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evaluated differently in the contact registers? frames comp. sci. contact reg. F % F % accuracy 2 2.82 2 2.56 being necessary 2 2.82 1 1.28 correctness 6 8.45 2 2.56 desirability 21 29.58 9 11.54 difficulty 15 21.13 14 17.95 importance 2 2.82 8 10.26 likelihood 4 5.63 4 5.13 obviousness 2 2.82 0 0.00 sufficiency 2 2.82 2 2.56 suitability 4 5.63 4 5.13 usefulness 10 14.08 23 29.49 success 0 0.00 4 5.13 fame 0 0.00 2 2.56 Table 4: TARGET BE eval-expr: eval. frames with algorithm To answer this question, we extract the sentiment expressions used to evaluate algorithm with CQP and categorize them semantically to have a basis of comparison. The semantic categorization is done manually according to FrameNet (Ruppenhofer et al., 2010), which provides a dataset of semantic frames of lexical units. The sentiment expressions are inserted in the online FrameNet interface which outputs the respective frames for each expression. Ultimately, we aim an automatic categorization. Table 4 shows semantic frames used in the TARGET BE eval-expr feature in computer science and the contact registers. The percentages show that computer science uses algorithm more frequently with the frames desirability (e.g., perfect, good) and correctness (by correct), the contact registers instead more frequently with importance (e.g., key element, important) and usefulness (e.g., helpful, useful). To test whether these differences are significant, we calculate the fisher’s exact test, a univariate method for small raw frequencies, with the R environment (R Development Core Team, 2010) for the relevant frames (desirability, correctness, importance and usefulness). The p-value for this comparison is 0.00119, showing that computer science and the contact registers differ significantly in their use of these frames with algorithm in the TARGET BE eval-expr feature. However, while the expression of correctness may be an evaluative act in some domains,to call an algorithm ’correct’ in computer science is rather a factual attribution. Thus, the concepts used to evaluate may vary according to register. We have to investigate further the semantic diversification related to scientific registers as well as the appropriateness of FrameNet frames for academic concepts. Nevertheless, in summary we can say that for the TARGET BE eval-expr feature the contact registers adopt targets from the seed register computer science, but in the case of algorithm evaluate it differently. 5 Summary and envoi In this paper, we have introduced a methodology to analyze sentiment expressions in academic writing. Our focus of analysis has been on specific lexico-grammatical features used to evaluate targets. As our overarching goal is to analyze registers emerged by register contact, we have (1) analyzed whether opinion targets are adopted from the seed register by the contact register, and (2) whether these targets are evaluated in the same way by both seed and contact registers. The analysis shows that there are domain-specific targets adopted by the contact registers. However, the evaluation of the targets can differ, as in the case of algorithm. Thus, we were able to detect domain-specific variation in terms of sentiment expressions, i.e. for interpersonal lexicogrammatical features. In terms of methods, we have applied a corpusbased approach that allows to detect, extract and annotate features related to sentiment expressions semi-automatically in academic writing. In the future, we will widen the range of targetoriented features moving towards a more comprehensive picture of domain-specific variation in terms of targets. We also have to take into account the semantic diversification related to different registers and investigate further the appropriateness of FrameNet categories to describe concepts used within scientific writing. As our model of analysis accounts also for writer- and reader-oriented features, we investigate these features as well. Doing so, we aim at analyzing domain-specific variation in terms of the strength of the writer’s presence in specific domains and the reader’s engagement across registers. 294 Proceedings of KONVENS 2012 (PATHOS 2012 workshop), Vienna, September 21, 2012
References Monica Bednarek. 2006. Evaluation in Media Discourse: Analysis of a Newspaper Corpus. Continuum. Douglas Biber and Edward Finegan. 1989. Drift and the evolution of English style: a history of three genres. Language, 65(3):487–517. Douglas Biber, Stig Johansson, and Geoffrey Leech. 1999. Longman Grammar of Spoken and Written English. Longman, Harlow. 2010. The IMS Open Corpus Workbench. http://www.cwb.sourceforge.net. Stefania Degaetano and Elke Teich. 2011. The lexicogrammar of stance: an exploratory analysis of scientific texts. In Stefanie Dipper and Heike Zinsmeister, editors, Bochumer Linguistische Arbeitsberichte 3 - Beyond Semantics: Corpus-based Investigations of Pragmatic and Discourse Phenomena, Bochum. Stefania Degaetano-Ortlieb, Kermes Hannah, Ekaterina Lapshinova-Koltunski, and Teich Elke. 2012. SciTex A Diachronic Corpus for Analyzing the Development of Scientific Registers. In Paul Bennett, Martin Durrell, Silke Scheible, and Richard J. Whitt, editors, New Methods in Historical Corpus Linguistics, volume Corpus Linguistics and Interdisciplinary Perspectives on Language - CLIP, Vol. 3. Narr. Stefan Evert and Hannah Kermes. 2003. Annotation, storage, and retrieval of mildly recursive structures. In Annotation, storage, and retrieval of mildly recursive structures, Lancaster, UK. Stefan Evert, 2005. The CQP Query Language Tutorial. IMS Stuttgart. CWB version 2.2.b90. M.A.K. Halliday. 2004. An Introduction to Functional Grammar. Arnold, London. Susan Hood. 2010. Appraising Research: Evaluation in Academic Writing. Palgrave Macmillan. Susan Hunston and Geoff Thompson. 2001. Evaluation in Text: Authorial stance and the construction of discourse. Oxford University Press, Oxford. Susan Hunston. 2011. Corpus approaches to evaluation: phraseology and evaluative language. Taylor & Francis. Ken Hyland. 2005. Stance and engagement: a model of interaction in academic discourse. Discourse Studies, 7(2):173–192. Jim R. Martin and Peter R.R. White. 2005. The Language of Evaluation, Appraisal in English. Palgrave Macmillan, London & New York. Lisa McGrath and Maria Kuteeva. 2012. Stance and engagement in pure mathematics research articles: Linking discourse features to disciplinary practices. English for Specific Purposes, 31:161–173. Michael O’Donnell. 2008. The UAM CorpusTool: Software for corpus annotation and exploration. In Proceedings of the XXVI Congreso de AESLA, Almeria, Spain, 3-5 April. R Development Core Team, 2010. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Josef Ruppenhofer, Michael Ellsworth, Miriam R. L. Petruck, Christopher R. Johnson, and Jan Scheffczyk. 2010. Framenet II: Extended theory and practice. Technical report, ICSI. Swapna Somasundaran. 2010. Discourse-level relations for opinion analysis. Ph.D. thesis, University of Pittsburgh. Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. Lexiconbased methods for sentiment analysis. Computational Linguistics, 37(2):267–307. Elke Teich and Peter Fankhauser. 2010. Exploring a corpus of scientific texts using data mining. In S. Gries, S. Wulff, and M. Davies, editors, Corpuslinguistic applications: Current studies, new directions, pages 233–247. Rodopi, Amsterdam and New York. Theresa Ann Wilson. 2008. Fine-grained Subjectivity and Sentiment Analysis: Recognizing the Intensity, Polarity, and Attitudes of Private States. Ph.D. thesis, University of Pittsburgh. 295 Proceedings of KONVENS 2012 (PATHOS 2012 workshop), Vienna, September 21, 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
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DET NSUBJ ADV OBJA AMOD Seine Tante
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A Computational Semantic Analysis o
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(6) a. Is Spain very large? b. Is B
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References Alan C. Bale. 2008. A Un
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219 Proceedings of KONVENS 2012 (Ma
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221 Proceedings of KONVENS 2012 (Ma
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Using subcategorization frames to i
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5.2 Word clustering based on the Le
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erage of english grammars. In Proce
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cases not disambiguated using langu
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Not surprisingly, the results show
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Automatic Identification of Languag
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Differences also appear at the synt
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Acknowledgments The authors express
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2 Related Work Previous work on POS
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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 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
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Page 291 and 292: Domain-specific variation of sentim
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Page 297 and 298: 3 Basic Methodology of Annotation T
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Page 301 and 302: 4.3 Auxiliary data - Mall.cz We hav
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Page 305 and 306: A Phrase-Based Opinion List for the
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Page 313 and 314: treebank. In Proceedings of the Wor
Page 315 and 316: Apart from these, we distinguish op
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Page 319 and 320: What is the Meaning of 5 *’s? An
Page 321 and 322: For each category, we follow (Const
Page 323 and 324: Positivity Term 2.87692577130 elske
Page 325 and 326: In recent years, the use of rating
Page 327 and 328: KONVENS 2012 LThist 2012: First Int
Page 329 and 330: Organisation Committee: Thierry Dec
Page 331 and 332: Workshop Program Friday, September
Page 333 and 334: Rule-Based Normalisation of Histori
Page 335 and 336: isation rules were applied to the i
Page 337 and 338: ments in the gold standard have bee
Page 339 and 340: cation in the contemporary SUC samp
Page 341 and 342: proach. References Maria Ågren, Ro
Page 343 and 344: 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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