PPT-APE: An Annotation Language and Middleware for Energy-Effic

Nima Nikzad Octav Chipara and William G Griswold nnikzadcsucsdedu Dept of Computer Science and Engineering UC San Diego Dept of Computer Science University of Iowa . Thesis Proposal. Tao Huang. taohuang@cs.indiana.edu. Outline. Introduction. Motivation. Related System Survey. Research Issues. Milestones. Contributions. Introduction. Annotation Definitions. A commentary on an object that: (Cousins et al. 2000). (rad)f Massimo Poesio. University of . Essex. Part 1: Intro, . Microtask. . crowdsourcing. ANNOTATED CORPORA: . AN ERA OF PLENTY?. With the release of the . OntoNotes. and ANC corpora for English and a number of corpora for other languages (Prague Dependency Treebank especially) we may think we won’t need to do any more annotation for a while . A Close Reading Strategy for Better Comprehension. Text Annotation by Teachers. What is Text Annotation?. While reading, students mark the pages for . Important information. Text meaning or key details. Triplets to Sentence Fingerprints. Motivation. . Scientific concepts are annotated with controlled vocabulary (CV) terms from ontologies such as Gene Ontology (GO) and Plant Ontology (PO). . Our Arabidopsis specific tool - . RuSSIR Young Scientist Conference,. 24-28| 2015| St. Petersburg, Russia. Arshad Khan. School of Electronics & Computer Science,. University of Southampton, UK. Overview. Searching in online repositories of multidisciplinary research data is becoming a challenge due to the volume and types of data being published every year. A Close Reading Strategy for Better Comprehension. Text Annotation by Teachers. What is Text Annotation?. While reading, students mark the pages for . Important information. Text meaning or key details. ConceptRank. Petra Budíková, Michal Batko, Pavel Zezula. Outline. Search-based annotation. Motivation. Problem formalization. Challenges. ConceptRank. Idea. Semantic network construction. PageRank and ConceptRank. Nima Nikzad. , . Octav. . Chipara. †. , . and William G. Griswold. nnikzad@cs.ucsd.edu. Dept. of Computer Science and Engineering, UC San Diego. Dept. of Computer Science, University . of . Iowa. †. Nima Nikzad. , . Octav. . Chipara. †. , . and William G. Griswold. nnikzad@cs.ucsd.edu. Dept. of Computer Science and Engineering, UC San Diego. Dept. of Computer Science, University . of . Iowa. †. Lecture 3. Gene Finding and Sequence Annotation. Objectives of this lecture. Introduce you to basic concepts and approaches of gene finding. Show you differences between gene prediction for prokaryotic and eukaryotic genomes. Alberto Di Meglio (CERN). Project Director. Outline. What is EMI?. EMI Vision and Objectives. How does it work?. Conclusions. 06/10/2010. EMI Overview v. 3.0. 2. Outline. What is EMI?. EMI Vision and Objectives. Tool. (. TART). Outline. Pragmatics & Corpora. Speech/Dialogue-Act Annotation. The DART Approach. From DART to . TART. Observations & Issues. Intended Features. Potential Applications. Pragmatics & Corpora. However, genomes are large and complex and visualizing this information is not easy. You need to imagine that the raw annotation information, by itself, can be contained in a text file, which could include positional information (coordinates relative to the reference genome) and different features (e.g. annotation type – gene, mRNA, intron/exon, variant, UTR, etc.).