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middle of the frequency distribution, so that the problem at large can be solved only to a small extent by tagging. 3.2 POS-context disambiguation rules For the remaining 33 potential connectives (where the POS-tag alone does not give enough information to disambiguate), our next step was to look at their immediate context and determine wheter POS tag patterns can be identified for making the decision. We found that for ten potential connectives, it is indeed possible to formulate a set of context-rules that use the POS-tags of the tokens directly before and after the potential connective, and which perform with an f-score > 0.75. These connectives and the context rules are shown in table 4. The underlined POS-tag is the one the potential connective is annotated with, while the POS-tags before and/or after describe the directly adjacent words. To merge some context rules together the following symbols are used: $ stands for any punctuation mark, VV.* for any full verb and V.FIN for any finite verb. 4 Summary and outlook We provided a comprehensive analysis of the connective ambiguity problem in German and showed for our base set of 41 ambiguous words that • a set of words has a relatively clear tendency to occur in either the connective or non-connective reading, • for eight words, plain POS tagging yields good results for disambiguation, and • for another ten words, fairly reliable POScontext patterns can serve to disambiguate the reading. These results can serve as the basis for an implementation. For the remaining ambiguous words, a straightforward first step is to simply assume the majority reading as taken from Table 1. Alternatively, one could search more intensely for POS patterns by working with larger corpora and machine learning techniques. And another option to explore, of course, is to test whether syntactic chunking or even full parsing can be trusted to resolve the ambiguities. For English, this has been done by (Hutchinson, 2004), using the Charniak parser. In an interesting experiment, (Versley, 2010) suggested to project connective annotations from English onto German data, thus cirvumventing the problem of lacking (German) training data for machine learning. Working directly on the output of a German parser, however, has to our knowledge not been attempted yet. References Nicholas Asher and Alex Lascarides. 2003. Logics of Conversation. Cambridge University Press, Cambridge. Petra Bayerl. 2004. Disambiguierung deutschsprachiger Diskursmarker: Eine Pilot- Studie. Linguistik Online, 18. Stefanie Dipper and Manfred Stede. 2006. Disambiguating potential connectives. In Miriam Butt, editor, Proc. of KONVENS ’06, pages 167–173, Konstanz. Ben Hutchinson. 2004. Mining the web for discourse markers. In Proc. of LREC-04, Lisbon. William Mann and Sandra Thompson. 1988. Rhetorical structure theory: Towards a functional theory of text organization. TEXT, 8:243–281. Daniel Marcu. 2000. The theory and practice of discourse parsing and summarization. MIT Press, Cambridge/MA. Renate Pasch, Ursula Brauße, Eva Breindl, and Ulrich Herrmann Waßner. 2003. Handbuch der deutschen Konnektoren. Walter de Gruyter, Berlin/New York. Emily Pitler and Ani Nenkova. 2009. Using syntax to disambiguate explicit discourse connectives in text. In Proc. of the ACL-IJCNLP 2009 Conference Short Papers, pages 13–16, Suntec, Singapore. Association for Computational Linguistics. Livia Polanyi. 1988. A formal model of the structure of discourse. Journal of Pragmatics, 12:601–638. Helmut Schmid. 1994. Probabilistic part-of-speech tagging using decision trees. In Proc. of International Conference on New Methods in Language Processing, pages 44–49, Manchester. Angela Schneider. 2012. Disambiguierung von Diskurskonnektoren im Deutschen. Bsc. thesis, Dept. Linguistics, Universität Potsdam, March. Yannick Versley. 2010. Discovery of ambiguous and unambiguous discourse connectives via annotation projection. In Proc. of the Workshop on the Annotation and Exploitation of Parallel Corpora (AEPC), Tartu/Estland. 256 Proceedings of KONVENS 2012 (Main track: poster presentations), Vienna, September 20, 2012
word raw freq. non conn. conn. (of 200) (of 200) und 1.550.931 121 79 als 382.237 183 17 auch 351.946 199 1 wie 268.878 188 12 so 263.201 163 37 nur 226.392 199 1 aber 219.248 55 145 dann 106.610 56 144 doch 92.350 120 80 da 88.776 110 90 denn 60.647 84 116 also 54.251 142 58 seit 39.670 179 21 während 39.320 98 102 darauf 32.630 187 13 dabei 27.616 181 19 allein 24.781 183 17 wegen 20.994 9 191 dafür 20.857 178 22 daher 18.874 16 184 sonst 18.139 144 56 statt 15.749 163 37 zugleich 15.093 119 81 allerdings 14.481 33 167 dagegen 13.388 52 148 ferner 12.622 18 182 trotz 10.921 20 180 darum 10.090 120 80 außer 10.084 185 15 soweit 8.884 31 169 entgegen 6.904 142 58 danach 6.351 85 115 wonach 3.042 186 14 worauf 2.840 103 97 weshalb 2.638 124 76 seitdem 2.396 139 61 womit 2.048 109 91 aufgrund 1.806 200 0 allenfalls 1.162 177 23 wogegen 614 54 146 nebenher 286 93 107 weswegen 188 111 89 POS = Prec. Recall connective in % in % denn KON 85.6 94.2 doch KON 86.3 83.1 entgegen APPO, 97.9 65.7 APPR seit KOUS 77.8 84.0 seitdem KOUS 81.0 77.0 trotz APPR 99.5 100 während KOUS 81.8 96.1 wegen APPO, 98.3 100 APPR Table 2: POS tagging results for connective readings POS = non- Prec. Recall connective in % in % denn ADV 91.9 79.1 doch ADV 90.2 92.1 entgegen PTKVZ 85.7 99.3 seit APPR 98.0 96.6 seitdem PAV 90.2 92.1 trotz NN 100 95.0 während APPR 95.3 78.6 wegen NN 100 60.0 Table 3: POS tagging results for non-connective reading Table 1: Ambiguous connectives with their raw frequencies and non-/connective distributions for 200 random occurrences 257 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
Page 205 and 206: # #Documents Index Size 1 29,704 1.
Page 207 and 208: References P. Bański, C. Belica, H
Page 209 and 210: (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
Page 221 and 222: 221 Proceedings of KONVENS 2012 (Ma
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: verbials (e.g., therefore, still).
Page 259 and 260: Corpus-based Acquisition of German
Page 261 and 262: Events Objects IPrec CPrec IPrec CP
Page 263 and 264: References Marco Baroni, Silvia Ber
Page 265 and 266: Preface The present proceedings con
Page 267 and 268: Workshop Program Friday, September
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
Page 279 and 280: Human 6 System 6 Human 4 System 4 A
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
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the Stuttgart-Tübingen Tagset (STT
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As for noun-based opinion phrases a
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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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