Discriminative Phrase Embedding for Paraphrase Identification
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Abstract (arXiv)
This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN to learn the discriminative weights of words and phrases specific to paraphrase task, so that a weighted sum of embeddings can represent sentences more effectively. Based on these two innovations we get competitive state-of-the-art performance on paraphrase identification.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2016.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 3.
4 papers.
- 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methodscited by 13
- 2024Topic Modeling as Multi-Objective Contrastive Optimizationcited by 12
- 2024Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Losscited by 7
- 2024A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacycited by 2
- 2024A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy30 Jun 2024
- 2024Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss21 Jun 2024
- 2024Topic Modeling as Multi-Objective Contrastive Optimization12 Feb 2024
- 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methods13 Dec 2022