Abstract
This paper describes the implementation and evaluation of a generic component to extract temporal information from texts in Swedish. It proceeds in two steps. The first step extracts time expressions and events, and generates a feature vector for each element it identifies. Using the vectors, the second step determines the
temporal relations, possibly none, between the extracted events and orders them in time.
We used a machine learning approach to find the relations between events. To run the learning algorithm, we collected a corpus of road accident reports from newspapers websites that we manually annotated. It enabled us to train decision trees and to evaluate the performance of the algorithm.
temporal relations, possibly none, between the extracted events and orders them in time.
We used a machine learning approach to find the relations between events. To run the learning algorithm, we collected a corpus of road accident reports from newspapers websites that we manually annotated. It enabled us to train decision trees and to evaluate the performance of the algorithm.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of LREC-2006, The fifth international conference on Language Resources and Evaluation |
| Pages | 259-264 |
| Number of pages | 6 |
| Publication status | Published - 2006 |
| Event | LREC-2006, The fifth international conference on Language Resources and Evaluation - Genoa Duration: 2006 May 24 → 2006 May 26 |
Conference
| Conference | LREC-2006, The fifth international conference on Language Resources and Evaluation |
|---|---|
| Period | 2006/05/24 → 2006/05/26 |
Subject classification (UKÄ)
- Computer Science
Free keywords
- information extraction
- semantic analysis
- temporal relations
- Natural language processing