Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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References W.F. Battig and W.E. Montague. 1969. Category norms for verbal items in 56 categories: A replication and extension of the Connecticut category norms. Journal of Experimental Psychology Monograph, 80(3, part 2.):1–45. A. Budanitsky and G. Hirst. 2006. Evaluating WordNet-based measures of lexical semantic relatedness. Computational Linguistics, 32(1):13–47. J.A. Bullinaria and J.P. Levy. 2007. Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39:510–526. J.A. Bullinaria and J.P. Levy. 2012. Extracting semantic representations from word co-occurrence statistics: Stop-lists, stemming and SVD. Behavior Research Methods, 44. B. Calderone. 2009. Learning phonological categories by independent component analysis. Journal of Quantitative Linguistics, 17(2):132–156. G. Celeux and G. Govaert. 1991. Clustering criteria for discrete data and latent class models. Journal of Classification, 8:157–176. A. Chagnaa, C.-Y. Ock, C.-B. Lee, and P. Jaimai. 2007. Feature extraction of concepts by independent component analysis. International Journal of Information Processing Systems, 3(1):33–37. C. K. Chung and J. W. Pennebaker. 2008. Revealing dimensions of thinking in open-ended selfdescriptions: An automated meaning extraction method for natural language. Research in Personality, 42(1):96–132. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research (JMLR), 12:2493– 2537. P. Comon. 1994. Independent component analysis—a new concept? Signal Processing, 36:287–314. J.E. Deese. 1954. The associative structure of some common English adjectives. Journal of Verbal Learning and Verbal Behavior, 3(5):347–357. G. Grefenstette. 1992. Finding the semantic similarity in raw text: The Deese antonyms. Technical Report FS-92-04, AAAI. Lars Kai Hansen, Peter Ahrendt, and Jan Larsen. 2005. Towards cognitive component analysis. In Proceedings of AKRR’05, International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, pages 148–153, Espoo, Finland, June. T. Honkela, A. Hyvärinen, and J.J. Väyrynen. 2010. WordICA — emergence of linguistic representations for words by independent component analysis. Natural Language Engineering, 16:277–308. A. Hyvärinen and E. Oja. 1997. A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7):1483–1492. A. Hyvärinen, J. Karhunen, and E. Oja. 2001. Independent Component Analysis. John Wiley & Sons. J. Karlgren, A. Holst, and M. Sahlgren. 2008. Filaments of meaning in word space. In Proceedings of the European Conference on Information Retrieval, pages 531–538. I. Kivimäki, K. Lagus, I.T. Nieminen, J.J. Väyrynen, and T. Honkela. 2010. Using correlation dimension for analysing text data. In Proceedings of ICANN 2010, pages 368–373. Springer. T.K. Landauer and S.T. Dumais. 1997. A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2):211– 240. J.P. Levy, J.A. Bullinaria, and M. Patel. 1998. Explorations in the derivation of semantic representations from word co-occurrence statistics. South Pacific Journal of Psychology, 10:99–111. M.P. Marcus, B. Santorini, and M.A. Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313–330. Y. Niwa and Y. Nitta. 1994. Co-occurrence vectors from corpora vs. distance vectors from dictionaries. In Proceedings of the 15th International Conference on Computational Linguistics, pages 304– 309. M. Patel, J.A. Bullinaria, and J.P. Levy. 1997. Extracting semantic representations from large text corpora. In Proceedings of Fourth Neural Computation and Psychology Workshop: Connectionist Representations, pages 199–212. Springer. M. Sahlgren. 2006. The Word-Space Model: using distributional analysis to represent syntagmatic and paradigmatic relations between words in highdimensional vector spaces. Ph.D. thesis, Stocholm University, Department of Linguistics. H. Schütze. 1993. Word space. In Advances in Neural Information Processing Systems 5, pages 895–902. Morgan Kaufmann. A. Sorjamaa. 2010. Methodologies for Time Series Prediction and Missing Value Imputation. Ph.D. thesis, Aalto University School of Science. P.-N. Tan, M. Steinbach, and V. Kumar. 2005. Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. P.D. Turney and P. Pantel. 2010. From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37:141–188. P.D. Turney. 2001. Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In Proceedings 106 Proceedings of KONVENS 2012 (Main track: oral presentations), Vienna, September 20, 2012
of the Twelfth European Conference on Machine Learning (EMCL2001), pages 491–502, Freiburg, Germany. Springer-Verlag. P.D. Turney. 2005. Measuring semantic similarity by latent relational analysis. In Proceedings of the 19th International Joint conference on Artificial intelligence (IJCAI-05), pages 1136–1141. M. A. Vicente, P. O. Hoyer, and A. Hyvärinen. 2007. Equivalence of some common linear feature extraction techniques for appearance-based object recognition tasks. IEEE Transactions Pattern Analusis Machine Intelligence, 29(5):896–900. 107 Proceedings of KONVENS 2012 (Main track: oral presentations), Vienna, September 20, 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
Page 55 and 56: nological phrases on the basis of p
Page 57 and 58: Figure 2: A screenshot of the entir
Page 59 and 60: When analyzing the evaluation sets
Page 61 and 62: Projecting Semantic Roles via Tai M
Page 63 and 64: quent prediction, return it (iii) i
Page 65 and 66: for the B/T/FT settings. This is pr
Page 67 and 68: ut it is implied by Table 3 and Fig
Page 69 and 70: References Wanxiang Che, Zhenghua L
Page 71 and 72: Our ultimate goal is to develop com
Page 73 and 74: ADORNING CAUSE_FLUIDIC_MOTION COTHE
Page 75 and 76: English nouns, verbs, adjectives, a
Page 77 and 78: considerably worse than FrameNet. T
Page 79 and 80: Extraction. In Proceedings of the 1
Page 81 and 82: lexicon from a document-derived voc
Page 83 and 84: Initialize hypernym set H to contai
Page 85 and 86: ulary distance. Let d(a, b) denote
Page 87 and 88: the reduced lexicon (243) was neare
Page 89 and 90: Adding nominal spice to SALSA - fra
Page 91 and 92: SALSA verbs nouns 0 FE 0 2 1 FE 68
Page 93 and 94: gen, which in one of its meanings i
Page 95 and 96: In the next section we describe how
Page 97 and 98: Acknowledgments This work was funde
Page 99 and 100: most commonly used (Turney and Pant
Page 101 and 102: anteed because the forward feature
Page 103 and 104: 5 000 feat ICA50 SVD50 SENNA Semcat
Page 105: words bicycle and tricycle go with
Page 109 and 110: incorporate the context information
Page 111 and 112: those candidate phrases that are an
Page 113 and 114: 5.2 Evaluation Metrics for WPD The
Page 115 and 116: Runs F-measures α=0.5 α=0.2 Pur.
Page 117 and 118: Evaluations (SemEval-2007), June (2
Page 119 and 120: 2 Related Work The majority of pape
Page 121 and 122: and Roth, 2009; Lin and Wu, 2009).
Page 123 and 124: Features Dev Test CoNLL-2003 w 0 +
Page 125 and 126: Feature Dev Test Test+ — 93.78 91
Page 127 and 128: the Joint Conference of the 47th An
Page 129 and 130: tion. It was first included as a sp
Page 131 and 132: indicates the presence of the ellip
Page 133 and 134: [Armada britainiarrak] [Ipar Irland
Page 135 and 136: Información, (JOTRI 2003), pages 1
Page 137 and 138: Phonetically aided Syntactic Parsin
Page 139 and 140: mar network or PCFG. Grammar networ
Page 141 and 142: phonetic confusion that has the con
Page 143 and 144: in confusion in the network. This i
Page 145 and 146: Linguistic analyses of the LAST MIN
Page 147 and 148: Table 1: Comparison between corpora
Page 149 and 150: Table 2: Most frequent POS patterns
Page 151 and 152: Table 3: A problematic dialog situa
Page 153 and 154: 6 Summary We have presented the cur
Page 155 and 156: 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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Workshop Program Friday, September
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Unsupervised Sentiment Analysis wit
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pling (Geman and Geman, 1984) to es
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References Anders Björkelund, Bern
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Class # Events in % neutral 45’01
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3.1.1 Rules Let t 1..n be tokens of
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Human 6 System 6 Human 4 System 4 A
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Codebuch Akteursanalyse. rev. 2011.
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of our work in the whole applicatio
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3.4 Negation and graduation Valence
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local context. For instance, in une
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n’était pas très favorable au G
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Domain-specific variation of sentim
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query building blocks comments real
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References Monica Bednarek. 2006. E
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3 Basic Methodology of Annotation T
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(3) A. V případě jeho kandidatur
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4.3 Auxiliary data - Mall.cz We hav
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Overlap f-score Source 0.750 Polar
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A Phrase-Based Opinion List for the
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the Stuttgart-Tübingen Tagset (STT
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As for noun-based opinion phrases a
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More than half of the words and phr
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treebank. In Proceedings of the Wor
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Apart from these, we distinguish op
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close variants)”. In fact importa
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What is the Meaning of 5 *’s? An
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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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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
Page 409 and 410:
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
Page 423 and 424:
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
Page 437 and 438:
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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
Page 479 and 480:
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
Page 503 and 504:
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
Page 517 and 518:
Towards standardized lexical semant
Page 519:
Getting involved into language reso
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