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The PAUSE tag is used for silent (unfilled) pauses which can occur at any position in the utterance. (3) das ist irgend so ein this is some so a This is some eh rapper. (-) Rapper rapper The PTKFILL tag is used for filled pauses which can occur at any position in the utterance. (4) das ist irgend so ein äh Rapper this is some so a eh rapper This is some rapper. 3.2 Other particles The PTKONO tag is used for labelling onomatopoeia and forms of echoism. (5) das Lied ging so lalalala the song went like lalalala The PTKREZ tag is used for backchannel signals. We define backchannel signals as plain, nonemotional reactions of the recipient to signal the speaker that the utterance has been received and understood. (6) A: stell dir das mal vor ! A: imagine you this PART. VERB PART. ! Imagine that ! (7) B: m-hm B: uh-huh Preliminary annotation experiments showed a very low inter-annotator agreement for the distinction between answer particles and backchannel signals for ja (yes). To support consistency of annotation, we always label ja as an answer particle and not as a backchannel signal. The PTKQU tag is used for question tags like nicht/ne (no), richtig/gell (right), oder (or), added to the end of a positive or negative statement. (8) wir we treffen uns am Kino , ne ? meet REFL at the cinema , no ? We’ll meet at the cinema. Right ? The PTK tag is used for unspecific particles such as ja (yes), na (there, well) when occuring in utterance initial position. (9) ja wer bist du denn ? yes who are you then ? And who are you now? Please note that most occurences of ja (yes) in the middle field are modal particles (Example 10) which are assigned the ADV label (adverb) in the German treebanks. Occurences of ja in the prefield, on the other hand, should be considered as discourse markers and thus should be treated differently (also see Meer (2007) for a discussion on the different word classes of ja). (10) die hat ja auch nicht funktioniert . this has PTK.MOD also not worked . This didn’t work, either. The PTKPH tag is used as a placeholder when the correct word class can not be inferred from the context. Example (11), for instance, has many possible readings. In (a), the correct POS tag would be noun (NN), while in (b) we would assign a past participle (VVPP) tag. The placeholder might also stand for a whole VP, as in (c). (11) er hat dings hier . he has thingy here . a. er hat MP3-Player NN hier . he has MP3 player here . b. er hat gewonnen VVPP hier . he has won here . c. er hat (Schuhe gekauft) VP hier . he has shoes bought here . 3.3 Non-words Our tag set distinguishes 3 types of non-words. 1. uninterpretable 2. non-word in abandoned utterances 3. other The XYU tag is used for lexical material which is uninterpretable, mostly because of poor audio quality of the speech recordings or because of code-switching. This tag should also be used for word tokens where it is not clear whether they are unfinished or simply non-words. (12) wir we waren gestern bei (fremdsprachlich). were yesterday at (FOREIGN). Yesterday we’ve been at (FOREIGN). The XYB tag is used for abandoned words. (13) ich ha # sie I ha- she I ha- kommt Sams äh Sonntag . comes Satur- eh Sunday . she’ll come on Satur- eh Sunday. 240 <strong>Proceedings</strong> of KONVENS 2012 (Main track: poster presentations), Vienna, September 20, 2012
The XY tag is used for all non-words which do not fit one of the categories above. This category is consistent with the XY category used in the STTS where it is used for non-words including special symbols. 3.4 Inflective The NINFL tag is used for non-inflected verb forms (Teuber, 1998) which are a common stilistic device in comics and computer-mediated communication, but are also used in spoken language. (14) ich I muss noch putzen . must still clean . I still have to clean. Sigh! 3.5 Punctuation seufz ! sigh ! The $# tag is used to mark interrupted/abandoned utterances. These can (but not necessarily do) include unfinished words, as in Example (15). (15) sie war ge # she was (UNINTERPRETABLE) # 4 Inter-Annotator Agreement We measured inter-annotator agreement for three human annotators using the extended tagset on a test set (3415 tokens) of spontaneous multi-party dialogues from the KiDKo corpus (Wiese et al., 2012) and achieved a Fleiss’ κ of 0.975 (% agr. 96.5). Many of the errors made by the annotators concern the different functions of ja in spoken data (discourse marker vs. answer particles). 5 Discussion A major pitfall for the annotation of spoken language is the danger of carrying over annotation guidelines from standard written text which, at first glance, seem to be adequate for the description of spoken language, too. Only on second glance does it become obvious that what looked similar at first does not necessarily need to be the same. A case in point is ja (yes), which in written text mostly occurs as a modal particle in the middle field, labelled as ADV, while in spoken dialogues occurences of ja in utterance-initial position, labelled as answer particles (PTKANT), are by far the more frequent (Table 2). Motivated by the difference in distribution, we took a closer look at these instances and observed that many of them POS TIGER TüBa-D/Z TüBa-D/S PTKANT 43 147 27986 ADV 154 372 4679 ITJ 2 0 0 NN 0 16 0 total 199 536 32664 Table 2: Distribution of ja (yes) in different corpora, normalised by corpus size are in fact discourse markers (Example 9). We thus added the label PTK to our tag set, which is defined by its position in the utterance and its function. As a second example, consider weil (because, since) which, according to standard grammars, is a subordinating conjunction. In TIGER as well as in the TüBa-D/S, all occurences of weil are annotated as KOUS (subordinating conjunction). However, in TüBa-D/S we also find examples where weil is used to coordinate two main clauses (indicated by V2 word order) and thus should be labelled as a coordination (KON) (Example 16). (16) [...] fahren wir nicht zu früh los , weil sonst [...] drive we not too early PTK , because else bin ich unausgeschlafen am I sleepdeprived let’s not start too early, else I’ll be tired out Finally, it is important to keep in mind that all types of linguistic annotation not only provide a description, but also an interpretation of the data. This is especially true for the annotation of learner data, where the formulation of target hypotheses has been discussed as a way to deal with the ambiguity inherent to a learner’s utterances (Hirschmann et al., 2007; Reznicek et al., 2010). When annotating informal spoken language, we encounter similar problems (see Example 11). Adding an orthographic normalisation to the transcription might be seen as a poor man’s target hypothesis where decisions made during the annotation become more transparent. 6 Conclusion In the paper we extended the Stuttgart-Tübingen TagSet, the standard POS tag set for German, for the annotation of spoken language. Our extension allows for a more meaningful treatment of spoken language phenomena while also maintaining the comparability with corpora of written text annotated with the original version of the STTS. 241 <strong>Proceedings</strong> 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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EI EII Tag AKR MARK Typography ITER
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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
Page 197 and 198: Figure 3: In the sense comparison v
Page 199 and 200: Using Information Retrieval Technol
Page 201 and 202: against every tree. Others avoid th
Page 203 and 204: A 0.780715 NN Figure 4: A sp
Page 205 and 206: # #Documents Index Size 1 29,704 1.
Page 207 and 208: References P. Bański, C. Belica, H
Page 209 and 210: (2012), in their corpus study of va
Page 211 and 212: DET NSUBJ ADV OBJA AMOD Seine Tante
Page 213 and 214: A Computational Semantic Analysis o
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
Page 221 and 222: 221 Proceedings of KONVENS 2012 (Ma
Page 223 and 224: Using subcategorization frames to i
Page 225 and 226: 5.2 Word clustering based on the Le
Page 227 and 228: erage of english grammars. In Proce
Page 229 and 230: cases not disambiguated using langu
Page 231 and 232: Not surprisingly, the results show
Page 233 and 234: Automatic Identification of Languag
Page 235 and 236: Differences also appear at the synt
Page 237 and 238: Acknowledgments The authors express
Page 239: 2 Related Work Previous work on POS
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).
Page 257 and 258: word raw freq. non conn. conn. (of
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
Page 271 and 272: pling (Geman and Geman, 1984) to es
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
Page 279 and 280: Human 6 System 6 Human 4 System 4 A
Page 281 and 282: Codebuch Akteursanalyse. rev. 2011.
Page 283 and 284: of our work in the whole applicatio
Page 285 and 286: 3.4 Negation and graduation Valence
Page 287 and 288: local context. For instance, in une
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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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