SASL: A semantic annotation system for literature
|SASL: A semantic annotation system for literature|
|Author(s)||Yuan P., Wang G., Zhang Q., Jin H.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Name disambiguation, Semantic annotation (Extra: Academic institutions, Knowledge base, Name disambiguation, Regular expressions, Scientific literature, Search results, Semantic annotations, Semantic information, Wikipedia, Hidden Markov models, Knowledge based systems, Search engines, Terminology, World Wide Web, Semantics)|
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SASL: A semantic annotation system for literature is a 2009 conference paper written in English by Yuan P., Wang G., Zhang Q., Jin H. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Due to ambiguity, search engines for scientific literatures may not return right search results. One efficient solution to the problems is to automatically annotate literatures and attach the semantic information to them. Generally, semantic annotation requires identifying entities before attaching semantic information to them. However, due to abbreviation and other reasons, it is very difficult to identify entities correctly. The paper presents a Semantic Annotation System for Literature (SASL), which utilizes Wikipedia as knowledge base to annotate literatures. SASL mainly attaches semantic to terminology, academic institutions, conferences, and journals etc. Many of them are usually abbreviations, which induces ambiguity. Here, SASL uses regular expressions to extract the mapping between full name of entities and their abbreviation. Since full names of several entities may map to a single abbreviation, SASL introduces Hidden Markov Model to implement name disambiguation. Finally, the paper presents the experimental results, which confirm SASL a good performance.
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