Name-It: Naming and Detecting Faces in News Videos
IEEE MultiMedia
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Automatic rule induction for unknown-word guessing
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Generic technologies for single- and multi-document summarization
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Using patterns of thematic progression for building a table of contents of a text
Natural Language Engineering
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Measuring aboutness of an entity in a text
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
A dataset for the evaluation of lexical simplification
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
| Hi-index | 0.00 |
We present a novel approach to automatically annotate images solely using associated text. We detect and classify all entities (persons and objects) in the text after which we determine the salience (the importance of an entity in a text) and visualness (the extent to which an entity can be perceived visually) of these entities. We combine these measures to compute the probability that an entity is present in the image. The suitability of our approach was successfully tested on 900 image-text pairs of Yahoo! News.