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By way of example, consider the Change position on a scale frame (Figure 1), evoked in English by lexical units across different word classes such as accelerated.a, advance.v, climb.v, contraction.n, and others. In the German SALSA corpus, the frame is licensed by frame-evoking elements like, e.g., abstürzen.v, erhöhen.v, gewinnen.v, and klettern.v (crash, increase, win, climb). The core set of frame-specific roles that apply includes ATTRIBUTE, DIFFERENCE, FINAL STATE, FI- NAL VALUE, INITIAL STATE, INITIAL VALUE, ITEM and VALUE RANGE, out of which only ITEM and ATTRIBUTE are realized in our example. 3 Overview of the SALSA corpus The SALSA project used the frames of Frame- Net Releases 1.2 and 1.3 to perform German annotation on top of the TIGER corpus. Since the English frames were not always available or appropriate, SALSA additionally developed a number of ”proto-frames”, i.e., predicate-specific frames, to provide coverage for predicate instances that were not covered by FrameNet at the time SALSA analyzed relevant German vocabulary. Figure 1 shows a sentence from the TIGER corpus that is annotated with one original Frame- Net frame, Change position on a scale with frame elements ITEM and ATTRIBUTE, and with one SALSA proto-frame, Zahl1-salsa with its frame element INDIVIDUALS, which is assigned to the same NP as the frame element ITEM of Change position on a scale. SALSA Release 1.0 (Burchardt et al., 2006) was published in October 2007. The total size of the annotations in SALSA Release 1.0 includes 20.380 annotated instances for 493 verb lemmas and 348 annotated instances for 14 noun lemmas (Table 1). SALSA Release 1.0 Release 2.0 token type token type verb 20,380 493 20,380 493 noun 348 14 15,871 155 total 20,728 507 36,251 648 Table 1: Annotated instances in SALSA 1.0 and 2.0 SALSA Release 2.0 complements the verb annotations in Release 1.0 with the annotation of more than 15 000 noun instances (Table 1). The selection of nouns that were annotated mostly includes nominalizations (e.g. Eroberung (conquest), Freude (delight)) and relational nouns like Bruder (brother) or Seite (side). The annotation scheme follows the one for verbs as closely as possible. There are, however, differences due to • multi-word expressions, e.g. guter Hoffnung sein (expect a baby, be expecting) • constructions, e.g. wählen (choose) – X n-ter Wahl (as in e.g. second quality socks) • named entities, e.g. Der Heilige Krieg (holy war) • lack in parallelism of verb and noun senses, e.g. gut ankommen (’be well received’ – *Arrival) The goal in selecting the nouns chosen for analysis was to achieve a balanced distribution with lexical units across all frequency bands (Table 2). An interesting point of comparison is the number of realized frame elements for nouns and verbs in the SALSA corpus. As shown by Table 3, the average number of FEs is higher for verbs at 1.91 than for nouns at 1.45. Note that this is the case despite the fact that during the annotation of SALSA Release 1.0 Release 2.0 >500 2 4 301-500 6 17 101-300 41 81 51-100 68 93 31-50 75 89 11-30 99 122
SALSA verbs nouns 0 FE 0 2 1 FE 68 91 2 FEs 376 57 3 FEs 49 4 4 FEs 0 1 avg. # FEs 1.91 1.45 Table 3: Avg. number of realized frame elements (FEs) per lemma for verbs and nouns in SALSA nouns the annotators were allowed to also assign non-core frame elements when suitable, while the vast number of verbal annotations do not include any non-core elements. 1 In the next section we describe our efforts at quality control during the annotation process. 4 Inter-Annotator Agreement The annotation process in SALSA involved a thorough quality control, including double annotation by two independent annotators for all instances in the corpus. Disagreements in the annotations were resolved by two expert annotators in an adjudication phase. Table 4 shows interannotator agreement 2 for frames and frame elements. Despite showing approximately the same degree of polysemy (Table 4), agreement on noun frames is higher than for verbal targets. For frame elements, however, percentage agreement for nominal instances is far below the one for verbs. This is mostly due to the annotation of SUPPORT and CONTROLLER, which rank highest among the frame elements on which the annotators disagreed. Percentage agreement for SUPPORT is 43.2%, and the agreement for CON- 1 In the first phase of the project when only verbs were annotated, the policy was to usually forgo annotating noncore FEs in favor of annotating a greater number of targets with core FEs. 2 We do not report chance-corrected agreement but percentage agreement for reasons discussed in (Burchardt et al., 2006). In addition, chance-corrected agreement measures like the kappa statistics are often misleading when applied to unbalanced data sets. Consider, e.g., the lemma Bruder (brother), where we have 29 annotated instances out of which 28 are assigned the frame Kinship by both annotators. However, due to the skewed distribution of word senses the chance-corrected agreement (Fleiss κ) is only 0.491, while the percentage agreement of 96.6% better reflects that the annotators agreed on all but one instance for the lemma Bruder. SALSA frames frame elements # word senses verbs 84.6 81.0 2.6 nouns 92.9 73.3 2.7 all 87.1 78.2 2.6 Table 4: Percentage agreement for human annotators in SALSA 2.0 on frames and frame elements and avg. number of word senses (frames) per verb/noun lemma TROL is even lower with 23.6%. 3 Another reason for the low coder agreement on frame elements is caused by relaxations in the annotation guidelines which, unlike in Release 1.0, allowed the annotators to assign non-core frame elements for nouns, when appropriate. Apparently, some annotators made more use of this than others. Amongst the 15 highest-ranking frame elements on which annotators disagreed most frequently, we found DESCRIPTOR, DEGREE, PER- SISTENT CHARACTERISTIC, PLACE and TIME, all of which are non-core frame elements. In most cases, these FEs had only been annotated by one annotator, while the other one had assigned the core frame elements only. Amongst the frames which proved to be most difficult for the annotators are the ones expressing fine-grained distinctions between related meanings, such as the distinction between Statement and Telling, between Being born and Birth, or between Judgment and Judgment communication. Other difficult cases involved the annotation of more abstract concepts like Causation, which was often mixed up with the Reason frame. 5 Comparison of SALSA and FrameNet Now we want to focus on some central methodological and analytical issues which came to our attention during the annotation process. 5.1 Frame development and workflow A key difference between SALSA and FrameNet lies in how the vocabulary to be analyzed and annotated is chosen. FrameNet has two modes of working, a lexicographic and a full text one. In full-text analysis mode the goal is to cover running text as densely as possible with frame semantic annotations. When frames are found to be 3 Efforts to overcome this difficulty by replacing support and control predicates with fully flashed out frames and proto-frames did not succed. 91 <strong>Proceedings</strong> of KONVENS 2012 (Main track: oral presentations), Vienna, September 19, 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
Page 39 and 40: A Supervised POS Tagger for Written
Page 41 and 42: Our tagset mixes and matches tags a
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Page 45 and 46: stochastic rules as opposed to larg
Page 47 and 48: Kevin Duh, and Katrin Kirchhoff. 20
Page 49 and 50: forms, and that existentials can be
Page 51 and 52: Pseudo Verb Punctuation PNC
Page 53 and 54: NLP Workflow for On-line Definition
Page 55 and 56: nological phrases on the basis of p
Page 57 and 58: Figure 2: A screenshot of the entir
Page 59 and 60: When analyzing the evaluation sets
Page 61 and 62: Projecting Semantic Roles via Tai M
Page 63 and 64: quent prediction, return it (iii) i
Page 65 and 66: for the B/T/FT settings. This is pr
Page 67 and 68: ut it is implied by Table 3 and Fig
Page 69 and 70: References Wanxiang Che, Zhenghua L
Page 71 and 72: Our ultimate goal is to develop com
Page 73 and 74: ADORNING CAUSE_FLUIDIC_MOTION COTHE
Page 75 and 76: English nouns, verbs, adjectives, a
Page 77 and 78: considerably worse than FrameNet. T
Page 79 and 80: Extraction. In Proceedings of the 1
Page 81 and 82: lexicon from a document-derived voc
Page 83 and 84: Initialize hypernym set H to contai
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Page 89: Adding nominal spice to SALSA - fra
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Page 95 and 96: In the next section we describe how
Page 97 and 98: Acknowledgments This work was funde
Page 99 and 100: most commonly used (Turney and Pant
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Page 103 and 104: 5 000 feat ICA50 SVD50 SENNA Semcat
Page 105 and 106: words bicycle and tricycle go with
Page 107 and 108: of the Twelfth European Conference
Page 109 and 110: incorporate the context information
Page 111 and 112: those candidate phrases that are an
Page 113 and 114: 5.2 Evaluation Metrics for WPD The
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Page 117 and 118: Evaluations (SemEval-2007), June (2
Page 119 and 120: 2 Related Work The majority of pape
Page 121 and 122: and Roth, 2009; Lin and Wu, 2009).
Page 123 and 124: Features Dev Test CoNLL-2003 w 0 +
Page 125 and 126: Feature Dev Test Test+ — 93.78 91
Page 127 and 128: the Joint Conference of the 47th An
Page 129 and 130: tion. It was first included as a sp
Page 131 and 132: indicates the presence of the ellip
Page 133 and 134: [Armada britainiarrak] [Ipar Irland
Page 135 and 136: Información, (JOTRI 2003), pages 1
Page 137 and 138: Phonetically aided Syntactic Parsin
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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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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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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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