DMCA
Ananiadou S. Identification of manner in bio-events
| Venue: | Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012), Istanbul,Turkey, ELRA |
| Citations: | 3 - 2 self |
BibTeX
@INPROCEEDINGS{Nawaz_ananiadous.,
author = {Raheel Nawaz and Paul Thompson and Sophia Ananiadou},
title = {Ananiadou S. Identification of manner in bio-events},
booktitle = {Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012), Istanbul,Turkey, ELRA},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Due to the rapid growth in the volume of biomedical literature, there is an increasing requirement for high-performance semantic search systems, which allow biologists to perform precise searches for events of interest. Such systems are usually trained on corpora of documents that contain manually annotated events. Until recently, these corpora, and hence the event extraction systems trained on them, focussed almost exclusively on the identification and classification of event arguments, without taking into account how the textual context of the events could affect their interpretation. Previously, we designed an annotation scheme to enrich events with several aspects (or dimensions) of interpretation, which we term meta-knowledge, and applied this scheme to the entire GENIA corpus. In this paper, we report on our experiments to automate the assignment of one of these meta-knowledge dimensions, i.e. Manner, to recognised events. Manner is concerned with the rate, strength intensity or level of the event. We distinguish three different values of manner, i.e., High, Low and Neutral. To our knowledge, our work represents the first attempt to classify the manner of events. Using a combination of lexical, syntactic and semantic features, our system achieves an overall accuracy of 99.4%.
Keyphrases
first attempt biomedical literature event argument overall accuracy term meta-knowledge recognised event several aspect event extraction system different value meta-knowledge dimension strength intensity annotated event annotation scheme rapid growth textual context precise search entire genia corpus semantic feature high-performance semantic search system
