Abstract
We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities.- Anthology ID:
- W19-0804
- Volume:
- RELATIONS - Workshop on meaning relations between phrases and sentences
- Month:
- May
- Year:
- 2019
- Address:
- Gothenburg, Sweden
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/W19-0804
- DOI:
- 10.18653/v1/W19-0804
- Cite (ACL):
- Mark-Christoph Mueller. 2019. Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications. In RELATIONS - Workshop on meaning relations between phrases and sentences, Gothenburg, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications (Mueller, IWCS 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/W19-0804.pdf