Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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available from OED extracted from Corpora information flow & control structure *"!+ -% .% .% .% *"!+ /% (9:/,$(#% (9:/ - 345!$5&# - 6%45!$5&# 7 ;8% .% .% .% (9:/ / 345!$5&# - 6%45!$5&# 7 ;8% 0!12/,$(#% 0!12/ - 345!$5&# -6%45!$5&# 7..8% .% .% .% 0!12/ / 345!$5&#-6%45!$5&#7..8% *"!+,$(#% stemmer !""#,$(#% !""# $% -% 0!12/ 1==5 -% incr yes .% .% .% .% 1==5 /% .% .% (#1=,$(#% (#1= -% .% .% yes .% !""# $% $&% &"'""#(% )% no !""# $% $&% >1==5,$(#% )% no $&% ,1/$?"&% )% no @5&95>% AB1?C% no !""# -% .% .% .% !""# /% &"'""#,$(#% +$?D"&5!E% 1&#!E -% .% .% .% 1&#!E /% GUI control Figure 5: Architecture of Morextractor and Morphilizer dated. Deleting the entry from the original file enables the user to interrupt her or his work and go on at a later point in time. As a summary, the main sequences of the algorithm (MoreXtractor and Morphilizer) is visualized in figure 5. 2.3.3 MorQuery MorQuery is the third component in the tool set. It is an independent program to query the Morphilo database more easily. A web-based interface is also available. In essence, the user makes a selection of the features of interests (corpus, types/tokens, word class, morpheme/allomorph, affix position, prefixes/suffixes/compound/words, derivation/inflection). The software combines these choices to valid SQL commands, queries the database and returns the results as textual output. The results can be saved for further statistical analyses in a tab-delimited format. While for very specific information requests, SQL queries can also be entered directly, a selection of the most common queries can be chosen from a drop down menu. 2.4 Morphilo Platform: Morphorm Morphorm is a platform attempting to contribute to a sustainable framework of reusability of diachronic linguistic data. The framework incorporates the Morphilo tool set and the Morphilo database. In addition, it extents the prevalent structure to meet the requirements of a multiuser design. The main idea behind Morphorm is similar to web wikis: share work - receive full profit. Users contribute to the data stock and profit themselves from a more representative set of data and less annotation work. With each additional unit of annotated text, future annotation work will be substantially less for all users since each item (word or compound) has to be analyzed only once. Figure 6 depicts the architecture. Note that MoreXtractor receives direct input on the time range and words from the database here. This 420 <strong>Proceedings</strong> of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
Figure 6: Architecture of Morphorm feature is part of Morphilizer in the standalone application. Also, a list of analyzed corpora ensures that no data is processed twice. Each new corpus is written to this file. The second difference in Morphorm is that new data is not written into the original database, but to separate datasets that are structurally identical. The third adjustment made in Morphorm is quality control. Decentralizing quality control is a sensitive issue and cannot per se be fully automated. There is no full-fledged solution available, but we will use indicators and reported feedback by users. A first indicator is the frequency of usage of a certain dataset by the user community and in publications. A high frequency indicates a certain trust in the analyzed data. A second indicator is, if available, data of the registered user, e.g. his or her project, background, or department. Third, unexpected differences in the result sets of the Morphilo and the user dataset hint at possible erratic annotations. However, from the suspicious datasets a sample will be drawn and will be checked manually. Last, reported errors from other users will contribute to revising or excluding datasets from accommodating it to the master file. If a “user dataset” meets all quality standards, it is incorporated into the Morphilo database. The integration of MorQuery made an additional selection field necessary. The user makes the choice on a selection of datasets most suitable to her or him. The quality of the Morphilo database is assured; for all others that have not been checked for quality no guarantees can be made. So, it is up to the user to make a decision on the trade-off between representativeness and risk of wrong annotations. A possible way of dealing with this situation is to make several queries (similar to the procedure described above for quality control): one with the Morphilo dataset, one with all datasets and one with the personal selection of datasets. If the results deviate substantially from the Morphilo results, the selection should be treated with caution. The data should be checked individually and reported to the quality control. 421 <strong>Proceedings</strong> 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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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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Page 377 and 378: of the EACL 2009 Workshop on GEMS:
Page 379 and 380: Most of the research on genre class
Page 381 and 382: Precision Recall F 1 Unigrams 0.551
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Page 397 and 398: Accuracy N % All Words 781/1000 78.
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Page 401 and 402: Digitised Historical Text: Does it
Page 403 and 404: 3 Related Work Previous research on
Page 405 and 406: 5.1 End-of-line Soft Hyphen Deletio
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Page 409 and 410: References Ahmad Abdulkader and Mat
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Page 415 and 416: From Semi-Automatic to Automatic Af
Page 417 and 418: Figure 2: ER-Diagramm of the Morphi
Page 419: Figure 4: Morphilizer’s implement
Page 423 and 424: References Harald R. Baayen, R. Pie
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Page 439 and 440: verbs were collected mostly from an
Page 441 and 442: 3 Theoretical Background Let us now
Page 443 and 444: are sensible to semantic properties
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Page 451 and 452: Verb class same frame different fra
Page 453 and 454: Construction Grammar are introduced
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Page 461 and 462: Källström and Inger Lindberg, edi
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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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