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Query 5 Query 6 Sample reponse #hits reponse #hits 1 0.015s 10 0.043s 9 2 0.066s 833 0.110s 564 3 0.044s 915 0.158s 624 4 0.018s 70 0.015s 52 5 0.055s 985 0.087s 676 6 0.026s 374 0.039s 279 7 0.068s 1,369 0.098s 964 Table 4: Queries 5 and 6 for all sample corpora (Table 1), reporting response time and number of hits. Query 7 Sample reponse #hits 1 0.333s 8,489 2 4.234s 186,969 3 9.032s 421,229 4 10.721s 522,118 5 20.362s 910,038 6 19.543s 892,494 7 19.544s 894.905 Table 5: Query 7 for all sample corpora (Table 1), reporting response time and number of hits; search was aborted after 1 million matches. per. However, parsing the XML input files and building indexes for the corpora reported in Table 1 took between 4 and 60 hours. Anyway, indexing is not the most time-critical part in the KorAP scenario: new DeReKo versions are released only twice a year. At those points, they can be indexed in background while users can still use the previous release. The search, on the other hand, is expected to deliver instant results whenever possible. There might be compromises necessary in case of very complex queries, but the querying side is where KorAP places emphasis on performance. In short: the engine has not been optimized to build indexes fast, but to be queried fast. The reported response times reveal one result very cleary: the querying time depends on the number of hits more than on the size of the corpora or indexes. In a simple token query (Query 1), sample corpus 3 contains many more hits than sample 4 which results in a threefold response time, despite having only approximately half of the size. Less obvious differences between the corpora such as document sizes, vocabulary and other factors probably play a role too, but the number of hits seems to be most significant. Not surprisingly, more complex queries take longer to process, but reply times still increase less than linearly in relation to corpus size. Intersections only increase response significantly when there are very many hits either, as in Query 7 (Table 5). In order to avoid intersections with large result sub-sets, one approach is a smarter distribution of indexes, so that joins can be performed at an early stage with smaller sets, before the final reduce step is performed on the full set. Another factor is query optimization so that complex queries are rewritten in order to avoid intersections and set differences whenever possible. 6 Summary & Outlook We have used the Lucene engine to develop an indexing module for the KorAP corpus analysis platform. We have implemented a Lucene analyzer class that handles pre-analysed texts and corpora with multi-level stand-off annotations. The platform shifts away the task of segmenting, tokenizing, and analysing corpora towards the users and external tools and is ready to include new annotations of different kinds. We have presented a way to apply inverted indexes within a MapReduce-like environment, parallelizing tasks and making the platform scalable and ready to process very large corpora. Implicitly, this work demonstrates that techniques and software from the related field of IR can successfully be applied for linguistic search tasks. Acknowledgements This work has been made possible by the Institut für Deutsche Sprache (IDS) in the context of the KorAP project, funded by the Leibniz- Gemeinschaft and is a direct result of the work with the other KorAP core team members Piotr Bański and Elena Frick. Further IDS colleagues from within and outside the KorAP team have contributed important theoretical advice and practical help: Cyril Belica, Peter Fankhauser, Marc Kupietz, Roman Schneider, Oliver Schonefeld, and the Cosmas-II-Team: Franck Bodmer, Peter Harders, Helge Krause. 206 Proceedings of KONVENS 2012 (Main track: poster presentations), Vienna, September 19, 2012
References P. Bański, C. Belica, H. Krause, M. Kupietz, C. Schnober, O. Schonefeld, and A. Witt. 2011. KorAP data model: first approximation, December. P. Bański, Peter M. Fischer, E. Frick, E. Ketzan, M. Kupietz, C. Schnober, O. Schonefeld, and A. Witt. 2012. The new IDS corpus analysis platform: Challenges and prospects. In Proceedings of LREC-2012, Istanbul, May. F. Bodmer. 2005. COSMAS II. Recherchieren in den Korpora des IDS. Sprachreport, 21(3):2–5. S. Brin and L. Page. 1998. The anatomy of a largescale hypertextual web search engine. Computer networks and ISDN systems, 30(1-7):107–117. C. Chiarcos, J. Ritz, and M. Stede. 2009. By all these lovely tokens...: merging conflicting tokenizations. In Proceedings of the Third Linguistic Annotation Workshop, pages 35–43. Association for Computational Linguistics. J. Dean and S. Ghemawat. 2004. MapReduce: Simplified data processing on large clusters. In OSDI’04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December. R. Fiehler and P. Wagener. 2005. Die Datenbank Gesprochenes Deutsch (DGD)-Sammlung, Dokumentation, Archivierung und Untersuchung gesprochener Sprache als Aufgaben der Sprachwissenschaft. Gesprächsforschung-Online-Zeitschrift zur verbalen Interaktion, 6:136–147. S. Ghodke and S. Bird. 2008. Querying linguistic annotations. In Proceedings of the 13th Australasian Document Computing Symposium, pages 69–72. S. Ghodke and S. Bird. 2010. Fast query for large treebanks. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 267–275. Association for Computational Linguistics. IDS. 2011. Deutsches Referenzkorpus / Archiv der Korpora geschriebener Gegenwartssprache 2011-II. D. Janus and A. Przepiórkowski. 2007. Poliqarp: An open source corpus indexer and search engine with syntactic extensions. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pages 85–88. Association for Computational Linguistics. A. Kilgarriff. 2007. Googleology is bad science. Computational Linguistics, 33(1):147151. D.E. Knuth. 1997. The Art of Computer Programming. Addison-Wesley, Reading, Massachusetts, 3rd edition. M. Kupietz, C. Belica, H. Keibel, and A. Witt. 2010. The German reference corpus DeReKo: a primordial sample for linguistic research. In LREC 2010 Main Conference Proceedings. Malta. Lucene, 2012. Apache Lucene – Index File Formats. The Apache Software Foundation, April. M. McCandless, E. Hatcher, and O. Gospodnetić. 2010. Lucene in Action. Manning Publications Co., 2nd edition, July. R. Perkuhn, H. Keibel, and M. Kupietz. 2012. Korpuslinguistik. Fink. (UTB 3433), Paderborn. H. Schmid. 1994. Probabilistic part-of-speech tagging using decision trees. In Proceedings of international conference on new methods in language processing, volume 12, pages 44–49. Manchester, UK. R. Schneider. 2012. Evaluating DBMS-based access strategies to very large multi-layer corpora. In Proceedings of the LREC 2012 Workshop: Challenges in the management of large corpora. European Language Resources Association (ELRA). J. Sinclair, 2004. Trust the text, chapter 1. Routledge, Milton Park, UK. A. Sokirko, 2003. A technical overview of DWDS/Dialing Concordance. Henry S. Thompson and David McKelvie. 1997. Hyperlink semantics for standoff markup of read-only documents. In Proceedings of SGML Europe. M. Volk. 2002. Using the web as corpus for linguistic research. Tähendusepüüja. Catcher of the Meaning. A Festschrift for Professor Haldur Oim. A. Zeldes, J. Ritz, A. Lüdeling, and C. Chiarcos. 2009. Annis: A search tool for multi-layer annotated corpora. In Proceedings of Corpus Linguistics, pages 20–23. C. Zhang, J. Naughton, D. DeWitt, Q. Luo, and G. Lohman. 2001. On supporting containment queries in relational database management systems. In Proceedings of the 2001 ACM SIGMOD international conference on Management of data, volume 30, pages 425–436. ACM. 207 Proceedings of KONVENS 2012 (Main track: poster presentations), Vienna, September 19, 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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Page 157 and 158: s k n i i: f phh ohehnh ih xh s k n
Page 159 and 160: Dataset µ G µ P P [G>15] P [P >15
Page 161 and 162: Perplexity H(L | P ) 2PKE-Uni 7.002
Page 163 and 164: (1, 1) (2, 1) (3, 1) (4, 1) (1, 2)
Page 165 and 166: Use of linguistic features for impr
Page 167 and 168: whereas English has a verb-initial
Page 169 and 170: Accuracy of Accuracy of Accuracy of
Page 171 and 172: English Persian Train: Sentences 30
Page 173 and 174: to compensate for sparsity of paral
Page 175 and 176: Name # Documents # Tokens # Lemmas
Page 177 and 178: Figure 1: Precision-Recall graphs c
Page 179 and 180: A Multi-level Annotation Model for
Page 181 and 182: and sentence level with the aim of
Page 183 and 184: (Schmid, 1995; Schiller et al., 199
Page 185 and 186: EI EII Tag AKR MARK Typography ITER
Page 187 and 188: References Saima Aman and Stan Szpa
Page 189 and 190: Robust processing of noisy web-coll
Page 191 and 192: 1. first strip of the papery skin o
Page 193 and 194: References Jon Chamberlain, Massimo
Page 195 and 196: ments between its LSRs. Additionall
Page 197 and 198: Figure 3: In the sense comparison v
Page 199 and 200: Using Information Retrieval Technol
Page 201 and 202: against every tree. Others avoid th
Page 203 and 204: A 0.780715 NN Figure 4: A sp
Page 205: # #Documents Index Size 1 29,704 1.
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
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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
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word raw freq. non conn. conn. (of
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Corpus-based Acquisition of German
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Events Objects IPrec CPrec IPrec CP
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References Marco Baroni, Silvia Ber
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Preface The present proceedings con
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Workshop Program Friday, September
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Unsupervised Sentiment Analysis wit
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pling (Geman and Geman, 1984) to es
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References Anders Björkelund, Bern
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Class # Events in % neutral 45’01
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3.1.1 Rules Let t 1..n be tokens of
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Human 6 System 6 Human 4 System 4 A
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Codebuch Akteursanalyse. rev. 2011.
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of our work in the whole applicatio
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3.4 Negation and graduation Valence
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local context. For instance, in une
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n’était pas très favorable au G
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Domain-specific variation of sentim
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query building blocks comments real
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References Monica Bednarek. 2006. E
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3 Basic Methodology of Annotation T
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(3) A. V případě jeho kandidatur
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4.3 Auxiliary data - Mall.cz We hav
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Overlap f-score Source 0.750 Polar
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A Phrase-Based Opinion List for the
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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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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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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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