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Max. Len. Accuracy 300 words 0.996 Table 1: Word Uni-Gram Results Proper nouns play an important role when using word uni-grams. It is very likely that texts from Portugal will contain named entities that are almost exclusively used in Portugal and vice-versa (e.g. names of important or famous people from Brazil/Portugal or names of companies). 5.2 Word Bi-Grams For the word bi-gram model evaluation, we explored how the maximum length of texts affects the performance of the algorithm. The results were classified according to the maximum text size, ranging from 100 words to 700 words. The best results were reached with a maximum length of 500 words, after that, the model seems to indicate saturation, as can be seen in table 2: Max. Len. Accuracy 100 words 0.851 200 words 0.886 300 words 0.889 400 words 0.904 500 words 0.912 600 words 0.912 700 words 0.905 Table 2: Text Size and Word N-Grams Results from table 2 are presented in figure number 1: Figure 1: Text Size and Word Bi-Grams One explanation for the results is that only a few journalistic texts in both corpora are larger than 500 words. Adding these few texts to the classification brings no improvement in the algorithm’s performance. 5.3 Character N-Grams The best results obtained in our experiments relied on a character n-gram model and were designed using texts of maximum 300 tokens. Results reached 0.998 accuracy for 4-grams, and they are presented in table number 3: N-Grams Accuracy 2-Grams 0.994 3-Grams 0.996 4-Grams 0.998 5-Grams 0.988 6-Grams 0.990 Table 3: Character N-Gram Results The good results obtained by character n-grams in comparison to the word n-gram models presented in the previous sections, indicate that the orthographical differences between Brazilian and European Portuguese are still a strong factor for distinguishing these varieties. 6 Conclusions and Future Work This paper explored computational techniques for the automatic identification of two varieties of Portuguese. From the three groups of features tested, the character-based model using 4-grams performed best. The results for word bi-grams were not very good reaching an accuracy result of 0.912. These outcomes suggest that for this task lexico-syntactic differences are not as important as differences in orthography and lexicon. The small number of classes contributes to the encouraging results presented here. When performing binary classification, the baseline result is already 0.50 and when provided with meaningful features, algorithms are quite likely to achieve good results. Experiments are being carried out to integrate the character-based model described in this paper in a real-world language identification tool. Preliminary results for this model in a 6-fold classification reached above 90% accuracy and f- measure. 236 Proceedings of KONVENS 2012 (Main track: poster presentations), Vienna, September 20, 2012
Acknowledgments The authors express gratitude to Folha de São Paulo for the Brazilian corpus and Sascha Diwersy for the European corpus. Many thanks to the anonymous reviewers who provided important feedback to increase the quality of this paper. References Bird, S.; Klein, E.; Loper, E. 2009. Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit O’Reilly Media Cavnar, W.; Trenkle, J. 1994. N-gram-based Text Categorization. 3rd Symposium on Document Analysis and Information Retrieval (SDAIR-94) Dunning, T. 1993. Accurate Methods for the Statistics of Surprise and Coincidence. Computational Linguistics - Special Issue on Using Large Corpora Volume 19, Issue 1. MIT Press Dunning, T. 1994. Statistical Identificaion of Language Technical Report MCCS-94-273 Computing Research Lab, New Mexico State University Grafenstette, G. 1995. Comparing two Language Identification Schemes. JADT 1995, 3rd International Conference on Statistical Analysis of Textual Data Rome Ingle, N. 1980. Language Identification Table Technical Translation International Koller, O.; Abad, A.; Trancoso, I.; Viana, C. 2010. Exploiting Variety-dependent Phones in Portuguese Variety Identification Applied to Broadcast News Transcription. Proceedings of Interspeech 2010 Ljubesic, N.; Mikelic, N.; Boras, D. 2007. Language Identificaiton: How to Distinguish Similar Languages? Proceedings of the 29th International Conference on Information Technology Interfaces Martins, B.; Silva, M. 2005. Language Identification in Web Pages Proceedings of the 20th ACM Symposium on Applied Computing (SAC), Document Engineering Track Santa Fe, EUA. p. 763-768 Palmer, D. 2010. Text Processing. In: Indurkhya, N.; Damerau, F. (Ed.) Handbook of Natural Language Processing - Second Edition.. CRC Press. p. 9-30 Piersman, Y.; Geeraerts, D.; Spelman, D. 2010. The Automatic Identification of Lexical Variation Between Language Varieties. Natural Language Engieeniring 16 (4).. Cambridge University Press. p. 469-491 Rehurek, R.; Kolkus, M. 2009. Language Identification on the Web: Extending the Dictionary Method. Proceedings of CICLing. Lecture Notes in Computer Sciences. Springer. p. 357-368 Rouas, J.; Trancoso, I.; Ribeiro, M.; Abreu, M. 2008. Language and Variety Verification on Broadcast News for Portuguese. Speech Communication vol. 50 n.11. Elsevier Soares da Silva, A. 2010. Measuring and parameterizing lexical convergence and divergence between European and Brazilian Portuguese: endo/exogeneousness and foreign and normative influence. Advances in Cognitive Sociolinguistics. Berlin. De Gruyter Vojtek, P;. Belikova, M. 2007. Comparing Language Identification Methods Based on Markov Processess. Slovko, International Seminar on Computer Treatment of Slavic and East European Languages 237 Proceedings of KONVENS 2012 (Main track: poster 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
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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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Page 187 and 188: References Saima Aman and Stan Szpa
Page 189 and 190: Robust processing of noisy web-coll
Page 191 and 192: 1. first strip of the papery skin o
Page 193 and 194: References Jon Chamberlain, Massimo
Page 195 and 196: ments between its LSRs. Additionall
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Page 201 and 202: against every tree. Others avoid th
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Page 207 and 208: References P. Bański, C. Belica, H
Page 209 and 210: (2012), in their corpus study of va
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Page 215 and 216: (6) a. Is Spain very large? b. Is B
Page 217 and 218: References Alan C. Bale. 2008. A Un
Page 219 and 220: 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
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Page 231 and 232: Not surprisingly, the results show
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Page 235: Differences also appear at the synt
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).
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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
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Page 273 and 274: References Anders Björkelund, Bern
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
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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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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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