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The outcome of the present work is a starting point for automatically detecting semantic change. The approach gives us indications, but there is still a need for manual inspection, which we hope to decrease as resources and methods are refined. This approach is not restricted to Swedish, and would benefit from an attempt in a language with more elaborate language resources. References S. Banerjee and T. Pedersen. 2003. The Design, Implementation, and Use of the Ngram Statistic Package. In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, pages 370–381, Mexico City, February. L. Bloomfield. 1933. Language. Henry Holt, New York. L. Borin and M. Forsberg. 2008. Saldo 1.0 (svenskt associationslexikon version 2). Språkbanken, Göteborg universitet. http: //spraakbanken.gu.se/eng/saldo/. L. Borin, M. Forsberg, and D. Kokkinakis. 2010. Diabase: Towards a diachronic blark in support of historical studies. LREC2. L. Borin, M. Forsberg, and C. Ahlberger. 2011. Semantic search in literature as an e-humanities research tool: Conplisit – consumption patterns and life-style in 19th century swedish literature. In NEALT Proceedings Series (NODALIDA 2011 Conference Proceedings), volume 11, pages 58–65. T. Brants. 1998. TnT - Statistical Part-of-Speech Tagging. http://www.coli.uni-sb.de/ ˜thorsten/tnt/. K. Cavallin. 2012. Exploring semantic change with lexical sets. In Proceedings of the 15th EURALEX International Congress, pages 1018–1022. P. Cook and G. Hirst. 2011. Automatic identification of words with novel but infrequent senses. In Proceedings of the 25th Pacific Asia Conference on Language Information and Computation (PACLIC 25), pages 265–274, Singapore, December. A.F. Dalin. 1850-1855. Ordbok Öfver svenska språket, volume I, II. Svenska Akademien, Stockholm. T. Dunning. 1993. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61–74. J.R. Firth. 1957. A synopsis of linguistic theory 1930- 1955. Studies in linguistic analysis, pages 1–32. K. Gulordava and M. Baroni. 2011. A distributional similarity approach to the detection of semantic change in the google books ngram corpus. In Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, pages 67–71, Edinburgh, UK, July. Association for Computational Linguistics. Z. Harris. 1968. Mathematical Structures of Language. John Wiley and Son, New York. M. Hilpert. 2012. Diachronic collostructional analysis: How to use it and how to deal with confounding factors. In K-L. Allan and J.A. Robinson, editors, Current Methods in Historical Semantics, pages 133–160. De Gruyter Mouton, Berlin. E. Jezek and A. Lenci. 2007. When GL meets the corpus: A data-driven investigation of semantic types and coercion phenomena. In Proceedings of the 4th International Workshop on Generative Approaches to the Lexicon, Paris. J H. Lau, P. Cook, D. McCarthy, D. Newman, and T. Baldwin. 2012. Word sense induction for novel sense detection. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), pages 591–601, Avignon, France, April. Litteraturbanken. 2010. http:// litteraturbanken.se/. C. Manning and H. Schütze. 1999. Foundations of statistical natural language processing. MIT Press, Cambridge, MA. J-B. Michel, Y.K. Shen, A.P. Aiden, A. Veres, M.K. Gray, The Google Books Team, J.P. Pickett, D. Hoiberg, D. Clancy, P. Norvig, J. Orwant, S. Pinker, M.A. Nowak, and E.L. Aiden. 2011. Quantitative analysis of culture using millions of digitized books. Science, 331(6014):176–182, January 14. J. Nivre and J. Hall. 2005. MaltParser: A Language- Independent System for Data-Driven Dependency Parsing. In Proceedings of the Fourth Workshop on Treebanks and Linguistic Theories (TLT2005), pages 137–148, December. E. Pettersson, B. Megyesi, and J. Nivre. 2012. Parsing the Past - Identification of Verb Constructions in Historical Text. In Proceedings of the 6th EACL Workshop on Language Technology for Cultural Heritage, Social Sciences and Humanities., Avignon, France. C. Rohrdantz, A. Hautli, T. Mayer, M. Butt, D.A. Keim, and F. Plank. 2011. Towards tracking semantic change by visual analytics. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2, HLT ’11, pages 305–310, Stroudsburg, PA, USA. Association for Computational Linguistics. E. Sagi, S. Kaufmann, and B. Clark. 2009. Semantic density analysis: Comparing word meaning across time and phonetic space. In Proceedings 376 Proceedings of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
of the EACL 2009 Workshop on GEMS: GEometrical Models of Natural Language Semantics, pages 104–111, Athens, Greece, March. 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, Stockholm University. M. Sahlgren. 2008. The distributional hypothesis. Italian Journal of Linguistics, 20(1):1–18. SAOB. 1954. Svenska Akademiens Ordbok. Svenska Akademien, Lund. Språkbanken. 2011. http://spraakbanken. gu.se/korp/. 377 Proceedings of KONVENS 2012 (LThist 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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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
Page 325 and 326: In recent years, the use of rating
Page 327 and 328: KONVENS 2012 LThist 2012: First Int
Page 329 and 330: Organisation Committee: Thierry Dec
Page 331 and 332: Workshop Program Friday, September
Page 333 and 334: Rule-Based Normalisation of Histori
Page 335 and 336: isation rules were applied to the i
Page 337 and 338: ments in the gold standard have bee
Page 339 and 340: cation in the contemporary SUC samp
Page 341 and 342: proach. References Maria Ågren, Ro
Page 343 and 344: 2 Related Work There has been consi
Page 345 and 346: vided to ensure that they are norma
Page 347 and 348: alects, or even languages. Norma ca
Page 349 and 350: ization (rather than modernization)
Page 351 and 352: The Sketch Engine as infrastructure
Page 353 and 354: 2.1 The Corpus of English Dialogues
Page 355 and 356: Other notable contrasts are that 17
Page 357 and 358: Evaluating a Post-editing Approach
Page 359 and 360: Figure 1: Example of a marriage lic
Page 361 and 362: all possible concatenations of indi
Page 363 and 364: Table 3: Post-editing results. Time
Page 365 and 366: okstaffua, bokstaffwa, bokstafwa, b
Page 367 and 368: Rule Cost v↔u 0.1 v↔w 0.1 u↔o
Page 369 and 370: graphic word boundaries and conside
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Page 373 and 374: ut adding a statistical measure to
Page 375: e.g. philosophical and political pr
Page 379 and 380: Most of the research on genre class
Page 381 and 382: Precision Recall F 1 Unigrams 0.551
Page 383 and 384: The DTA ‘base format’: A TEI-Su
Page 385 and 386: Lib are complementary in the sense
Page 387 and 388: inline metadata solutions for edito
Page 389 and 390: ing. The fourth level contains an e
Page 391 and 392: the work package ‘Resources and T
Page 393 and 394: is mainly used with parallel corpor
Page 395 and 396: Corrections by the editor were omit
Page 397 and 398: Accuracy N % All Words 781/1000 78.
Page 399 and 400: $$ $$ $$ $$ $$ $$ $$ $$ $$ Figure 4
Page 401 and 402: Digitised Historical Text: Does it
Page 403 and 404: 3 Related Work Previous research on
Page 405 and 406: 5.1 End-of-line Soft Hyphen Deletio
Page 407 and 408: quite difficult to get an idea of h
Page 409 and 410: References Ahmad Abdulkader and Mat
Page 411 and 412: cision, recall, and F1. Section 4 d
Page 413 and 414: not corrected corrected p P R F1 P
Page 415 and 416: From Semi-Automatic to Automatic Af
Page 417 and 418: Figure 2: ER-Diagramm of the Morphi
Page 419 and 420: Figure 4: Morphilizer’s implement
Page 421 and 422: Figure 6: Architecture of Morphorm
Page 423 and 424: References Harald R. Baayen, R. Pie
Page 425 and 426: 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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