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Multi-faceted alignment. Toward automatic detection of textual similarity in Gospel-derived texts
(2015)
Ancient Germanic Bible-derived texts stand in as test material for producing computational means for automatically determining where textual contamination and linguistic interference have influenced the translation process. This paper reports on the results of research efforts that produced a text corpus; a method for decomposing the texts involved into smaller, more directly comparable thematically-related chunks; a database of relationships between these chunks; and a user-interface allowing for searches based on various referential criteria. Finally, the state of the product at the end of the project is discussed, namely as it was handed over to another researcher who has extended it to automatically find semantic and syntactic similarities within comparable chunks.
Current work on sentiment analysis is characterized by approaches with a pragmatic focus, which use shallow techniques in the interest of robustness but often rely on ad-hoc creation of data sets and methods. We argue that progress towards deep analysis depends on a) enriching shallow representations with linguistically motivated, rich information, and b) focussing different branches of research and combining ressources to create synergies with related work in NLP. In the paper, we propose SentiFrameNet, an extension to FrameNet, as a novel representation for sentiment analysis that is tailored to these aims.
Contents:
1. Andreas Dittrich: Intra-connecting a small exemplary literary corpus with semantic web technologies for exploratory literary studies, S. 1
2. John Kirk, Anna Čermáková: From ICE to ICC: The new International Comparable Corpus, S. 7
3. Dawn Knight, Tess Fitzpatrick, Steve Morris, Jeremy Evas, Paul Rayson, Irena Spasic, Mark Stonelake, Enlli Môn Thomas, Steven Neale, Jennifer Needs, Scott Piao, Mair Rees, Gareth Watkins, Laurence Anthony, Thomas Michael Cobb, Margaret Deuchar, Kevin Donnelly, Michael McCarthy, Kevin Scannell: Creating CorCenCC (Corpws Cenedlaethol Cymraeg Cyfoes – The National Corpus of Contemporary Welsh), S. 13
4. Marc Kupietz, Andreas Witt, Piotr Bański, Dan Tufiş, Dan Cristea, Tamás Váradi: EuReCo - Joining Forces for a European Reference Corpus as a sustainable base for cross-linguistic research, S. 15
5. Harald Lüngen, Marc Kupietz: CMC Corpora in DeReKo, S. 20
6. David McClure, Mark Algee-Hewitt, Douris Steele, Erik Fredner, Hannah Walser: Organizing corpora at the Stanford Literary Lab, S. 25
7. Radoslav Rábara, Pavel Rychlý ,Ondřej Herman: Accelerating corpus search using multiple cores, S. 30
8. John Vidler, Stephen Wattam: Keeping Properties with the Data: CL-MetaHeaders – An Open Specification, S. 35
9. Vladimir Benko: Are Web Corpora Inferior? The Case of Czech and Slovak, S. 43
10. Edyta Jurkiewicz-Rohrbacher, Zrinka Kolaković, Björn Hansen: Web Corpora – the best possible solution for tracking phenomena in underresourced languages: clitics in Bosnian, Croatian and Serbian, S. 49
11. Vít Suchomel: Removing Spam from Web Corpora Through Supervised Learning Using FastText, S. 56
This work proposes opinion frames as a representation of discourse-level associations that arise from related opinion targets and which are common in task-oriented meeting dialogs. We define the opinion frames and explain their interpretation. Additionally we present an annotation scheme that realizes the opinion frames and via human annotation studies, we show that these can be reliably identified.
In this contribution, we report on an effort to annotate German data with information relevant to opinion inference. Such information has previously been referred to as effect or couched in terms of eventevaluation functors. We extend the theory and present an extensive scheme that combines both approaches and thus extends the set of inference-relevant predicates. Using these guidelines to annotate 726 German synsets, we achieve good inter-annotator agreement.
We examine different features and classifiers for the categorization of opinion words into actor and speaker view. To our knowledge, this is the first comprehensive work to address sentiment views on the word level taking into consideration opinion verbs, nouns and adjectives. We consider many high-level features requiring only few labeled training data. A detailed feature analysis produces linguistic insights into the nature of sentiment views. We also examine how far global constraints between different opinion words help to increase classification performance. Finally, we show that our (prior) word-level annotation correlates with contextual sentiment views.
We present an approach to the new task of opinion holder and target extraction on opinion compounds. Opinion compounds (e.g. user rating or victim support) are noun compounds whose head is an opinion noun. We do not only examine features known to be effective for noun compound analysis, such as paraphrases and semantic classes of heads and modifiers, but also propose novel features tailored to this new task. Among them, we examine paraphrases that jointly consider holders and targets, a verb detour in which noun heads are replaced by related verbs, a global head constraint allowing inferencing between different compounds, and the categorization of the sentiment view that the head conveys.