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ing to this “J-shaped distribution”. The mean star rating of this distribution at 4.28 stars corresponds to an opinion value of 0.64, so many neutral adjectives get an opinion value in this region. Thus, a correction is necessary. We proceed in the following way. In a first step, we classify all single adjectives into the two classes “Neutrals Following J-shaped Distribution” and “Others”. For words in the first class we require their star distribution to follow the “J-shaped distribution” quantitatively and qualitatively. The quantitative deviation from this distribution we estimate by calculating the measure ∑ S J1 = 1 − √ 5 ( ns ) 2 n − a s (2) s=1 where a s is the relative frequency of s-star review titles in the whole sample. We found out that a value of S J1 greater than 0.85 indicates that the star distribution for a word is quite similar to the “J-shaped distribution” of the whole set of review titles. In addition, we set the measure S J2 to TRUE (T) if n 1 ≥ n 2 and n 4 ≤ n 5 , otherwise S J2 is set to FALSE (F). In this way, we make sure that the correction of the opinion value is only applied to words also following the “J-shaped distribution” qualitatively. In the second step, the opinion values get corrected for words fulfilling both conditions by weighting the star rating frequencies n s with the relative frequencies of the whole sample (a s ): OV c = (∑ 5 ) s=1 ns·s a s ∑5 − 3 : 2 (3) s=1 ns a s Table 2 lists some words and the effect of the “J-shaped distribution” correction to them. The same correction procedure has to be applied for single nouns. The corresponding fractions of s-star ratings are 13% for 1-star, 6% for 2-star, 8% for 3-star, 15% for 4-star and 58% for 5-star review titles containing the single nouns. The correction is calculated in the same way as described above. Table 3 gives some examples for nouns with corrected opinion values. Adjective OV S J1 S J2 OV c schnell - fast 0.69 0.96 F − gut - good 0.67 0.75 F − hübsch - nice 0.56 0.71 T − jung - young 0.66 0.93 T 0.12 schwarz - black 0.67 0.97 T 0.08 andere - other 0.57 0.88 T 0.01 Table 2: Some adjectives and the effect of the “Jshaped distribution” correction. Bold opinion values mark the values entering the final list. Noun OV S J1 S J2 OV c Traum - dream 0.91 0.67 T − Gefühl - feeling 0.69 0.88 F − Gehirn - brain 0.59 0.93 T 0.08 Zeit - time 0.57 0.96 T 0.06 Kunst - art 0.56 0.96 T 0.05 Table 3: Some nouns and the effect of the “J-shaped distribution” correction. Bold opinion values mark the values entering the final list. 4 Results and Discussion 4.1 Statistical Summary After these steps, our list consists of 3,210 words and phrases. Table 4 summarizes the frequencies of adjectives, adjective phrases, nouns and noun phrases as well as the number of weak and strong subjective words and phrases. We regard a word or phrases as weak subjective, if the opinion value lies between 0.33 and 0.67 or −0.33 and −0.67. Having an opinion value greater than 0.67 or smaller than −0.67, a word or phrase is assumed to be strong subjective. Words and phrases with an opinion value between −0.33 and +0.33 we regard as being neutral. total n ws ss Adjectives 1,277 390 400 487 Adjective phrases 938 135 170 633 Nouns 502 124 112 266 Noun phrases 493 51 88 354 Sum 3,210 700 770 1,740 Table 4: Number of neutral (n), weak subjective (ws) and strong subjective (ss) words and phrases. 310 <strong>Proceedings</strong> of KONVENS 2012 (PATHOS 2012 workshop), Vienna, September 21, 2012
More than half of the words and phrases are strong subjective while less than one fourth are neutral. 4.2 Examples of Opinion Values for Words and Phrases Table 5 lists some adjectives and adjective phrases, Table 6 some nouns and noun phrases together with their opinion values. Adjectives OV großartig - great +0.95 exzellent - excellent +0.91 optimal - optimal +0.87 amüsant - amusing +0.71 gut - good +0.67 brauchbar - useful +0.38 durchschnittlich - average −0.02 mies - crummy −0.44 schlecht - bad −0.56 enttäuschend - disappointing −0.65 unbrauchbar - unusable −0.86 grottenschlecht - abysmal −0.96 Adjective Phrases OV extrem gut - extremely good +1.00 sehr sehr gut - very very good +0.97 sehr gut - very good +0.90 sehr praktisch - very useful +0.87 gewohnt gut - good as usual +0.78 recht gut - quite good +0.48 nicht schlecht - not bad +0.38 nicht optimal - not optimal −0.05 eher schwach - rather weak −0.26 ziemlich langweilig - rather boring −0.61 nicht gut - not good −0.64 sehr schlecht - very bad −0.83 ganz mies - just crummy −1.00 Table 5: Examples of adjectives and adjective phrases with their opinion values. The opinion values look plausible. The whole range of possible opinion values is covered. Interesting is a look at the role of valence shifters and negation words. The valence shifter “sehr” - ‘very’ shifts the opinion values in the expected direction. Also a multiple usage (“sehr sehr gut” - ‘very very good’) leads to a further increase of the opinion value. Also visible is the fact that a valence shifter can not be described with a common factor which can be applied to all adjectives. The results for the negation word “nicht” - ‘not’ show that it does not always change the sign of the opinion value (as for “gut” - ‘good’) leaving its absolute value nearly unchanged. In fact, for many words the negation changes a strong polarity to a weak or neutral one, e.g., for “schlecht” - ‘bad’ or “optimal” - ‘optimal’. Nouns OV Weltklasse - world class +0.98 Superprodukt - super product +0.97 Meisterwerk - masterpiece +0.94 Durchschnitt - average −0.05 Unsinn - nonsense −0.52 Enttäuschung - disappointment −0.71 Zumutung - impertinence −0.77 Frechheit - impudence −0.89 Zeitverschwendung - waste of time −0.93 Noun Phrases OV absolutes Muss - absolute must +0.97 sehr gute Qualität - very good quality +0.94 großer Spaß - great fun +0.75 nur Durchschnitt - only average −0.02 mangelnde Qualität - lack of quality −0.55 absoluter Fehlkauf - absolute bad buy −1.00 Table 6: Examples of nouns and noun phrases with their opinion values. Also for the nouns the opinion values obtained look good. Again, the valence shifters change the opinion values in the expected way. 4.3 Comparison to Existing Lists To compare our list with the two existing polarity lexicons (PL and GPC, see Chapter 2), we compare some single opinion words, which are frequently used in text sources. The result of this comparison is given in Table 7. For both, PL and GPC, the polarity (positive - P and negative - N) is given. For GPC, the attached numbers give the probability of the word having this polarity. For PL, the number indicates the strength of the opinion. We can see that the values in the three lists 311 <strong>Proceedings</strong> of KONVENS 2012 (PATHOS 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
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
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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
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Page 283 and 284: of our work in the whole applicatio
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Page 287 and 288: local context. For instance, in une
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Page 291 and 292: Domain-specific variation of sentim
Page 293 and 294: query building blocks comments real
Page 295 and 296: References Monica Bednarek. 2006. E
Page 297 and 298: 3 Basic Methodology of Annotation T
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Page 301 and 302: 4.3 Auxiliary data - Mall.cz We hav
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Page 305 and 306: A Phrase-Based Opinion List for the
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Page 313 and 314: treebank. In Proceedings of the Wor
Page 315 and 316: Apart from these, we distinguish op
Page 317 and 318: close variants)”. In fact importa
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
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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
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Page 343 and 344: 2 Related Work There has been consi
Page 345 and 346: vided to ensure that they are norma
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Page 349 and 350: ization (rather than modernization)
Page 351 and 352: The Sketch Engine as infrastructure
Page 353 and 354: 2.1 The Corpus of English Dialogues
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Page 357 and 358: Evaluating a Post-editing Approach
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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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