Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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References M. Bednarek. 2009. Dimensions of evaluation: cognitive and linguistic perspectives. Pragmatics and Cognition, 17(1):181–193. P. Charaudeau. 1992. Grammaire du sens et de l’expression. Hachette París. V. Hatzivassiloglou and K.R. McKeown. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, pages 174–181, Morristown, NJ, USA. Association for Computational Linguistics. J.R. Martin and P.R.R. White. 2005. The Language of Evaluation: Appraisal in English. Palgrave. M. Noailly. 1999. L’adjectif en français. Editions Ophrys. P. Turney. 2002. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th Annual Meeting of the ACL (ACL’02), pages 417– 424. Philadelphia, Pennsylvania, USA, ACL. M. Vernier, L. Monceaux, B. Daille, and E. Dubreil. 2009. Catégorisation des évaluations dans un corpus de blogs multi-domaine. Revue des Nouvelles Technologies de l’Information, pages 45–69, nov. J. Wiebe and R. Mihalcea. 2006. Word sense and subjectivity. In ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pages 1065–1072. A. Zaenen and L. Polanyi. 2004. Contextual valence shifters. In Proc. AAAI Spring Symposium on Exploring Attitude and Affect in Text, pages 106–111. L. Zhang and S. Ferrari. 2012. Opinion analysis: the effect of negation on polarity and intensity. In PATHOS 2012. 318 Proceedings of KONVENS 2012 (PATHOS 2012 workshop), Vienna, September 21, 2012
What is the Meaning of 5 *’s? An Investigation of the Expression and Rating of Sentiment Daniel Hardt Copenhagen Business School dh.itm@cbs.dk Julie Wulff Copenhagen Business School cbs@juliewulff.dk Abstract Do user populations differ systematically in the way they express and rate sentiment? We use large collections of Danish and U.S. film reviews to investigate this question, and we find evidence of important systematic differences: first, positive ratings are far more common in the U.S. data than in the Danish data. Second, highly positive terms occur far more frequently in the U.S. data. Finally, Danish reviewers tend to under-rate their own positive reviews compared to U.S. reviewers. This has potentially far-reaching implications for the interpretation of user ratings, the use of which has exploded in recent years. 1 Introduction There is a persistent stereotype concerning the way sentiment is expressed and evaluated by Scandinavians and Americans, which is illustrated by these two anecdotes. In the first anecdote, a U.S. researcher gives a talk in a Scandinavian country. After the talk, the researcher is approached by an audience member, who says, “the talk was ok”. The U.S. researcher is puzzled by this, until another member of the audience explains to him that this was actually intended to express high praise. The second anecdote: a student at the beginning of his graduate studies at a U.S. university has several meetings with a prominent faculty member, and is repeatedly told that his research ideas are “wonderful”. The student is gratified by this, until he overhears other students talking about how this faculty member seems to always respond to ideas by calling them “wonderful”. There is abundant anecdotal evidence that Scandinavians and Americans differ in the way they express and evaluate sentiment: compared to Americans, it seems that Scandinavians downgrade their positive expressions of sentiment. But is this stereotype actually true? In this paper, we investigate this question by analyzing large collections of Danish and U.S. film reviews. These reviews are short pieces of text, combined with a numerical rating which expresses the user’s overall evaluation. In our view, such data should provide a meaningful test of the stereotype – if Scandinavians and Americans do indeed differ as we have described, this should be reflected in distributional differences in these datasets. In particular, the hypothesis concerns distributions of very positive evaluations: compared to U.S. reviewers, we expect a Danish tendency to “downgrade” from very positive to somewhat less positive. We will examine this hypothesis from three different perspectives, in looking at the Danish data vs. the U.S. data: 1. Ratings: are there relatively fewer high ratings? 2. Text: are there relatively fewer highly positive terms? 3. Ratings vs. Text: are there fewer high ratings for texts of a given positivity? In what follows, we begin with a description of the data sets. Next we examine the distribution of ratings. Then we look at the text positivity: we 319 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
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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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Page 273 and 274: References Anders Björkelund, Bern
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Page 277 and 278: 3.1.1 Rules Let t 1..n be tokens of
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Page 283 and 284: of our work in the whole applicatio
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Page 295 and 296: References Monica Bednarek. 2006. E
Page 297 and 298: 3 Basic Methodology of Annotation T
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Page 325 and 326: In recent years, the use of rating
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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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