This book provides relevant theoretical frameworks and the latest empirical research findings in biomedicine information retrieval as it pertains to linguistic granularity.
Front Cover.
Title Page.
Copyright Page.
Table of Contents.
Detailed Table of Contents.
Preface.
1: Text Mining for Biomedicine.
2: Works at a Lexical Level: Crossroads Between NLP and Ontological Knowledge Management.
3: Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish Mesh®.
4: Expanding Terms with Medical Ontologies to Improve a Multi-Label Text Categorization System.
5: Using Biomedical Terminological Resources for Information Retrieval.
6: Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval.
7: Translation of Biomedical Terms by Inferring Rewriting Rules.
8: Lexical Enrichment of Biomedical Ontologies.
9: Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach.
10: Going Beyond Words: NLP Approaches Involving the Sentence Level.
11: Information Extraction of Protein Phosphorylation from Biomedical Literature.
12: CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence.
13: Analyzing the Text of Clinical Literature for Question Answering.
14: Pragmatics, Discourse Structures and Segment Level as the Last Stage in the NLP Offer to Biomedicine.
15: Discourse Processing for Text Mining.
16: A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems.
17: Extracting Patient Case Profiles with Domain-Specific Semantic Categories.
18: NLP Software for IR in Biomedicine.
19: Identification of Sequence Variants of Genes From Biomedical Literature: The OSIRIS Approach.
20: Verification of Uncurated Protein Annotations.
21: A Software Tool for Biomedical Information Extraction (And Beyond).
22: Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering.
23: Seekbio: Retrieval of Spatial Relations for System Biology.
