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5 Conclusion Folk narratives are a valuable resource for historical comparative folk narrative studies. In this paper we presented experiments on classifying folk narrative genres. The goal was to automatically classify narratives, dating from the 16th century until now, as legend, saint’s legend, fairy tale, urban legend, personal narrative, riddle, situation puzzle, joke or song. Character n-grams were found to be the most effective of all features. We therefore plan to explore historical texts in non standard Dutch, since character n-grams can be easily extracted from them. We also intend to explore features that help detect difficult genres and generalize across specific stories. For example, features that can detect humorous components. 6 Acknowledgements This research was supported by the Folktales as Classifiable Texts (FACT) project, part of the CATCH programme funded by the Netherlands Organisation for Scientific Research (NWO). References J. Abello, P. Broadwell, and T. R. Tangherlini. 2012. Computational folkloristics. Communications of the ACM, 55(7):60–70, July. C. O. Alm, D. Roth, and R. Sproat. 2005. Emotions from text: machine learning for text-based emotion prediction. In Proceedings of HLT/EMNLP, pages 579–586. W. Bascom. 1965. The Forms of Folklore: Prose Narratives. The Journal of American Folklore, 78(307). J. H. Brunvand. 1993. The Baby Train and Other Lusty Urban Legends. W. W. Norton & Company. P. Burger and T. Meder. 2006. -A rope breaks. A bell chimes. A man dies.- The kwispel: a neglected international narrative riddle genre. In Toplore. Stories and Songs, pages 28–38. K. Crammer and Y. Singer. 2002. On the learnability and design of output codes for multiclass problems. Mach. Learn., 47(2-3):201–233, May. R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. 2008. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, 9(6/1/2008):1871–1874. A. Finn and N. Kushmerick. 2006. Learning to classify documents according to genre. Journal of the American Society for Information Science and Technology, 57(11):1506–1518. L. Friedland and J. Allan. 2008. Joke retrieval: recognizing the same joke told differently. In Proceedings of CIKM, pages 883–892. R. Grundkiewicz and F. Gralinski. 2011. How to Distinguish a Kidney Theft from a Death Car? Experiments in Clustering Urban-Legend Texts. In Proceedings of the Workshop on Information Extraction and Knowledge Acquisition. I. Kanaris and E. Stamatatos. 2007. Webpage Genre Identification Using Variable-Length Character n- Grams. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02, ICTAI ’07, pages 3–10, Washington, DC, USA. IEEE Computer Society. B. Kessler, G. Nunberg, and H. Schuetze. 1997. Automatic Detection of Text Genre. In Proceedings of the 35th ACL/8th EACL. K. A. La Barre and C. L. Tilley. 2012. The elusive tale: leveraging the study of information seeking and knowledge organization to improve access to and discovery of folktales. Journal of the American Society for Information Science and Technology, 63(4):687–701. P. V. Lobo and D. M. de Matos. 2010. Fairy Tale Corpus Organization Using Latent Semantic Mapping and an Item-to-item Top-n Recommendation Algorithm. In Proceedings of LREC. T. Meder. 2010. From a Dutch Folktale Database towards an International Folktale Database. Fabula, 51(1-2):6–22. S. Mohammad. 2011. From once upon a time to happily ever after: tracking emotions in novels and fairy tales. In Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, LaTeCH ’11, pages 105–114. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python . Journal of Machine Learning Research, 12:2825–2830. S. Sharoff, Z. Wu, and K. Markert. 2010. The Web Library of Babel: evaluating genre collections. In Proceedings of LREC. H.-J. Uther. 2004. The Types of International Folktales: A Classification and Bibliography Based on the System of Antti Aarne and Stith Thompson. Vols 1-3. Suomalainen Tiedeakatemia, Helsinki. A. Van Den Bosch, B. Busser, S. Canisius, and W. Daelemans. 2007. An efficient memory-based morphosyntactic tagger and parser for Dutch. In Computational Linguistics in the Netherlands Selected Papers from the Seventeenth CLIN Meeting, pages 99–114. OTS. 382 Proceedings of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
The DTA ‘base format’: A TEI-Subset for the Compilation of Interoperable Corpora Alexander Geyken BBAW Jägerstr. 22/23 D-10117 Berlin geyken@bbaw.de Susanne Haaf BBAW Jägerstr. 22/23 D-10117 Berlin haaf@bbaw.de Frank Wiegand BBAW Jägerstr. 22/23 D-10117 Berlin wiegand@bbaw.de Abstract This article describes a strict subset of TEI P5, the DTA ‘base format’, which combines the richness of encoding noncontroversial structural aspects of texts while allowing only minimal semantic interpretation. The proposed format is discussed with regard to other commonly used XML/TEI schemas. Furthermore, the article presents examples of good practices showing how external corpora can either be converted into the DTA ‘base format’ directly or after cautiously extending it. Thus, the proposed encoding schema contributes to the paradigm shift recently observed in corpus compilation, namely from private encoding to interoperable encoding. 1 Introduction Up to the end of the 1990s corpus compilation on the basis of the Guidelines of the Text Encoding Initiative (TEI; most recent release: P5, cf. Burnard and Baumann, 2012) was mainly a project specific activity. Corpus documents were validated against a project specific document grammar, (possibly) private character encodings were used, and the documents were transformed into proprietary formats in order to get indexed for full text retrieval. In that era of project specific encoding, exchange of documents across projects was not a primary goal, and, in general, character encoding problems as well as differences in the document type grammar (DTD) were obstacles to a broader exchange of data. With the advent of XML and Unicode, documents encoded according to the recommendations of the TEI became interchangeable, or, more precisely, documents encoded in TEI P5 could be safely exchanged on different platforms without worrying about incompatibilities of character encoding. However, differences in structural encodings still remained. The large flexibility of using the TEI Guidelines to encode similar semantic phenomena with different XML elements is one major reason for this problem: for example, there are several ways to encode the hierarchy of sections in documents, either with numbered division elements ( . . . ) elements or by enumerating the hierarchy with numeric @n-values: , , etc. Likewise, there are different ways to encode information about person names (, , ), several ways to link text passages (, , @corresp, @next/@prev, . . . ) etc. However, the main reason for differences in structural encoding resides in the fact that different projects use different subsets of the TEI according to their needs. Problems like these become apparent when the attempt is made to carry out specific tasks with the exchanged data on another platform together with another document collection. Problems occur on several levels, the first one being the difficulty to create a common style sheet across different document collections encoded in different TEI P5 schemas in order to present all document collections uniformly on the web. Another problem concerns the exchange of TEI metadata: Due to the flexibility of the TEI tag set, the structure of TEI Headers may dif- 383 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
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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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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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