Massih-Reza Amini
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Massih-Reza Amini has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Anastasios Tombros
University of London
1 shared publication - Hugo Zaragoza
LIP6, University of Paris 6, case 169, 4 Place Jussieu, F-75252 Paris cedex 05, France
1 shared publication - Mounia Lalmas
University of London
1 shared publication - Nicolas Usunier
University Pierre and Marie Curie
1 shared publication - Patrick Gallinari
LIP6, University of Paris 6, case 169, 4 Place Jussieu, F-75252 Paris cedex 05, France
1 shared publication
Latest Publications
- 3 November 2006
- Published by Springer Nature in Information Retrieval Journal
- Vol. 10 (3) , 233-255
Learning-based summarisation of XML documents
- 3 November 2006
- Published by Springer Nature in Information Retrieval Journal
- Vol. 10 (3) , 233-255
Learning-based summarisation of XML documents
- Open Access
- 1 January 1999
- Published by BCS Learning & Development Ltd in Electronic Workshops in Computing
Sequence Models for Automatic Highlighting and Surface Information Extraction
With the increase of textual information available electronically, we assist to a great diversification of the demands on Information Retrieval (IR) and Information Extraction (IE) systems. In this paper we apply Machine Learning techniques of sequence analysis to the tasks of highlighting and labeling text with respect to an information extraction task. Specifically, dynamic probability models are used. Like IR systems, they use little semantics, are fully trainable and do not require any knowledge representation of the domain. Unlike IR approaches, documents are considered as a dynamic sequence of words. Furthermore, additional word information is naturally included in the representation. Models are evaluated on a sub-task of the MUC6 Scenario Template corpus. When morpho-syntactic word information is introduced into the representation, an increase in performances is observed.
- 1 January 1999
- Published by BCS Learning & Development Ltd in Electronic Workshops in Computing
Open AccessSequence Models for Automatic Highlighting and Surface Information Extraction
With the increase of textual information available electronically, we assist to a great diversification of the demands on Information Retrieval (IR) and Information Extraction (IE) systems. In this paper we apply Machine Learning techniques of sequence analysis to the tasks of highlighting and labeling text with respect to an information extraction task. Specifically, dynamic probability models are used. Like IR systems, they use little semantics, are fully trainable and do not require any knowledge representation of the domain. Unlike IR approaches, documents are considered as a dynamic sequence of words. Furthermore, additional word information is naturally included in the representation. Models are evaluated on a sub-task of the MUC6 Scenario Template corpus. When morpho-syntactic word information is introduced into the representation, an increase in performances is observed.