AnCora: Multilevel Annotated Corpora for Catalan and Spanish

By M. Taulé, M. A. Martí, Marta Recasens. International Conference on Language Resources and Evaluation, 2008

389 citations recorded by the source corpus.

This paper presents AnCora, a multilingual corpus annotated at different linguistic levels consisting of 500,000 words in Catalan (AnCora-Ca) and in Spanish (AnCora-Es). At present AnCora is the largest multilayer annotated corpus of these languages freely available from http://clic.ub.edu/ancora. The two corpora consist mainly of newspaper texts annotated at different levels of linguistic description: morphological (PoS and lemmas), syntactic (constituents and functions), and semantic (argument structures, thematic roles, semantic verb classes, named entities, and WordNet nominal senses). All resulting layers are independent of each other, thus making easier the data management. The annotation was performed manually, semiautomatically, or fully automatically, depending on the encoded linguistic information. The development of these basic resources constituted a primary objective, since there was a lack of such resources for these languages. A second goal was the definition of a consistent methodology that can be followed in further annotations. The current versions of AnCora have been used in several international evaluation competitions

Subfields: Artificial Intelligence, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/90b4b1b6e5ef410dd2f285734789422d79bb2974

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Citations in the corpus

Large-scale evidence of dependency length minimization in 37 languages (2015, background): “Arabic Semitic HamleDT Prague Arabic Dependency Treebank (43) Bulgarian IE/Slavic HamleDT BulTreeBank (44) Bengali IE/Indo-Aryan HamleDT Hyderabad Dependency Treebank (45) Catalan IE/Romance HamleDT AnCora-CA (46) Chinese Sino-Tibetan — Chinese Dependency Treebank[40] Croatian IE/Slavic HamleDT+ Cro”
IXA pipeline: Efficient and Ready to Use Multilingual NLP tools (2014, methodology): “Training models for different Named Entity types, e.g., using MUC-7 and Ontonotes 4.0 datasets (Weischedel et al., 2010) for English and Ancora corpus for Spanish (Taulé et al., 2008).”
SemEval-2010 Task 13: TempEval-2 (2010, background): “Its documents are originally from the Spanish part of the AnCora corpus (Taul´e et al., 2008).”
Efforts on Machine Learning over Human-mediated Translation Edit Rate (2014, methodology): “Klein, 2007) trained over an English and a Spanish treebank (Taulé et al., 2008).”
Asiya: An Open Toolkit for Automatic Machine Translation (Meta-)Evaluation (2010, methodology): “As for texts in Catalan and Spanish, we used the Ancora corpus (Taulé et al., 2008) to train the SVMTool and the 3LB corpus(35) to train the BIOS processor.”
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages (2020, methodology): “For NER, we train and evaluate Sta n z a with 12 publicly available datasets covering 8 major languages as shown in Table 3 (Nothman et al., 2013; Tjong Kim Sang and De Meulder, 2003; Tjong Kim Sang, 2002; Benikova et al., 2014; Mohit et al., 2012; Taulé et al., 2008; Weischedel et al., 2013).”
The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages (2009, methodology): “3 are described in (Taulé et al., 2008), (Xue and Palmer, 2009), (Hajič et al.”
A Latent Variable Model of Synchronous Syntactic-Semantic Parsing for Multiple Languages (2009, methodology): “The synchronous derivations described above are modelled with a type of Bayesian Network called an Incremental Sigmoid Belief Network (ISBN) (Titov and Henderson, 2007a).”
SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations (2013, methodology): “Furthermore, we can include the full-featured TIPSem (Llorens et al., 2010), which unlike TIPSemB-F used the AnCora (Taulé et al., 2008) corpus annotations as features including semantic roles.”
Multilingual Semantic Role Labeling (2009, methodology): “After correcting this, we could reach a labeled semantic F1 of 79.91 in Spanish.”
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages (2020, background): “• Catalan-AnCora : Built on top of the Spanish-Catalan AnCora corpus (Taulé et al., 2008), it contains mainly news articles.”