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LexDivPara: A Measure of Paraphrase Quality with Integrated Sentential Lexical Complexity
We present a novel method that automatically measures quality of sentential paraphrasing. Our method balances two conflicting criteria: semantic similarity and lexical diversity. Using a diverse annotated corpus, we built learning to rank models on edit distance, BLEU, ROUGE, and cosine similarity features. Extrinsic evaluation on STS Benchmark and ParaBank Evaluation datasets resulted in a model ensemble with moderate to high quality. We applied our method on both small benchmarking and large-scale datasets as resources for the community.
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- PAR ID:
- 10293412
- Date Published:
- Journal Name:
- International Workshop on Intelligent Systems and Applications
- ISSN:
- 2159-1539
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different species. Existing methods of PPI network alignment often try to improve the coverage ratio of the alignment result by aligning all proteins from different species. However, there is a fundamental biological premise that needs to be considered carefully: not every protein in a species can, nor should, find its homologous proteins in other species. In this work, we propose a novel alignment method to map only those proteins with the most similarity throughout the PPI networks of multiple species. For the similarity features of the protein in the networks, we integrate both topological features with biological characteristics to provide enhanced supports for the alignment procedures. For topological features, we apply a representation learning method on the networks that can generate a low dimensional vector embedding with its surrounding structural features for each protein. The topological similarity of proteins from different PPI networks can thus be transferred as the similarity of their corresponding vector representations, which provides a new way to comprehensively quantify the topological similarities between proteins. We also propose a new measure for the topological evaluation of the alignment results which better uncover the structural quality of the alignment across multiple networks. Both biological and topological evaluations on the alignment results of real datasets demonstrate our approach is promising and preferable against previous multiple alignment methodsmore » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-82199-9_1
