Abstract
In this paper, we present an analysis of feature extraction methods via dimensionality reduction for the task of biomedical Word Sense Disambiguation (WSD). We modify the vector representations in the 2-MRD WSD algorithm, and evaluate four dimensionality reduction methods: Word Embeddings using Continuous Bag of Words and Skip Gram, Singular Value Decomposition (SVD), and Principal Component Analysis (PCA). We also evaluate the effects of vector size on the performance of each of these methods. Results are evaluated on five standard evaluation datasets (Abbrev.100, Abbrev.200, Abbrev.300, NLM-WSD, and MSH-WSD). We find that vector sizes of 100 are sufficient for all techniques except SVD, for which a vector size of 1500 is referred. We also show that SVD performs on par with Word Embeddings for all but one dataset.- Anthology ID:
- W17-2334
- Volume:
- BioNLP 2017
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada,
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 272–281
- Language:
- URL:
- https://aclanthology.org/W17-2334
- DOI:
- 10.18653/v1/W17-2334
- Cite (ACL):
- Sam Henry, Clint Cuffy, and Bridget McInnes. 2017. Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation. In BioNLP 2017, pages 272–281, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation (Henry et al., BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-2334.pdf