Details
| Original language | English |
|---|---|
| Title of host publication | CLEF 2019 Working Notes |
| Subtitle of host publication | Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum |
| Publication status | Published - 2019 |
| Externally published | Yes |
| Event | 20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019 - Lugano, Switzerland Duration: 9 Sept 2019 → 12 Sept 2019 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR Workshop Proceedings |
| Volume | 2380 |
| ISSN (Print) | 1613-0073 |
Abstract
In this work we describe our results achieved in the ProtestNews Lab at CLEF 2019. To tackle the problems of event sentence detection and event extraction we decided to use contextualized string embeddings. The models were trained on a data corpus collected from Indian news sources, but evaluated on data obtained from news sources from other countries as well, such as China. Our models have obtained competitive results and have scored 3rd in the event sentence detection task and 1st in the event extraction task based on average F1-scores for different test datasets.
Keywords
- Classification, Contextualized String Embeddings, Named Entity Recognition
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
Cite this
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CLEF 2019 Working Notes: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum. 2019. (CEUR Workshop Proceedings; Vol. 2380).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - CLEF ProtestNews Lab 2019
T2 - 20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019
AU - Skitalinskaya, Gabriella
AU - Klaff, Jonas
AU - Spliethöver, Maximilian
N1 - Publisher Copyright: © 2019 CEUR-WS. All rights reserved.
PY - 2019
Y1 - 2019
N2 - In this work we describe our results achieved in the ProtestNews Lab at CLEF 2019. To tackle the problems of event sentence detection and event extraction we decided to use contextualized string embeddings. The models were trained on a data corpus collected from Indian news sources, but evaluated on data obtained from news sources from other countries as well, such as China. Our models have obtained competitive results and have scored 3rd in the event sentence detection task and 1st in the event extraction task based on average F1-scores for different test datasets.
AB - In this work we describe our results achieved in the ProtestNews Lab at CLEF 2019. To tackle the problems of event sentence detection and event extraction we decided to use contextualized string embeddings. The models were trained on a data corpus collected from Indian news sources, but evaluated on data obtained from news sources from other countries as well, such as China. Our models have obtained competitive results and have scored 3rd in the event sentence detection task and 1st in the event extraction task based on average F1-scores for different test datasets.
KW - Classification
KW - Contextualized String Embeddings
KW - Named Entity Recognition
UR - http://www.scopus.com/inward/record.url?scp=85070519146&partnerID=8YFLogxK
M3 - Conference contribution
T3 - CEUR Workshop Proceedings
BT - CLEF 2019 Working Notes
Y2 - 9 September 2019 through 12 September 2019
ER -