Collaboratively built semi-structured content and Artificial Intelligence: The story so far
TLDR
The overall picture shows that not only are semi-structured resources enabling a renaissance of knowledge-rich AI techniques, but also that significant advances in high-end applications that require deep understanding capabilities can be achieved by synergistically exploiting large amounts of machine-readable structured knowledge in combination with sound statistical AI and NLP techniques.About:
This article is published in Artificial Intelligence.The article was published on 2013-01-01 and is currently open access. It has received 176 citations till now. The article focuses on the topics: Applications of artificial intelligence & Knowledge acquisition.read more
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YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
TL;DR: YAGO2 as mentioned in this paper is an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space, and it contains 447 million facts about 9.8 million entities.
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Entity Linking meets Word Sense Disambiguation: A Unified Approach
TL;DR: Babelfy is presented, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations.
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Computational generation of referring expressions: A survey
Emiel Krahmer,Kees van Deemter +1 more
TL;DR: The REG problem is introduced and early work in this area is described, discussing what basic assumptions lie behind it, and showing how its remit has widened in recent years.
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SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking
Andrea Moro,Roberto Navigli +1 more
TL;DR: The aim with this task is to analyze whether, and if so, how, using a resource that integrates both kinds of inventories might enable WSD and EL to be solved by means of similar (even, the same) methods.
Proceedings Article
SemEval-2013 Task 12: Multilingual Word Sense Disambiguation
TL;DR: The experience in producing a multilingual sense-annotated corpus for the SemEval-2013 task on multilingual Word Sense Disambiguation is described, and the results of participating systems are presented and analyzed.
References
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