Abstract
In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 161-171 |
| Number of pages | 11 |
| Volume | 5883 LNAI |
| DOIs | |
| Publication status | Published - 1 Dec 2009 |
| Externally published | Yes |
| Event | 11th International Conference of the Italian Association for Artificial Intelligence: Emergent Perspectives in Artificial Intelligence, AI IA 2009 - Reggio Emilia, Italy Duration: 9 Dec 2009 → 12 Dec 2009 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5883 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 11th International Conference of the Italian Association for Artificial Intelligence: Emergent Perspectives in Artificial Intelligence, AI IA 2009 |
|---|---|
| Country | Italy |
| City | Reggio Emilia |
| Period | 9/12/09 → 12/12/09 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Kernel-based learning for domain-specific relation extraction. / Basili, Roberto; Giannone, Cristina; Del Vescovo, Chiara; Moschitti, Alessandro; Naggar, Paolo.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5883 LNAI 2009. p. 161-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5883 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Kernel-based learning for domain-specific relation extraction
AU - Basili, Roberto
AU - Giannone, Cristina
AU - Del Vescovo, Chiara
AU - Moschitti, Alessandro
AU - Naggar, Paolo
PY - 2009/12/1
Y1 - 2009/12/1
N2 - In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements.
AB - In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements.
UR - http://www.scopus.com/inward/record.url?scp=78650683537&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650683537&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10291-2_17
DO - 10.1007/978-3-642-10291-2_17
M3 - Conference contribution
AN - SCOPUS:78650683537
SN - 3642102905
SN - 9783642102905
VL - 5883 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 171
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -