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not really increase the number of correct matches, as the rules only allow a very restricted amount of variation. Precision of the manual rules remains at 80% (= 118/147). The automatically extracted character mapping fails to improve upon the correct match score of the manual rules, and precision drops to 59% (= 116/197). These rules thus create many misleading matches, compared to the manual rules. Finally, the automatically extracted n-gram rules find a match for almost all words in the text, and moreover, retrieve the greatest number of correct matches at 62% of all tokens. This, however, comes at a cost of low precision: 64% (= 154/240). When using the top k oracles the precision for the automatically extracted n-gram rules is instead encouragingly high at 81% (= 195/240) and 87% (= 208/240), respectively. 3.4 Evaluation and Future Work One of the reasons that the automatically extracted n-gram method has such high overall coverage is that the substitution rules not only capture spelling variation, but inflection, too. For instance, for the inflected öknen ‘desert.DEF.SG’ we find the correct match ökn.N, a match we would have otherwise missed even though the spelling convention happens to be the same between source and target. In future work, we intend to treat morphology more systematically by incorporating the computational morphology mentioned in Section 2. For instance, we could use the morphology to lemmatize the text, after which the lemmata are linked-up to the dictionaries using our edit distance approach. The top k oracles for the automatically extracted n-gram rules combine high coverage with high precision. We hope to be able to capitalize on this potential in the future by using context information to model the oracle. For instance, for stijghar in the sentence görer hans stijghar retta ‘straighten his paths’, we find the verb stigha.V ‘ascend, walk’ as a closer match than the noun stigher.N ‘path’. Looking at the context, however, we may be able to guess that the nominal entry is more likely to be correct (see Wilcox-O’Hearn et al. (2008), Jurish (2010b) for the use of word level context in similar selection tasks). Another avenue for research lies in the anagram hash filter. At the moment, we consider one million operations on the anagram hash, submitting on average just under 900 candidate matches for exact edit distance calculation. However, since we have over 6,000 substitution rules, many of which may apply for a particular word, even one million hash variations may only represent a fraction of the search space. When an even smaller number of operations is considered – attractive from a processing effort perspective – the filter removes many valid alternatives, either resulting in suboptimal top candidates or empty candidate lists. For instance, cristindom–kristindomber.N ‘christianity, baptism’ is found by the top 3 oracle in the 1 million operations, but not in the 10k filter. Such incorrectly filtered source-target pairs are typically long and either allow many different operations or require repeated applications of the same cheap rules. Resolving this mismatch between the prefilter step and the exact distance calculation is part of ongoing work. We found that, in addition to linking the tokens to entries in the lexica, the entries need to be linked between the lexica. There is overlap between the dictionaries, but the different dictionaries give different information. For instance, ær ‘is’, mentioned above, is spelled ær in the law text, but är in the bible text. Only the Schlyter entry ær.X links this inflected form to vara.V. The corresponding entries in the Söderwall dictionaries, är.X, give only noun, conjunction and pronoun uses. Consolidating the dictionaries will increase the amount of information available. Finally, the evaluation revealed the necessity to be able to handle multi-word tokens. For example, the text contains the expression köpæ iorth ‘bought land’. While the word iorth is correctly matched in the lexicon, the lexicon entries found for köpæ are incorrect, as they refer to the verb buying or the people involved in buying. The entry köpe iorþ.N does appear in two of the lexica but cannot presently be found as we only consider graphic words. There are also cases where we need to split a graphic word to match it against a dictionary. For instance, the parts of the compound villhonnugh ‘wild honey’ can separately be matched against vilder.AV ‘wild’ and hunagh.N ‘honey’. Splitting and merging compounds and matching them against the dictionary can be readily integrated by allowing substitution rules to contain 368 Proceedings of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
graphic word boundaries and considering multiple source tokens at one time (Reynaert, 2011). An effective way of doing separate matching as needed in the case of vill-honnugh remains a question for future work. 4 Conclusions We are working towards automatic annotation of about 3 million words of historical Swedish texts. As a first step, we are developing a module to handle spelling variation. Three sets of approximate matching rules were used, one with hand-crafted substitutions, and two with substitutions automatically extracted from alternative entries in a lexicon. We presented a pilot evaluation by matching words in two short texts to our historical lexica. While the automatically created rules find more matches, the manual rules have higher precision. Future work includes improving the anagram hash filter, incorporating Old Swedish morphology, handling multi-word units, and exploiting context information to improve precision. Acknowledgements The research presented here is carried out in the context of the Centre for Language Technology of the University of Gothenburg and Chalmers University of Technology. References Marcel Bollmann, Florian Petran, and Stefanie Dipper. 2011. Rule-based normalization of historical texts. In Proceedings of the RANLP Workshop on Language Technologies for Digital Humanities and Cultural Heritage, pages 34–42. Hissar, Bulgaria. Lars Borin and Markus Forsberg. 2008. Something old, something new: A computational morphological description of Old Swedish. In Proceedings of the LREC 2008 Workshop on Language Technology for Cultural Heritage Data (LaTeCH 2008), pages 9–16, Marrakech, Marocco. ELRA. Lars Borin, Markus Forsberg, and Lennart Lönngren. 2008. The hunting of the BLARK - SALDO, a freely available lexical database for Swedish language technology. In Nivre, Dahllöf, and Megyesi, editors, Resourceful language technology. Festschrift in honor of Anna Sågvall Hein, volume 7 of Studia Linguistica Upsaliensia, pages 21–32. Uppsala University, Department of Linguistics and Philology, Uppsala, Sweden. Lars Borin, Markus Forsberg, and Dimitrios Kokkinakis. 2010. Diabase: Towards a diachronic BLARK in support of historical studies. In Calzolari, Choukri, Maegaard, Mariani, Odijk, Piperidis, Rosner, and Tapias, editors, Proceedings of the LREC 2010 workshop on Semantic relations. Theory and Applications, Valletta, Malta. ELRA. Lars Borin, Markus Forsberg, and Johan Roxendal. 2012. Korp – the corpus infrastructure of språkbanken. In Calzolari, Choukri, Declerck, Doğan, Maegaard, Mariani, Odijk, and Piperidis, editors, Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey. ELRA. Anders Fredrik Dalin. 1853/1855. Ordbok öfver svenska språket, volume I–II. Stockholm, Sweden. Bryan Jurish. 2010a. Comparing canonicalizations of historical German text. In Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology, pages 72–77, Uppsala, Sweden. Association for Computational Linguistics. Bryan Jurish. 2010b. More than Words: Using Token Context to Improve Canonicalization of Historical German. Journal for Language Technology and Computational Linguistics, 25(1):23–40. Eva Pettersson, Beáta Megyesi, and Joakim 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. Martin Reynaert. 2011. Character confusion versus focus word-based correction of spelling and OCR variants in corpora. International Journal on Document Analysis and Recognition, 14:173–187. 10.1007/s10032-010-0133-5. Carl Johan Schlyter. 1887. Ordbok till Samlingen af Sweriges Gamla Lagar, volume 13 of Saml. af Sweriges Gamla Lagar. Lund, Sweden. Knut Fredrik Söderwall. 1884/1953. Ordbok Öfver svenska medeltids-språket. Ordbok Öfver svenska medeltids-språket. Supplement. Lund, Sweden. Martijn Wieling, Jelena Prokić, and John Nerbonne. 2009. Evaluating the pairwise string alignment of pronunciations. In Borin and Lendvai, editors, Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education (LaTeCH - SHELT&R 2009) Workshop at the 12th Meeting of the EACL, pages 26–34. Amber Wilcox-O’Hearn, Graeme Hirst, and Alexander Budanitsky. 2008. Real-word spelling correction with trigrams: A reconsideration of the mays, damerau, and mercer model. In Gelbukh, editor, Computational Linguistics and Intelligent Text Processing, volume 4919 of LNCS, pages 605–616. Springer. 369 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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Page 319 and 320: What is the Meaning of 5 *’s? An
Page 321 and 322: For each category, we follow (Const
Page 323 and 324: 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
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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: Rule Cost v↔u 0.1 v↔w 0.1 u↔o
Page 371 and 372: ation. Gulordava and Baroni (2011),
Page 373 and 374: ut adding a statistical measure to
Page 375 and 376: e.g. philosophical and political pr
Page 377 and 378: of the EACL 2009 Workshop on GEMS:
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
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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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