A Scalable and Distributed NLP Architecture for Web Document Annotation

A Scalable and Distributed NLP Architecture for Web Document Annotation

In the context of the ALVIS project, which aims at integrating linguistic information in topic-specific search engines, we develop a NLP architecture to linguistically annotate large collections of web documents. This context leads us to face the scalability aspect of Natural Language Processing. Th...

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Hauptverfasser: Deriviere, Julien, Hamon, Thierry, Nazarenko, Adeline
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Entity Recognition
Exact sciences and technology
Information systems. Data bases
Linguistic Annotation
Memory organisation. Data processing
Natural Language Processing Tool
Software
Speech and sound recognition and synthesis. Linguistics
Standard Personal Computer
Word Segmentation
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Beschreibung
Zusammenfassung:In the context of the ALVIS project, which aims at integrating linguistic information in topic-specific search engines, we develop a NLP architecture to linguistically annotate large collections of web documents. This context leads us to face the scalability aspect of Natural Language Processing. The platform can be viewed as a framework using existing NLP tools. We focus on the efficiency of the platform by distributing linguistic processing on several machines. We carry out an an experiment on 55,329 web documents focusing on biology. These 79 million-word collections of web documents have been processed in 3 days on 16 computers.
ISSN:0302-9743
1611-3349
DOI:10.1007/11816508_8