and 270: Unsupervised Sentiment Analysis wit
Page 271 and 272: pling (Geman and Geman, 1984) to es
Page 273 and 274: References Anders Björkelund, Bern
Page 275 and 276: Class # Events in % neutral 45’01
Page 277 and 278: 3.1.1 Rules Let t 1..n be tokens of
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Page 281 and 282: Codebuch Akteursanalyse. rev. 2011.
Page 283 and 284: of our work in the whole applicatio
Page 285 and 286: 3.4 Negation and graduation Valence
Page 287 and 288: local context. For instance, in une
Page 289 and 290: n’était pas très favorable au G
Page 291 and 292: Domain-specific variation of sentim
Page 293 and 294: query building blocks comments real
Page 295 and 296: References Monica Bednarek. 2006. E
Page 297 and 298: 3 Basic Methodology of Annotation T
Page 299 and 300: (3) A. V případě jeho kandidatur
Page 301 and 302: 4.3 Auxiliary data - Mall.cz We hav
Page 303 and 304: Overlap f-score Source 0.750 Polar
Page 305 and 306: A Phrase-Based Opinion List for the
Page 307 and 308: the Stuttgart-Tübingen Tagset (STT
Page 309 and 310: As for noun-based opinion phrases a
Page 311 and 312: More than half of the words and phr
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treebank. In Proceedings of the Wor
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Apart from these, we distinguish op
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close variants)”. In fact importa
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What is the Meaning of 5 *’s? An
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For each category, we follow (Const
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Positivity Term 2.87692577130 elske
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In recent years, the use of rating
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KONVENS 2012 LThist 2012: First Int
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Organisation Committee: Thierry Dec
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Workshop Program Friday, September
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Rule-Based Normalisation of Histori
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isation rules were applied to the i
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ments in the gold standard have bee
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cation in the contemporary SUC samp
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proach. References Maria Ågren, Ro
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2 Related Work There has been consi
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vided to ensure that they are norma
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alects, or even languages. Norma ca
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ization (rather than modernization)
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The Sketch Engine as infrastructure
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2.1 The Corpus of English Dialogues
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Other notable contrasts are that 17
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Evaluating a Post-editing Approach
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Figure 1: Example of a marriage lic
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all possible concatenations of indi
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Table 3: Post-editing results. Time
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okstaffua, bokstaffwa, bokstafwa, b
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Rule Cost v↔u 0.1 v↔w 0.1 u↔o
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graphic word boundaries and conside
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ation. Gulordava and Baroni (2011),
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ut adding a statistical measure to
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e.g. philosophical and political pr
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of the EACL 2009 Workshop on GEMS:
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Most of the research on genre class
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Precision Recall F 1 Unigrams 0.551
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The DTA ‘base format’: A TEI-Su
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Lib are complementary in the sense
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inline metadata solutions for edito
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ing. The fourth level contains an e
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the work package ‘Resources and T
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is mainly used with parallel corpor
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Corrections by the editor were omit
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Accuracy N % All Words 781/1000 78.
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$$ $$ $$ $$ $$ $$ $$ $$ $$ Figure 4
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Digitised Historical Text: Does it
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3 Related Work Previous research on
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5.1 End-of-line Soft Hyphen Deletio
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quite difficult to get an idea of h
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References Ahmad Abdulkader and Mat
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cision, recall, and F1. Section 4 d
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not corrected corrected p P R F1 P
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From Semi-Automatic to Automatic Af
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Figure 2: ER-Diagramm of the Morphi
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Figure 4: Morphilizer’s implement
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Figure 6: Architecture of Morphorm
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References Harald R. Baayen, R. Pie
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edition has been adopted. This is d
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marginalia, emendations or punctuat
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Word Lemma Class Subclass Type Tens
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Figure 3. Sample screenshot of comp
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KONVENS 2012 LexSem 2012: Workshop
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English) closely and discusses the
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Workshop Program Friday, September
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verbs were collected mostly from an
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3 Theoretical Background Let us now
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are sensible to semantic properties
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Cançado, Márcia and Amaral, Luana
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morphological tagger for Russian la
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whom/which something is taken. (7cz
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Verb class same frame different fra
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Construction Grammar are introduced
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posed to Hon åker på måndag ‘S
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nitions of Agent, for example. Inst
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Framenet++, VR dnr 2010-6013), by t
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Källström and Inger Lindberg, edi
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scoring function σ : W ×L×W →
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esults in the triple 〈heutige Son
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Unlexicalized links 7,833,635 n (i)
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The difference in accuracies betwee
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Towards high-accuracy bilingual phr
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over a set of candidate translation
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each soft restriction soft m set on
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(> 98% in scalable precision) and a
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References Colin Bannard and Chris
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Preface The need for automatically
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15:50-16:15 Coffee break 16:15-16:4
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Standards for the technical infrast
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⎡ ⎤ phrase CATEGORY np ⎡ ⎤
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The representation of the feature v
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ement, while the interpretation as
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hand, checking the validity of FSR
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Standardizing metadata descriptions
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2 LMF and semantic interoperability
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can register and create DCs. Users
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all kinds of lexemes, (see Cruse (1
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constraints, because many aspects o
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Menzo Windhouwer. 2012. RELcat: a R
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the meeting in Rome as a starting p
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As these documents provide the refe
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Two multi-layer annotated corpora r
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As audio data is not directly store
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Jörg Mayer. 1995. Transcription of
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Towards standardized lexical semant
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Getting involved into language reso
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