A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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[9] Lavie, Alon, Parlikar, Alok, and Ambati, Vamshi (2008) Syntax-driven Learning of Sub-sentential Translation Equivalents and Translation Rules from Parsed Parallel Corpora. In Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation (SSST-2), Columbus, OH. [10] Melamed, Daniel (1998) Manual annotation of translational equivalence: The BLINKER project. Technical report #98-07. Institute for Research in Cognitive Science, University of Pennsylvania, Philadelphia, PA. [11] Menezes, Arul and Richardson, Stephen D. (2001) A best-first alignment algorithm for automatic extraction of transfer mappings from bilingual corpora. In Proceedings of the workshop on Data-driven methods in machine translation, Toulouse, France. [12] Moore, Robert C. (2002) Fast and Accurate Sentence Alignment of Bilingual Corpora. In Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: From Research to Real Users, Tiburon, California. [13] Samuelsson, Yvonne, Volk, Martin, Gustafson–Capková, Sofia and Steiner, Elisabet Jönsson (2010) Alignment Guidelines for SMULTRON. Stockholm University, Department of Linguistics, University of Zürich, Institute of Computational Linguistics. Version 2.1, August 9, 2010. [14] Simov, K., Osenova, P., Laskova, L., Savkov, A. and Kancheva, S. (2011) Bulgarian-English Parallel Treebank: Word and Semantic Level Alignment. In Proceedings of Recent Advances in Natural Language Processing (RANLP), Hissar, Bulgaria. [15] Steinberger, R. and Pouliquen, B. and Widiger, A. and Ignat, C. and Erjavec, T. and Tufiş, D. and Varga, D. (2006) The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages. In Proceedings of Language Resources and Evaluation Conference (LREC’06), Genova, Italy. [16] Zhechev, Ventislav and Way, Andy (2008) Automatic generation of parallel treebanks. In 22nd International Conference on Computational Linguistics (COLING 2008), Manchester, UK. 180
Building a treebank of noisy user-generated content: The French Social Media Bank Djamé Seddah Benoît Sagot Marie Candito Virginie Mouilleron Vanessa Combet Alpage, Inria Paris-Rocquencourt & Université Paris Diderot, France Abstract We introduce the French Social Media Bank, the first user-generated content treebank for French. Its first release contains 1,700 sentences from various Web 2.0 and social media sources (FACEBOOK, TWITTER, web forums), including data specifically chosen for their high noisiness. 1 Introduction New forms of electronic communication have emerged in the last few years, namely social media and Web 2.0 communication media, both synchronous (e.g., microblogging) or asynchronous (e.g., forums). These new user-generated contents often depart, sometimes heavily, from canonical language. This prevents an accurate processing of such data by current state-of-art NLP tools (Foster et al. [9], Gimpel et al. [11]). The main difficulties, highlighted by Foster [8], range from surface differences (intended or accidental non-standard typography) to lexical idiosyncrasies (genuine unknown words, sloppy spelling) and specific syntactic structures absent from well-edited data (imperatives, direct speech, slang, etc.). The still difficult handling of those phenomena pleads for a better linguistic modeling and analysis of user-generated content. We introduce the French Social Media Bank, a freely available treebank containing 1700 manually annotated sentences. It constitutes the first resource covering the variety of French social medias, and the first data set we are aware of for FACEBOOK data. 2 Corpus The French web 2.0 covers a wide range of practices. We decided to focus on microblogging (FACEBOOK and TWITTER) and on two types of web forums: one large-audience health forum, DOCTISSIMO (forum.doctissimo.fr) and one specialized on video games JEUXVIDEOS.COM (www.jeuxvideo.com). For each source but the latter, we gathered both lightly edited data and noisier data, using handcrafted search queries. Lightly edited data were retrieved based on sourcespecific news topics. The noisiest texts, intended to serve as a stress test for 181
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A Treebank-based Investigation of I
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participle and a (te-)infinitival c
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Some verbs occur twice, since they
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Profiling Feature Selection for Nam
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prepositional objects (FOPP, OPP).
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the limited size of annotated data
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with high precision typically have
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‘This was “not significantly”
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The preposition durch typically has
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Non-Projective Structures in Indian
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the sequential order of nodes in a
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extra-posed relative clause that ge
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Experiments on Dependency Parsing o
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for mitigating the effects of spars
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obtained with MALTParser is 76.61%
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as a standardised serialisation for
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constituency and dependency, possib
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SynAF and/or in . However, they sha
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shows how some elements that are no
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Example
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‏ Example 8: represent
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Chinese) as the direct object NP.
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Example 15: Tokens and Word Forms
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In a second experiment, a dataset w
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[3] Leech G. N., Barnett, R. & Kahr
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Effectively long-distance dependenc
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In French, another case of eLDD is
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elativization, it-clefts or questio
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4.2.3 Annotation methodology Becaus
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Number of occurrences in FTB +SEQTB
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producing treebank based LFG approx
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Logical Form Representation for Lin
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gerundives for a total amount of so
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object+indirect object/object one.
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(VP (VB patch) ) ) ) (VP (VBZ is) (
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Types Adverb. Adject. Verbs Nouns T
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Eventually we may comment that ther
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DeepBank: A Dynamically Annotated T
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from another already existing one,
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to parse now does. The extra manual
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In the derivation show in Figure 1,
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4 Patching Coverage Gaps with An Ap
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will enable further improvements in
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ParDeepBank: Multiple Parallel Deep
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2 The ParDeepBank The PTB has emerg
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undergone extensive scientific scru
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the second combines the structures
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Sofia University). Each sentence wa
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data, and improvements in the infra
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The Effect of Treebank Annotation G
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ut without feature structures. This
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only the lexicon is fine-grained to
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Automatic Coreference Annotation in
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manually annotated Czech sentences.
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citizens of Bilbao] are very involv
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4.1.3 Coreference Selector Module T
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Nominal P R F1 MUC 75.33% 81.33% 78
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[9] G. Doddington, A. Mitchell, M.
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Analyzing the Most Common Errors in
Page 123 and 124: Graph 1 shows results of subsequent
Page 125 and 126: in the semantic type). This situati
Page 127 and 128: Will a Parser Overtake Achilles? Fi
Page 129 and 130: annotation is also based on a depen
Page 131 and 132: Set Name Sentences Tokens % Train/T
Page 133 and 134: dency relations (PRED, OBJ, SBJ, AD
Page 135 and 136: ton and Stacklazy), we trained a mo
Page 137 and 138: Feature Function Column Name Addres
Page 139 and 140: Using Parallel Treebanks for Machin
Page 141 and 142: phrases are generated by a shallow
Page 143 and 144: Figure 2: A Kybot p
Page 145 and 146: SUJ NP SENT MOD PP NP PP PP NP NP P
Page 147 and 148: Checkpoint Instances Google PT Our
Page 149 and 150: 6 Conclusions In this paper we have
Page 151 and 152: An integrated web-based treebank an
Page 153 and 154: 16] and we have earlier reported fr
Page 155 and 156: Figure 2: Screenshot of the interfa
Page 157 and 158: ple subcategorization frames may be
Page 159 and 160: 4 Integrated interface for annotati
Page 161 and 162: of the 5th International Conference
Page 163 and 164: Translational divergences and their
Page 165 and 166: posed so far, Cyrus’ work did not
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Page 171 and 172: for all lex_pair(s,t) do if head an
Page 173: the treatment of typical translatio
Page 177 and 178: Phenomenon Attested example Std. co
Page 179 and 180: Figure 1: French Social Media Bank
Page 181 and 182: Impact of treebank characteristics
Page 183 and 184: Det Adj N (a) Danish Det Adj N (b)
Page 185 and 186: sv: Bestämmelserna i detta avtal f
Page 187 and 188: 100 90 80 Unlabelled attachment 70
Page 189 and 190: vidner , tilhørere og tiltalte wit
Page 191 and 192: mance is not inconsiderable, as was
Page 193 and 194: Genitives in Hindi Treebank: An Att
Page 195 and 196: A genitive can occur with complex p
Page 197 and 198: quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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