A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific litera...

Extracting bio-entity relations has emerged as an important task due to the ever-growing number of bio-medical documents. In this paper, we present a simple and novel representation for extracting bio-entity relationships. The state-of-theart systems for such tasks rely on word based representations and variations of linguistic driven features. In contrast, we model bio-text by the most basic c...

Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques aim to blindly group the data by similarity. Among the different areas, manifold identification i...

Abstract In French, the noun apple ( la pomme ) is grammatically feminine, in German der Apfel it masculine. Does this entail that French speakers perceive apples to be feminine whereas attribute masculine characteristics them? Various studies suggest grammatical gender does indeed influence object perception Haertlé 2017 ; Boroditsky & Schmidt 2000 ), although findings are not always repli...

Named entity recognition (NER) of chemicals and drugs is a critical domain information extraction in biochemical research. NER provides support for text mining reactions, including relation extraction, attribute metabolic response relationship extraction. However, the existence complex naming characteristics biomedical field, such as polysemy special characters, make task very challenging. Here...