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Abstract Relation Extraction from Arabic text is a difficult and challenging task. Pattern-based methods often employ precise and accurate linguistics rules; however, they need huge amount of manual works to annotate corpora with desired tags. On the other hand, supervised methods need large corpus with semantic tags; which in turn imposes extra load for preparing annotated data. In this paper, a cross-language method for relation extraction is proposed, which uses the training data of other languages and trains a model for relation extraction from Arabic text. The task is supervised learning, in which several lexical and syntactic features are considered. The proposed method mainly relies on the Universal Dependency (UD) parsing and the similarity of UD trees in different languages. Regarding UD parse trees, all the features for training classifiers are extracted and represented in a universal space. To incorporate different features in training the classifier, a combination of kernel functions is proposed. Result of experiments on ACE-2004 data set reveals 63.5% F1 for Arabic test data. [ABSTRACT FROM AUTHOR]

Background: Single Nucleotide Polymorphisms (SNPs) are among the most important types of genetic variations influencing common diseases and phenotypes. Recently, some corpora and methods have been developed with the purpose of extracting mutations and diseases from texts. However, there is no available corpus, for extracting associations from texts, that is annotated with linguistic-based negation, modality markers, neutral candidates, and confidence level of associations. Method: In this research, different steps were presented so as to produce the SNPPhenA corpus. They include automatic Named Entity Recognition (NER) followed by the manual annotation of SNP and phenotype names, annotation of the SNP-phenotype associations and their level of confidence, as well as modality markers. Moreover, the produced corpus was annotated with negation scopes and cues as well as neutral candidates that play crucial role as far as negation and the modality phenomenon in relation to extraction tasks. Result: The agreement between annotators was measured by Cohen's Kappa coefficient where the resulting scores indicated the reliability of the corpus. The Kappa score was 0.79 for annotating the associations and 0.80 for the confidence degree of associations. Further presented were the basic statistics of the annotated features of the corpus in addition to the results of our first experiments related to the extraction of ranked SNP-Phenotype associations. The prepared guideline documents render the corpus more convenient and facile to use. The corpus, guidelines and inter-annotator agreement analysis are available on the website of the corpus: http://nil.fdi.ucm.es/?q=node/639. Conclusion: Specifying the confidence degree of SNP-phenotype associations from articles helps identify the strength of associations that could in turn assist genomics scientists in determining phenotypic plasticity and the importance of environmental factors. What is more, our first experiments with the corpus show that linguistic-based confidence alongside other non-linguistic features can be utilized in order to estimate the strength of the observed SNP-phenotype associations. Trial Registration: Not Applicable. [ABSTRACT FROM AUTHOR]

This paper focuses on the task of cross-language relation extraction, which aims to identify the semantic relations holding between entities in the text. The goal of the task is to train classifiers for low-resource languages by means of the annotated data from high-resource languages. Related methods usually employ parallel data or Machine Translator (MT) to project annotated data from a source to a target language. However, the availability and the quality of parallel data and MT are big challenges for low-resource languages. In this paper, a novel transfer learning method is presented for this task. The key idea is to utilize a tree-based representation of data, which is highly informative for classifying semantic relations, and also shared among different languages. All the training and test data are shown using this representation. We propose to use the Universal Dependency (UD) parsing, which is a language-agnostic formalism for representation of syntactic structures. Equipping UD parse trees with multi-lingual word embeddings makes an ideal representation for the cross-language relation extraction task. We propose two deep networks to use this representation. The first one utilizes the Shortest Dependency Path of UD trees, while the second employs the UD-based positional embeddings. Experiments are performed using SemEval 2010-task 8 training data, whereas French and Farsi are the test languages. The results show 63.9% and 56.2% F 1 scores, for French and Farsi test data, respectively, which are 14.4% and 17.9% higher than the baseline. This work can be considered a simple yet powerful baseline for further investigation into the cross-language tasks. • A new method for cross-language relation extraction, which is suitable for low-resource languages. • Introduction of a new baseline for cross-language relation extraction. • Utilizing Universal Dependency parse formalism, for making a machine learning model to solve relation extraction problem. • This research opens up avenues for further research on cross-language problems. [ABSTRACT FROM AUTHOR]