Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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Accuracy (%) 50 55 60 65 70 BNB JST Accuracy (%) 50 55 60 65 70 BNB JST 2 4 6 8 10 2 4 6 8 10 T T (a) MR (b) MDS (overall) Figure 2: Classification accuracies for different values of T all feat. selection MR MDS B D E K avg. LDA 1064 209 226 190 141 191 BNB 728 149 156 103 110 129 LDA 163 109 115 79 67 93 BNB 109 79 87 47 50 66 Table 2: Average inference time (sec) for each dataset topics in the unsupervised case and their results for lexically supervised classification indicate decreasing performance for higher topic numbers. To this end, we run experiments for values of T between 1 and 10 for both TBNB and JST. Note that the models simplify to BNB and LDA, respectively, if T = 1 since the class probabilities are then all conditioned on the same topic which essentialy leads to no conditions at all. Figure 2 shows the classification accuracy for each dataset individually and an overall average accuracy for the MDS data. We can observe that just like in the lexically supervised case, the additional topic layer leads to a decline in accuracy when more topics are introduced, with TBNB being more sensitive than JST. We achieve the best results for models with T = 1, where the best TBNB setup beats the best JST setup by 6.9% on the MR data and 5.4% on the MDS. Note that the best setup for TBNB uses feature selection while the one for LDA does not. We will briefly examine implications on the computational efforts. Computation time. BNB is the better model for document classification because it models document labels instead of word labels. Conversely, LDA needs to estimate more latent classes than needed (one per word instead of only one per document). This leads to higher computational effort which is unjustified as it is not reflected through better classification results. We measured the average time used for inference and labeling on an Intel Xeon 3.33 GHz CPU. We only report numbers for T = 1 as models with more topics are less accurate. Note however that these models can take significantly more time to compute since more label distributions need to be estimated. Table 2 shows the average inference time in seconds for each dataset. Using BNB saves 1/3 of computation time compared to LDA. Since LDA can only produce competitive results when run with all features, the differences become more drastic when comparing lines 1 and 4 of the table, yielding reductions up to around 90% on MR and 50% on MDS. Using a model with feature selection is thus even more desirable if efficiency is an issue. 6 Conclusion and Future Work We presented Bayesian Naive Bayes, a Bayesian model for unsupervised document classification. We showed that BNB is superior to the LDA model on the standard unsupervised sentiment classification task. In future work, we would like to examine the behavior of our model in a semisupervised setting where some document or feature labels are known. Acknowledgements This work was funded by the DFG as part of the SFB 732 project D7. We thank Thomas Müller for his helpful comments. 272 Proceedings of KONVENS 2012 (PATHOS 2012 workshop), Vienna, September 21, 2012
References Anders Björkelund, Bernd Bohnet, Love Hafdell, and Pierre Nugues. 2010. A high-performance syntactic and semantic dependency parser. In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, COLING ’10, pages 33–36, Stroudsburg, PA, USA. Association for Computational Linguistics. David M. Blei and Pedro J. Moreno. 2001. Topic segmentation with an aspect hidden markov model. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’01, pages 343–348, New York, NY, USA. ACM. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993–1022, 3. J. Blitzer, M. Dredze, and F. Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Annual Meeting-Association For Computational Linguistics, volume 45, page 440. Jordan Boyd-Graber and Philip Resnik. 2010. Holistic sentiment analysis across languages: multilingual supervised latent dirichlet allocation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP ’10, pages 45–55, Stroudsburg, PA, USA. Association for Computational Linguistics. Sajib Dasgupta and Vincent Ng. 2009. Topic-wise, sentiment-wise, or otherwise? Identifying the hidden dimension for unsupervised text classification. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 580–589, Singapore, August. Association for Computational Linguistics. H. Daumé III. 2008. hbc: Hierarchical bayes compiler. Pre-release version 0.7, URL http://www. cs. utah. edu/˜ hal/HBC. A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin. 2004. Bayesian data analysis. CRC press. S. Geman and D. Geman. 1984. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (6):721–741. Chenghua Lin and Yulan He. 2009. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management, CIKM ’09, pages 375–384, New York, NY, USA. ACM. Chenghua Lin, Yulan He, and Richard Everson. 2010. A comparative study of bayesian models for unsupervised sentiment detection. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, CoNLL ’10, pages 144–152, Stroudsburg, PA, USA. Association for Computational Linguistics. B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In ACL-EMNLP 2002, pages 79– 86. Ted Pedersen. 1997. Knowledge lean word sense disambiguation. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, AAAI’97/IAAI’97, pages 814– 814. AAAI Press. Philip Resnik and Eric Hardisty. 2010. Gibbs sampling for the uninitiated. Technical report, University of Maryland. Ivan Titov and Ryan McDonald. 2008. Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 111–120. Taras Zagibalov and John Carroll. 2008. Automatic seed word selection for unsupervised sentiment classification of chinese text. In Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1, COLING ’08, pages 1073–1080, Stroudsburg, PA, USA. Association for Computational Linguistics. 273 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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Page 223 and 224: Using subcategorization frames to i
Page 225 and 226: 5.2 Word clustering based on the Le
Page 227 and 228: erage of english grammars. In Proce
Page 229 and 230: cases not disambiguated using langu
Page 231 and 232: Not surprisingly, the results show
Page 233 and 234: Automatic Identification of Languag
Page 235 and 236: Differences also appear at the synt
Page 237 and 238: Acknowledgments The authors express
Page 239 and 240: 2 Related Work Previous work on POS
Page 241 and 242: The XY tag is used for all non-word
Page 243 and 244: Comparing Variety Corpora with Vis-
Page 245 and 246: INPUT variety corpus comparability
Page 247 and 248: References Andrea Abel and Stefanie
Page 249 and 250: Features Context features (token wi
Page 251 and 252: 0.7 0.7 0.6 0.6 0.5 0.5 F-measure 0
Page 253 and 254: ties into fine-grained classes. In
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: pling (Geman and Geman, 1984) to es
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
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
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Positivity Term 2.87692577130 elske
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In recent years, the use of rating
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KONVENS 2012 LThist 2012: First Int
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Organisation Committee: Thierry Dec
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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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