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Special Report on

Supervised Word Sense Disambiguation

supervised word sense disambiguation special research report Photo by acl.ldc.upenn.edu
This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose Accurate concept identification is crucial to biomedical natural language processing. However, ambiguity is common during the process of mapping terms to biomedical concepts (one term can be mapped to several concepts). A cost-effective approach to disambiguation relating to training is via semantic classification of the ambiguous terms, provided that the semantic classes of the concepts are available and are all different. We propose such a semantic classification based method to disambiguate ambiguous ...
on a variety of word types and ambiguities. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date. Current accuracy is difficult to state without a host of caveats. On English, accuracy at the coarse-grained ( homograph
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Word sense disambiguation - Wikipedia, the free encyclopedia #sandbox
on a variety of word types and ambiguities. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date. Current accuracy is difficult to state without a host of caveats. On English, accuracy at the coarse-grained ( homograph market research, surveys and trends
Word sense disambiguation
Eneko Agirre and Philip Edmonds have done an admirable job of providing a comprehensive look at the natural language processing task of word sense disambiguation (WSD). Rather than gathering papers on individuals’ recent research, the editors have commissioned the top names in the field to present overviews of major issues, methods, and research directions. The first several chapters establish the context for word sense disambiguation: why it is seen as necessary for natural language processing (NLP) applications, what the parameters of the task are, and how to evaluate a system’s performance. Adam Kilgarriff (29–46) explores ... market research, surveys and trends

SURVEY RESULTS FOR
SUPERVISED WORD SENSE DISAMBIGUATION

Quantitative Assessment of Dictionary-based Protein Named Entity ...
Results: The current version of BioThesaurus has over 2.6 million names or 2.1 million ..... We computed the percent- age of identifiers for each model organism that were ..... of supervised word sense disambiguation. J Am Med Inform ... industry trends, business articles and survey research
Methods in Biomedical Text mining - Raul Rodriguez-Esteban
The purpose of this introduction is to give a historical overview and background to the project of automatic curation of text-mined data described in Chapter 2 . This introduction describes the beginnings of text mining as a discipline and its arrival to the biomedical domain. It also describes the first interaction text mining projects, which precede and set a path to the development of GeneWays. This introduction is necessary to understand the architectural and structural choices made for the design of GeneWays. Finally, this introduction reviews the different efforts made in evaluating text-mined interaction data before ... industry trends, business articles and survey research
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Supervised Word Sense Disambiguation with Support Vector Machines ...
Supervised Word Sense Disambiguation with. Support Vector Machines and Multiple Knowledge Sources. Yoong Keok Lee and Hwee Tou Ng and Tee Kiah Chia ... technology research, surveys study and trend statistics
A Multi-aspect Comparison Study of Supervised Word Sense ...
word sense disambiguation (WSD; supervised machine learning for disambiguating the sense of a term in ..... LIU ET AL., Supervised Word Sense Disambiguation ... technology research, surveys study and trend statistics
Ted Pedersen - Supervised Word Sense Disambiguation Publications
(McInnes) - PhD Dissertation, Department of Computer Science and Engineering, University of Minnesota, Twin Cities, September, 2009. Learning High Precision Rules to Make Predictions of Morbidities in Discharge Summaries (Pedersen), Appears in the Proceedings of the Second i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, Nov 7-8, 2008, Washington, DC. Using UMLS Concept Unique Identifiers (CUIs) for Word Sense Disambiguation in the Biomedical Domain (McInnes, Pedersen, and Carlis) - Appears in the Proceedings of the Annual Symposium of the American Medical Informatics Association, Nov 10-14, 2007, ...
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SUPERVISED WORD SENSE DISAMBIGUATION
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