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agree in the sense that the classification into positive and negative words is consistent in all cases. Adjectives OV PL GPC toll - great +0.87 P 1.0 P 0.43 zufrieden - satisfied +0.82 P 1.0 P 0.49 spannend - exciting +0.78 P 1.0 P 0.17 gut - good +0.67 P 1.0 P 0.32 schwach - weak −0.18 N 0.7 N 0.61 schlecht - bad −0.56 N 1.0 N 0.61 Table 7: Opinion values taken from our list (OV) compared to values from the polarity lexicon (PL) and the GermanPolarityClues (GPC). Quantitatively, each of the two benchmark lists contain more than twice as many words than our list. However, we see an advantage of our approach in the fact that our list not only contains single words but also phrases, so the treatment of negation and intensification gets much easier. Furthermore, instead of only classifying the words into a few polarity classes, we calculated opinion values in a continuous range between −1 and +1 which leads to a more precise assessment when applying the list, e.g., in the field of aspect-based opinion mining. 4.4 Shortcomings and Future Work In Section 3.5 we discussed a correction necessary due to the “J-shaped distribution” of online reviews. This special distribution could cause another effect on the opinion values of single adjectives carrying a negative opinion. Some of these words sometimes are used ironically or in an idiomatic expression and therefore do not express a negative opinion. If these cases are rare and equally distributed over all review title, this effect would not lead to shifts in the opinion values. However, as these “misusages” cannot be regarded as distributed equally due to the “J-shaped distribution” of reviews, this can lead to a shift of the opinion values to positive values for some opinion words. As stated in Section 3.4 we assumed a three star rating to be the expression of a neutral opinion about a product. However, this has not to be exactly the case. In fact, reviews with a three star rating are sometimes regarded as slightly negative ones. This could lead to a shift of opinion values to positive values. If the mean star rating for neutral reviews is shifted by 0.1 stars to 3.1 stars for example, the resulting opinion values would be shifted by 0.05 to the positive side. This systematic uncertainty should be kept in mind for phrases with opinion values having a small statistical error and being close to zero. A general problem of the approach results from the fact, that online reviews were used as a text resource to derive the opinion values. Thus, the vocabulary used in these reviews determines the content of the opinion list. This leads to the conclusion, that opinion lists produced using this approach may be suitable for analyzing user generated content from Web 2.0 sources but may not be applicable for other text resources. To measure the quality of the list, we intent to perform benchmark tests using manually created opinion lists. Later we will perform also a quantitative evaluation. For a later version of the list, we want to enrich it with values for opinion bearing phrases based on verbs. 5 Conclusion In this paper we calculated opinion values for German adjective- and noun-based phrases with a fine granularity using the titles of Amazon reviews together with the star rating. Necessary corrections were applied. It seems as if we got astonishingly good results for more than 1,700 single words and 1,400 phrases, which are to evaluate in detail. The list obtained will be made available to the community soon. Acknowledgments References Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC-10), pages 2200–2204. Sabine Brants, Stefanie Dipper, Silvia Hansen, Wolfgang Lezius, and George Smith. 2002. The TIGER 312 Proceedings of KONVENS 2012 (PATHOS 2012 workshop), Vienna, September 21, 2012
treebank. In Proceedings of the Workshop on Treebanks and Linguistic Theories, pages 24–41. Paula Carvalho, Luís Sarmento, Mário J. Silva, and Eugénio de Oliveira. 2009. Clues for Detecting Irony in User-Generated Contents: Oh...!! It’s “so easy“ ;-). In Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement (TSA-09), pages 53– 56. Yejin Choi and Claire Cardie. 2008. Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-08), pages 793–801. Simon Clematide and Manfred Klenner. 2010. Evaluation and Extension of a Polarity Lexicon for German. In Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA-10), pages 7–13. Andrea Esuli and Fabrizio Sebastiani. 2006a. Determining Term Subjectivity and Term Orientation for Opinion Mining. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), pages 193–200. Andrea Esuli and Fabrizio Sebastiani. 2006b. SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC-06), pages 417–422. Nan Hu, Paul A. Pavlou, and Jennifer Zhang. 2007. Why do Online Product Reviews have a J-shaped Distribution? Overcoming Biases in Online Wordof-Mouth Communication. Marketing Science, 198:7. Nan Hu, Jie Zhang, and Paul A. Pavlou. 2009. Overcoming the J-shaped Distribution of Product Reviews. Communications of the ACM, 52:144–147. Manfred Klenner, Stefanos Petrakis, and Angela Fahrni. 2009. Robust Compositional Polarity Classification. In In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-09), pages 180–184. Jingjing Liu and Stephanie Seneff. 2009. Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-09), pages 161–169. Bing Liu and Lei Zhang. 2012. A Survey of Opinion Mining and Sentiment Analysis. In Charu C. Aggarwal and ChengXiang Zhai, editors, Mining Text Data, pages 415–463. Springer US. Bing Liu, Minqing Hu, and Junsheng Cheng. 2005. Opinion Observer: Analyzing and Comparing Opinions on the Web. In Proceedings of the 14th International World Wide Web Conference (WWW- 05), pages 342–351. Karo Moilanen and Stephen Pulman. 2007. Sentiment Composition. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-07), pages 378–382. Daniela Oelke, Ming Hao, Christian Rohrdantz, Daniel A. Keim, Umeshwar Dayal, Lars-Erik Haug, and Halldór Janetzko. 2009. Visual Opinion Analysis of Customer Feedback Data. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST-09), pages 187–194. Uwe Quasthoff. 1998. Projekt Deutscher Wortschatz. In Gerhard Heyer and Christian Wolff, editors, Linguistik und neue Medien. Proc. 10. Jahrestagung der Gesellschaft für Linguistische Datenverarbeitung., pages 93–99. DUV. Sven Rill, Johannes Drescher, Dirk Reinel, Jörg Scheidt, Oliver Schütz, Florian Wogenstein, and Daniel Simon. 2012. A Generic Approach to Generate Opinion Lists of Phrases for Opinion Mining Applications. In Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM-12). Anne Schiller and Christine Thielen. 1995. Ein kleines und erweitertes Tagset fürs Deutsche. In Tagungsberichte des Arbeitstreffens “Lexikon + Text”, 17./18. Februar 1994, Schloß Hohentübingen, Lexicographica Series Maior, pages 193–203. Niemeyer, Tübingen. Hiroya Takamura, Takashi Inui, and Manabu Okumura. 2005. Extracting Semantic Orientations of Words using Spin Model. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL-05), pages 133–140. Huifeng Tang, Songbo Tan, and Xueqi Cheng. 2009. A survey on sentiment detection of reviews. Expert Systems with Applications, 36:10760–10773. Ulli Waltinger. 2010. GermanPolarityClues: A Lexical Resource for German Sentiment Analysis. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC-10), pages 1638–1642. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing Contextual Polarity in Phrase- Level Sentiment Analysis. In Proceedings of the Human Language Technology Conference (HLT- 05), pages 347–354. 313 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
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Comparing Variety Corpora with Vis-
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INPUT variety corpus comparability
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References Andrea Abel and Stefanie
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Features Context features (token wi
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0.7 0.7 0.6 0.6 0.5 0.5 F-measure 0
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ties into fine-grained classes. In
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verbials (e.g., therefore, still).
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word raw freq. non conn. conn. (of
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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
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
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Page 311: More than half of the words and phr
Page 315 and 316: Apart from these, we distinguish op
Page 317 and 318: close variants)”. In fact importa
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
Page 345 and 346: vided to ensure that they are norma
Page 347 and 348: alects, or even languages. Norma ca
Page 349 and 350: ization (rather than modernization)
Page 351 and 352: The Sketch Engine as infrastructure
Page 353 and 354: 2.1 The Corpus of English Dialogues
Page 355 and 356: Other notable contrasts are that 17
Page 357 and 358: Evaluating a Post-editing Approach
Page 359 and 360: Figure 1: Example of a marriage lic
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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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