Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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Adjective JJ simple adjectives like Z 1 /kwys/ (good), O6‎‏ل /$gAl/ (fine) Adverb RB Case Case marking is not a feature of EA. Even for MSA tweets in our corpus that can result from users quoting press news and the like, case marking is ignored. Command Verb VB Cardinal Number CD Comparative Adjective JJR Connective Particle Coordinating Conjunction CC Both connective particles and coordinating conjunctions are collapsed into the CC tag. Both coordinate base and complex phrases. Examples of this tag include: و /w/ (and), /wkmAn/ (and و‎1‎‏(ن /wbEdyn/ (and then) and وب*‏ also). Definite Demonstrative Pronoun DM Definite particle is tokenized and tagged as DT. The information about whether a noun or an adjective is definite is thus structurally defined: in the tokenized corpus if a noun/adjective is preceded by the definite article, it is definite; otherwise it is not. Demonstratives are phrase boundary markers and thus they are distinguished from determiners which are not. Determiner DT Dialect Although the corpus contains MSA tweets coming mostly from users copying from press agencies, the dialect-standard distinction is not marked in the corpus. Direct Object Object nouns are not tagged, but object pronouns are. They are split-off during tokenization and tagged as OBJP. Distinction between direct and indirect pronoun objects is not marked in our tagset given that it is structurally – rather than – morphologically defined: indirect object pronouns are preceded by a preposition but direct ones are not. Dual DU The number features – singular, dual and plural – are given separate tags that can be combined with any relevant content word tag such as nouns, verbs, adjectives and personal pronouns. Emphatic Particle Existential EX Neither ATB nor RTS label existentials probably because they are expressed in MSA via the imperfect verb /ywjd/ or the demonstrative ھك /hnAk/ both meaning there is/are. In EA, there are two possible forms of existentials: /fy/ and /fyh/ (lit: in; gloss: there is/are). Given that both existentials are ambiguous with the preposition in which has the same 48 <strong>Proceedings</strong> of KONVENS 2012 (Main track: oral presentations), Vienna, September 19, 2012
forms, and that existentials can be phrase boundary anchors, we use the EX tag to label them. Feminine F Focus Particle Foreign Word FW Tweets in Arabic script that contain one or more foreign words have these words labeled as FW. Foreign Script Tweets entirely in foreign script whether they contain words from a foreign language – like English words – or contain Arabic words are filtered out in corpus preprocessing. Future (will). /H/ ‏ /h/ or ھ FT Future tense is marked in EA by the verb prefixes These two are split out in tokenization and are tagged as FT for future. The same tag is used then for the MSA separate future particle س ف /swf/. Future Particle Although it does not mark phrase boundaries, it is important for verb tense identification. Genitive Imperfect Verb VBP The tense of an imperfect verb can be present, future or progressive. This fine-grained tense classification is not represented by the tagset; yet this information is structurally predictable given that the split-off affixes indicating each tense are POS tagged. Indefinite Indicative Interjection UH Interrogative Adverb Interrogative Particle Interrogative Pronoun INT Interrogative adverbs like إزاي // used in MSA to form yes/no questions are not used in EA, yet if they exist in MSA tweets, they are also labeled as INT. Particle Jussive Masculine M The gender features – masculine and feminine – are given separate tags that can be combined with any relevant content word tag such as nouns, verbs, adjectives and personal pronouns. Mood Negative Particle NG ‏را‏ ش Negative particles come in two forms: a circumfix mA...$ as in /mArAHw$/ (they didn't go), and a number of free morphemes including F /m$/ and M /lA/ both meaning no among others. Bound and free negative 49 <strong>Proceedings</strong> of KONVENS 2012 (Main track: oral presentations), Vienna, September 19, 2012
Page 1 and 2: 11 th Conference on Natural Languag
Page 3 and 4: Table of Contents Main conference P
Page 5 and 6: Comparing variety corpora with vis-
Page 7 and 8: LexSem 2012: Workshop on Recent Dev
Page 9 and 10: KONVENS 2012 The 11 th Conference o
Page 11 and 12: Organizers: Jeremy Jancsary (ÖGAI/
Page 13 and 14: Ines Rehbein (Potsdam University) M
Page 15 and 16: Integrating viewpoints into newspap
Page 17 and 18: Phonetically aided syntactic parsin
Page 19 and 20: The Statistical Approach to Natural
Page 21 and 22: Data-driven Knowledge Extraction fo
Page 23 and 24: 4 The Different Relations Types In
Page 25 and 26: Relation Type #Patterns Examples Su
Page 27 and 28: Suits-to Served-with Substituted-by
Page 29 and 30: References Timothy Chklovski and Pa
Page 31 and 32: 1.2 Problem Definitions In a MRA, t
Page 33 and 34: oot person organisation product ...
Page 35 and 36: Group Organis. Persons Products all
Page 37 and 38: 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
Page 43 and 44: ecause the enclitic pronoun is an i
Page 45 and 46: stochastic rules as opposed to larg
Page 47: Kevin Duh, and Katrin Kirchhoff. 20
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
Page 85 and 86: ulary distance. Let d(a, b) denote
Page 87 and 88: the reduced lexicon (243) was neare
Page 89 and 90: Adding nominal spice to SALSA - fra
Page 91 and 92: SALSA verbs nouns 0 FE 0 2 1 FE 68
Page 93 and 94: gen, which in one of its meanings i
Page 95 and 96: In the next section we describe how
Page 97 and 98: 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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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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