We present two terminology extraction tools to compare a knowledge-poor and a knowledge-rich approach. Both tools process SWT and MWT and are designed to handle multilingualism. We run an evaluation on 6 languages and 2 different domains using crawled comparable corpora and hand-crafted reference term lists (RTL). We discuss the 3 main results achieved for terminology extraction. The first two evaluation scenarios concern the knowledge-rich framework. Scenario 1 (S1) compares performances for each of the languages depending on the ranking that is applied: specificity score vs. the number of occurrences. Scenario 2 (S2) examines the relevancy of the term variant identification to increase the precision ranking for any of the languages. Scenario 3 (S3) compares both tools and demonstrates that a probabilistic term extraction approach, developed with minimal effort, achieves satisfactory results when compared to a rule-based method. conference: cicling 2013 - samos

1. Cicling
Knowledge-poor and Knowledge-rich Approaches 2013
for Multilingual Terminology Extraction
Béatrice Daille (1), Helena Blancafort(2)
(1)University of Nantes – Lina, (2)Syllabs
Abstract
We present two terminology extraction tools to compare a knowledge-poor and a knowledge-rich
approach. Both tools process SWT and MWT and are designed to handle multilingualism. We run an evaluation
on 6 languages and 2 different domains using crawled comparable corpora and hand-crafted reference term
lists (RTL). We discuss the 3 main results achieved for terminology extraction. The first two evaluation scenarios
concern the knowledge-rich framework. Scenario 1 (S1) compares performances for each of the languages
depending on the ranking that is applied: specificity score vs. the number of occurrences. Scenario 2 (S2)
examines the relevancy of the term variant identification to increase the precision ranking for any of the
languages. Scenario 3 (S3) compares both tools and demonstrates that a probabilistic term extraction approach,
developed with minimal effort, achieves satisfactory results when compared to a rule-based method.
Knowledge-Rich Framework (KR) Knowledge-Poor Approach (KP)
1. Linguistic processing: tokenization, POS tagging, 1. Training of a Pseudo POS Tagger (Clark, 2003) with
lemmatization (TreeTagger) raw corpora (2,5 GB to 250 MB)
2. Rule-based candidate term (CT) extraction based 2. CRFs (Lafferty et al. 2001) to train a Term Candidate
on POS tags Extractor (Sha et al., Guégan and Loupy, 2011),
3. Hand-crafted rules for the grouping of variants manually small annotated corpora with CT (300 to
4. Multilingual framework : 6 languages 600 sentences)
Shared Features
Extraction of SWT and MWT, ranking based on specificity score
Ressources Term Variation
• Hand-crafted RTL from 103 to 159 terms with variants wind energy
• Monolingual Crawled Comparable Corpora from 220K to 474K wind turbine energy, onshore wind energy
tokens energy from wind, small-scale wind energy
Evaluation (Wind Energy Domain)
S1: F-Measure based on specificity S1: F-Measure based on occurrences S2: F-Measure based on specificity
ranking of CT with and without variants
Conclusions
 S1: Specificity ranking outperforms the frequency of occurrence
ranking
 S2: The handling of term variants improves the ranking for the first
candidate terms
 S3: The knowledge-poor approach provides satisfactory results with
minimal effort. Results are language and domain dependent.
 EN better results than DE  limits of multilingual framework
 ES better results in mobile domain than wind energy
 KR tool and RTL handle MWT of 2-3, KP longer terms as small
scale domestic wind turbine system
S3: F-Measure based on specificity  Future Work
Knowledge-rich vs. knowledge-poor
- Evaluate method using a POS tagger but no hand-crafted rules
The research leading to these results has received funding from the European Community's FP7/2007-2013 under
grant agreement nº 248005 for the TTC project (Terminology Extraction, Translation Tools and Comparable Corpora)