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ever, sentimental ascriptions could be particularly relevant for an actor if they appear in titles, leads or image captions of a newspaper article. Apart from that, a thorough assessment of more powerful learning algorithms is indispensable for future iterations on our prototype system. This should be accompanied by including additional carefully thought out features, such as polarity class ratios or, as outlined in the previous section, structural information. Also, we will have to separately evaluate our polarity composition component (as illustrated in section 3.1.1) in order to consider possible extensions. As for polarity class granularity, it is remarkable that controversial cases were particularly hard to identify, even in the four class model. The fög coding manual says that an event is controversial if “the reputation object is discussed controversially; positive and negative ascriptions are equally balanced.” 5 Our classifier had no means of assessing the balance of negative and positive ascriptions when trained on our feature set described in section 3.2—there were of course counts for POS and NEG zerograms, but due to the Naive Bayes independence assumption, no positive-negative ratio could be obtained. From a pragmatic point of view, one could argue that the system’s moderate accuracy could partially be outweighed by quantitative considerations. As mentioned in section 4.1, the pipeline processes an article in 5.3 seconds on average, and in the nature of things, classifier decisions are fully reproducible. Viewed in this light, it does not seem unreasonable to use automated systems for work that is rather tedious for human experts, such as pre-selecting texts or encoding articles where the polarity of an actor is perfectly obvious. 6 Conclusion In our exploratory work, we sought to assess the feasibility of automating a fine-grained classification process for media reputation analysis. We have trained two prototype classifiers relying on a lightweight approach to lexical sentiment composition, which we evaluated on a set of 200 pre- 5 “Das Reputationsobjekt wird kontrovers diskutiert; Positiv- und Negativzuschreibungen halten sich die Waage.” (fög, 2011) viously annotated newspaper articles. It clearly turned out that our approach is not suitable for handling a six-class polarity model. However, we gained substantial improvement from folding implicit and explicit ascriptions into a four-classmodel, through which we argue that automated approaches have a promising potential in qualitative media reputation analysis. In future work, we will consider structural information of newspaper articles for classification, as well as thoroughly examine the impact of more sophisticated machine learning algorithms on classification accuracy. Currently, we are training and evaluating an additional classifier for a three-partite polarity model, as a distinction between positive, neutral and negative is still most important in high-level aggregations such as the fög Media Reputation Index (Eisenegger and Imhof, 2008). We consider our present work as a motivating first step towards automating qualitative reputation analysis processes, calling for further collaboration in the intersection between sentiment analysis- and media reputation research. Acknowledgments We would like to thank Simon Clematide and Michael Amsler for motivating the use of VISL in our feature extraction pipeline. Also, we are grateful to Corina Tobler for translating the examples provided in section 3. References Chen, Lu, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, and Amit P. Sheth. 2012. Extracting Diverse Sentiment Expressions with Target- Dependent Polarity from Twitter. In Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM) 2012, pages 50–57, Dublin. Choi, Yejin, 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) 2008, pages 793–801, Honolulu. Clematide, Simon, and Manfred Klenner. 2010. Evaluation and extension of a polarity lexicon for German. In Proceedings of the First Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA) 2010, pages 7–13, Lisbon. 280 Proceedings of KONVENS 2012 (PATHOS 2012 workshop), Vienna, September 21, 2012
Codebuch Akteursanalyse. rev. 2011. Center for Research on the Public Sphere and Society (fög), University of Zurich. Internal coding manual for media reputation analysis. Deephouse, David L. 2000. Media reputation as a strategic resource: An integration of mass communication and resource-based theories. Journal of Management, 26(6):1091–1112. Eisenegger, Mark, and Kurt Imhof. 2008. The True, The Good and the Beautiful: Reputation Management in the Media Society. In Ansagr Zerfass, Betteke van Ruler, and Sriramesh Krishnamurthy, editors, Public Relations Research: European and International Perspectives and Innovation. VS Verlag für Sozialwissenschaften, Wiesbaden, pages 125–146. Eisenegger, Mark, Mario Schranz, and Jörg Schneider. 2010. Corporate Reputation and the News Media in Switzerland. In: Craig E. Carroll, editor, Corporate reputation and the news media. Agenda-setting within business news coverage in developed, emerging, and frontier markets. Routledge (Communication series), New York, S. 207–220. Godbole, Namrata, Manjunath Srinivasaiah, and Steven Skiena. 2007. Large-Scale Sentiment Analysis for News and Blogs. In Proceedings of the First International AAAI Conference on Weblogs and Social Media (ICWSM) 2007, Boulder. Hall, Mark, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1):10–18. Hu, Minqing, and Bing Liu. 2004. Mining Opinion Features in Customer Reviews. Proceedings of the 19th national conference on Artifical intelligence (AAAI) 2004, pages 755–760, San Jose. Klenner, Manfred, Angela Fahrni, and Stefanos Petrakis. 2009. PolArt: A Robust Tool for Sentiment Analysis. In Proceedings of NODALIDA 2009, pages 235–238, Odense. Loper, Edward, and Steven Bird. 2002. NLTK: The Natural Language Toolkit. In Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 63–70, Philadelphia. Moilanen, Karo, and Stephen Pulman. 2007. Sentiment Composition. In Proceedings of Recent Advances in Natural Language Processing (RANLP) 2007, pages 378–382, Borovets. Polanyi, Livia, and Annie Zaenen. 2004. Contextual valence shifters. In James G. Shanahan, Yan Qu, and Janyce Wiebe, editors, Computing Attitude and Affect in Text: Theory and Applications. Springer Verlag (The Information Retrieval Series), Dordrecht, pages 1–9. Sennrich, Rico, Gerold Schneider, Martin Volk, and Martin Warin. 2009. A New Hybrid Dependency Parser for German. In Proceedings of the Biennial GSCL Conference 2009, pages 115–124, Tübingen. VISL-group. 2008. http://beta.visl.sdu.dk/constraint grammar.html. Institute of Language and Communication (ISK), University of Southern Denmark. Wilson, Theresa, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing Contextual Polarity in Phrase- Level Sentiment Analysis. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP) 2005, pages 347–354, Vancouver. 281 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
Page 229 and 230: cases not disambiguated using langu
Page 231 and 232: Not surprisingly, the results show
Page 233 and 234: Automatic Identification of Languag
Page 235 and 236: Differences also appear at the synt
Page 237 and 238: Acknowledgments The authors express
Page 239 and 240: 2 Related Work Previous work on POS
Page 241 and 242: The XY tag is used for all non-word
Page 243 and 244: Comparing Variety Corpora with Vis-
Page 245 and 246: INPUT variety corpus comparability
Page 247 and 248: References Andrea Abel and Stefanie
Page 249 and 250: Features Context features (token wi
Page 251 and 252: 0.7 0.7 0.6 0.6 0.5 0.5 F-measure 0
Page 253 and 254: ties into fine-grained classes. In
Page 255 and 256: verbials (e.g., therefore, still).
Page 257 and 258: word raw freq. non conn. conn. (of
Page 259 and 260: Corpus-based Acquisition of German
Page 261 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: Human 6 System 6 Human 4 System 4 A
Page 283 and 284: of our work in the whole applicatio
Page 285 and 286: 3.4 Negation and graduation Valence
Page 287 and 288: local context. For instance, in une
Page 289 and 290: n’était pas très favorable au G
Page 291 and 292: Domain-specific variation of sentim
Page 293 and 294: query building blocks comments real
Page 295 and 296: References Monica Bednarek. 2006. E
Page 297 and 298: 3 Basic Methodology of Annotation T
Page 299 and 300: (3) A. V případě jeho kandidatur
Page 301 and 302: 4.3 Auxiliary data - Mall.cz We hav
Page 303 and 304: Overlap f-score Source 0.750 Polar
Page 305 and 306: A Phrase-Based Opinion List for the
Page 307 and 308: the Stuttgart-Tübingen Tagset (STT
Page 309 and 310: As for noun-based opinion phrases a
Page 311 and 312: More than half of the words and phr
Page 313 and 314: treebank. In Proceedings of the Wor
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
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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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