Abstract
We perform a comparative study for automatic term extraction from domain-specific language using a PageRank model with different edge-weighting methods. We vary vector space representations within the PageRank graph algorithm, and we go beyond standard co-occurrence and investigate the influence of measures of association strength and first- vs. second-order co-occurrence. In addition, we incorporate meaning shifts from general to domain-specific language as personalized vectors, in order to distinguish between termhood strengths of ambiguous words across word senses. Our study is performed for two domain-specific English corpora: ACL and do-it-yourself (DIY); and a domain-specific German corpus: cooking. The models are assessed by applying average precision and the roc score as evaluation metrices.- Anthology ID:
- 2020.lrec-1.540
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4388–4394
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.540
- DOI:
- Cite (ACL):
- Anurag Nigam, Anna Hätty, and Sabine Schulte im Walde. 2020. Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4388–4394, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction (Nigam et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.540.pdf