A framework for biological event extraction from text

Amami, Maha; Faiz, Rim; Elkhlifi, Aymen

doi:10.1145/2254129.2254193

Cited by 1 publication

(4 citation statements)

References 17 publications

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“…We can observe the existence of three conceptual nodes, [organ, sore] to the left, and [organ,limbe] and [form,penné] to the right. The latter two are linked by a relation node [organ, [1],organ] that, as previously mentioned, we do not visualize in the interest of understanding. To obtain this answer, a chain of transformations has been applied in order to build a projection from the conceptual representation of the query in Fig.…”

Section: The System At Workmentioning

confidence: 99%

“…This means some limitations for transparent, precise and easy retrieval by inexpert users across heterogeneous data sources, as well as for flexible and rapid adaptation to changes in the domain 37 . To solve these without incurring an information overload, we need to enable strategies for both knowledge mining (km) and discovery (kd) on unstructured documents expressed in natural language 1 .…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the title has been modified. A definitive version was subsequently published in International Journal on Artificial Intelligence Tools, 25 (1):1550029,…”

mentioning

confidence: 99%

“…The same applies to the relation nodes linking [ε,*] with [organ,sore] and [form,penné], since in both cases they can be specialized in [organ,[2] avec,organ] and [organ,[1],form], respectively.…”

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confidence: 99%

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Intelligent Retrieval for Biodiversity

Ferro

Fernández-Gavilanes

González

et al. 2016

Int. J. Artif. Intell. Tools

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A proposal for intelligent retrieval in the biodiversity domain is described. It applies natural language processing to integrate linguistic and domain knowledge in a mathematical model for information management, formalizing the notion of semantic similarity in different degrees. The goal is to provide computational tools to identify, extract and relate not only data but also scientific notions, even if the information available to start the process is not complete. The use of conceptual graphs as a basis for interpretation makes it possible to avoid the use of classic ontologies, whose start-up requires costly generation and maintenance protocols and also unnecessarily overload the accessing task for inexpert users. We exploit the automatic generation of these structures from raw texts through graphical and natural language interaction, at the same time providing a solid logical and linguistic foundation to sustain the curation of databases.

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Section: The System At Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In particular, the title has been modified. A definitive version was subsequently published in International Journal on Artificial Intelligence Tools, 25 (1):1550029,…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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