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performance of named-entity extraction (though correction may of course be valuable for other IR purposes), and that there is scope for improving accuracy via some heuristic preprocessing and the use of domain knowledge. In the near term our work will likely focus on employing simple heuristics and patternmatching tools to attempt to improve the quality of the NER for our specific domain. Longer term, we intend to exploit the domain-specific knowledge generated by the EHRI project (in the form of authority files describing people, places, and organizations) to validate and enhance the output of automatic entity recognition tools. References T. Blanke, M. Bryant, and M. Hedges. Ocropodium: open source OCR for small-scale historical archives. Journal of Information Science, 38(1):76–86, November 2011. ISSN 0165-5515, 1741-6485. doi: 10.1177/0165551511429418. URL http://jis.sagepub.com/cgi/doi/ 10.1177/0165551511429418. Thomas Breuel. The OCRopus open source OCR system. In Proceedings IS&T/SPIE 20th Annual Symposium, 2008. URL http:// citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.99.8505. Jean Carletta. Assessing agreement on classification tasks: the kappa statistic. Comput. Linguist., 22(2):249–254, June 1996. ISSN 0891-2017. URL http://dl.acm.org/ citation.cfm?id=230386.230390. Jenny Rose Finkel, Trond Grenager, and Christopher Manning. Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL ’05, pages 363– 370, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics. doi: 10.3115/ 1219840.1219885. URL http://dx.doi. org/10.3115/1219840.1219885. Claire Grover, Sharon Givon, Richard Tobin, and Julian Ball. Named entity recognition for digitised historical texts. 2008. URL http://citeseerx.ist.psu.edu/ viewdoc/summary?doi=10.1.1.166. 68. Christoph Müller and Michael Strube. Multi-level annotation of linguistic data with MMAX2. In Sabine Braun, Kurt Kohn, and Joybrato Mukherjee, editors, Corpus Technology and Language Pedagogy: New Resources, New Tools, New Methods, pages 197–214. Peter Lang, Frankfurt a.M., Germany, 2006. Thomas L. Packer, Joshua F. Lutes, Aaron P. Stewart, David W. Embley, Eric K. Ringger, Kevin D. Seppi, and Lee S. Jensen. Extracting person names from diverse and noisy OCR text. page 19. ACM Press, 2010. ISBN 9781450303767. doi: 10.1145/1871840.1871845. URL http://portal.acm.org/citation. cfm?doid=1871840.1871845. Helmut Schmid. Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proceedings of International Conference on New Methods in Language Processing, Manchester, UK, 1994. Ray Smith. An overview of the tesseract OCR engine. In Proceedings of the Ninth International Conference on Document Analysis and Recognition, 2007. URL http://dl.acm.org/ citation.cfm?id=1304846. Simon Tanner, Trevor Muoz, and Pich Hemy Ros. Measuring mass text digitization quality and usefulness. D-Lib Magazine, 15(7/8), July 2009. ISSN 1082- 9873. doi: 10.1045/july2009-munoz. URL http://www.dlib.org/dlib/ july09/munoz/07munoz.html. 414 Proceedings of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
From Semi-Automatic to Automatic Affix Extraction in Middle English Corpora: Building a Sustainable Database for Analyzing Derivational Morphology over Time Hagen Peukert University of Hamburg hagen.peukert@uni-hamburg.de Abstract The annotation of large corpora is usually restricted to syntactic structure and word class. Pure lexical information and information on the structure of words are stored in specialized dictionaries (Baayen et al., 1995). Both data structures – dictionary and text corpus – can be matched to get e.g. a distribution of certain (restricted) lexical information from a text. This procedure works fine for synchronic corpora. What is missing, however, is either a special mark-up in texts linking each of the items to a certain time or a diachronic lexical database that allows for the matching of the items over time. In what follows, we take the latter approach and present a tool set (MoreXtractor, Morphilizer, Mor- Query), a database (Morphilo-DB) and the architecture of a platform (Morphorm) for a sustainable use of diachronic linguistic data for Middle English, Early Modern English and Modern English. 1 Introduction The sustainability of linguistic resources has gained considerable attention in the last years or so (Dipper et al., 2006; Rehm et al., 2010; Schmidt et al., 2006; Stührenberg et al., 2008). This development was probably initiated and was certainly fostered by federally funded research projects on information structure (SFB 632), linguistic data structures (SFB 441), or multilingualism (SFB 538). Work on sustainable language resources culminated in a row of frameworks, data models and structures, formats and tools. Also, it continues to prosper in related work (e.g. CLARIN). SPLICR, for example, addresses the issue of normalization of XMLannotated language records, or meta data (Rehm et al., 2010). In fact, the authors discuss the issues of a steadily growing proprietary tag set, the availability, the accessibility and the findability of linguistic resources. More precisely, search engines locate commercially produced data collections, but miss deep structured ressources of small research projects. Privacy and property rights restrict accessibility. Proprietary tag sets not obeying to established standards pose a problem for automatic analysis. In this vein, PAULA concentrates on stand-off annotations and the TUS- NELDA repository states an example of integrated annotations. Both frameworks specify methods for handling and storing linguistic data. Finally, EXMERALDA is a tool for annotating spoken data in the first place. In sum, the focus in this field comprise work on annotation, format, tools, data integration (Dipper et al., 2006; Witt et al., 2009) and documentation (Simons and Bird, 2008). In a more general sense, these dimensions reflect Simons’ and Bird’s (2008) first three key players in a sustainable framework of language resources: creators, archives, aggregators, and users. Although some of the repositories include historical language resources, the data structures and tools do not take into account the diachronic dimension, that is, language change over large spans of time is not represented in any of the models. Indeed, one finds tools for tagging morphological information, annotation schemas or tran- 415 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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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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Page 441 and 442: 3 Theoretical Background Let us now
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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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